<commit_before>id,name,title,abstract,img,github,twitter,slides_url,talk_url
agupta,<NAME>,The Lab Notebook: Bringing Science Back to Data Science,"All of us deal with data. A lot of us do data science. And yet only some of us get a chance to really infuse science into that data science work. Ever visit one of your old experiments and find that you want to pull out your hair because you are not sure how you arrived at some of the models that you ended up selecting, why you transformed your data the way you did, and other choices that now seem arbitrary but were perhaps perfectly reasonable then? While we invent a time machine that allows us to go back and inspect our previous (more brilliant?) selves, i have a simpler proposal: the humble lab notebook. Remember those ruled notebooks we carried around in physical labs at school diligently writing things down as we figured out how to build the best soda volcanoes? Turns out they can help us solve this problem of tracking our decisions as we arrive at different configurations that we run in our data science work so that we don’t need to curse at our past selves for making poor choices. Not only do they act as great supplements for existing AI lifecycle management tools, but they also help us share our learnings better with our colleagues (and future selves!). Come join me on this journey and let’s explore how the lab notebook can bring back science into data science. We’ll look at why you should have a lab notebook for all your data science work, how you should go about maintaining that lab notebook, and what you should and what you should NOT include in that lab notebook.",agupta,atg-abhishek,atg_abhishek,,
wlyon,<NAME>,Will You Be My Graph Data-bae? A Graph Data Love Story,"This talk tells the story of two technologies that were always meant to be together but came from different worlds, yet finally were able to come together and help the world make sense of data through APIs: GraphQL and graph databases. We'll discuss graph databases like Neo4j - what they are, why and how to use them, as well as the tradeoffs of building applications with graph databases. We'll review GraphQL and how to build GraphQL APIs. Then we'll see how GraphQL and Neo4j are even more powerful when used together, each leveraging features of the other (the property graph data model, a strict type system, index-free adjacency, extensibility through schema directives, and the Cypher query language) - resulting in a truly symbiotic and mutually beneficial relationship.",wlyon,johnymontana,lyonwj,,
jjanssens,<NAME>,Set your code free; turn it into a command-line tool,"If your data analyses involve coding, then you know how liberating it is to use and create functions. They hide complexity, improve testability, and enable reusability. In this talk I explain how you can really set your code free: by turning it into a command-line tool. The command line can be a very flexible and efficient environment for working with data. It's specialized in combining tools that are written in all sorts of languages (including Python and R), running them in parallel, and applying them to massive amounts of (streaming) data. Although the command line itself has quite a learning curve, turning your existing code into a tool is, as I demonstrate, a matter of a few steps. I discuss how your new tool can be combined with existing tools in order to obtain, scrub, explore, and model data at the command line. Finally, I share some best practices regarding interface design and distribution.",jjanssens,jeroenjanssens,jeroenhjanssens,,
bvezina,<NAME>,Should open content be used to train artificial intelligence?,"Developments in artificial intelligence (AI) raise several questions when it comes to the use of copyright material and Creative Commons-licensed content in particular. One of them is whether CC-licensed content (photographs, artworks, text, music, etc.) should be used as input to train AI. This question illustrates the tension between the value of open data vs. legitimate concerns about ethical, moral and responsible use of openly licensed content, which this session will explore.",bvezina,,Brigitte_Vezina,,
jborghi,<NAME>,"Identifying the who, what, and (sometimes) where of research data sharing at an academic institution","In many areas of academic research, there is a growing emphasis on sharing data and other materials. However, tracking which researchers affiliated with a given institution are sharing, what they are sharing, and what tools they are using to share remains extremely challenging. To help inform the support services offered to affiliated researchers, I attempted to track the research data shared by Stanford Medicine in 2020. In this talk, I will describe the methods I used, the (incomplete but hopefully representative) information I was able to collect, and how this information will be used moving forward. Spoiler alert, I sorted through thousands of journal articles and data availability statements and discovered data in some unexpected places.",jborghi,,JohnBorghi,,
vwarmerdam,<NAME>,Human-Learn: Let's Draw Machine Learning Models and Train Humans Instead,"Back in the old days, it was common to write rule-based systems. <br><br>Systems that do; data -> rules -> labels. <br><br> Nowadays, it's much more fashionable to use machine learning instead. <br><br>Something like; (data, labels) -> ML -> rules. <br><br>I started wondering if we might have lost something in this transition. So I made an open source package in an attempt to keep the hype at bay. Maybe natural intelligence is just enough and maybe enough is plenty. In this talk I'll present a toolbox called ""human-learn"" that allows you to, literally, draw out a machine learning model. I'll demonstrate the benefits of the approach. Not only do we allows the machine to learn from a domain expert, we also force the algorithm designer to actually look at the data.",vwarmerdam,koaning,fishnets88,,
kshaw,<NAME>,From opaque to open: untangling apparel supply chains with open data,"Historically, the apparel sector has been shrouded in secrecy and mystique, using this to generate an impression of exclusivity and glamour. As the tragic Rana Plaza building collapse in Dhaka, Bangladesh in 2013 showed (in which well over 1,000 workers died manufacturing clothing for many major western brands) the reality is anything but. The aftermath of the Rana Plaza disaster shone a light on how few brands were aware of where their products were being manufactured, let alone the conditions in which they were being made. In response to this, a growing trend has developed for supply chain disclosure in the apparel sector, spurred on by groups such as the Transparency Pledge coalition and Fashion Revolution. However, in response to these calls for greater transparency, supply chain disclosure has been inconsistent, difficult to track from one website to another and data is often locked away in non-machine readable formats such as PDFs or tables embedded in websites. A lack of standard formatting for information as basic as name and address data (coupled with the poor quality of this data) makes it difficult and costly for anyone to compare across datasets and understand shared connections to facilities. Data has been stuck in silos and lacked a universal, central ID through which systems could synchronize, making interoperability between systems impossible. Enter the Open Apparel Registry (OAR). The OAR was launched in March 2019 to address this data challenge. At its heart, the OAR exists to drive improvements in data quality for the benefit of all stakeholders in the apparel sector. As well as many other efficiency and process benefits, the way the OAR organizes and presents data ultimately improves the lives of some of the most vulnerable workers in global supply chains. During our talk, we’ll share details about the challenges facing the apparel sector, including low levels of technical sophistication and understanding of open data; collaborative work that’s being done to educate the sector on the power of open data, including the launch of the Open Data Standard for the Apparel Sector (ODSAS); before finishing up with inspiring examples of how data from the Open Apparel Registry being freely shared and used is creating meaningful changes in the lives of some of global society’s most oppressed people.",kshaw,open-apparel-registry,openapparelreg,,
psoriano,<NAME>,csv-detective: solving some of the misteries of open data,"Over the last few years, the emphasis on data quality has evolved from being a nice to have to an absolute necessity. As already stated in past editions of this venue, the quality of open data is essential to *truly fulfill its promise as a channel of empowerment*. In practical terms, data quality for tabular data entails, among other tasks, checking the structural integrity and schematic consistency of its contents. In order to satisfy these requirements, we need to look into the files content and first determine whether our files are indeed CSV files, and secondly, and more importantly, we want to discover the type of data we have in order to properly validate it and thus evaluate and then hopefully improve the quality of our datasets. In this talk, we present our work on the automatic detection of data types within the columns of CSV files. Going beyond classic computer science data types (float, integer, date), we are also interested in detecting more specific in-domain data categories. Specifically, given a CSV file, we look into its columns and determine whether it contains postal codes, enterprise identifiers, geographic coordinates, days of the week, and so on. We will show how applied to data from the French open data platform that we maintain (data.gouv.fr.), these specific types allow users to better control data in order to join or link datasets between them. We see our work as a first step and important stepping stone towards data integrity and validation checks while also facilitating the discoverability and contextualization of open datasets. Our approaches are evaluated by annotating and testing over thousands of columns extracted from more than 15 000 CSV files found in data.gouv.fr.",psoriano,etalab,Etalab,,
dwinston,<NAME>,CSV-LD: Spreadsheet-based Linked Data,"Comma-separated-values (CSV) is a useful data serialization and sharing format. This talk introduces the idea of CSV-LD as a CSV-based format to serialize Linked Data, mirroring the way that JSON-LD is a JSON-based format to serialize Linked Data. ""CSV"" here includes any dialect that uses a different delimiter, such as tab-separated-values (TSV). The syntax of CSV-LD is designed to easily integrate into workflows that already use CSV, and provides a smooth upgrade path from CSV to CSV-LD. It is primarily intended to be a way to use Linked Data as part of spreadsheet-based data entry; to facilitate data validation, display, and conversion of CSV into other formats via use of CSV on the Web (CSVW) metadata; and to build FAIR data services. The term ""CSV-LD"" was previously used to describe a now-obsoleted precursor to the CSVW specifications; both approaches require a second file, a JSON-LD template document, to be shared along with a CSV file. The approach described here, in contrast, requires only a CSV file from the data producer, one that includes links to CSVW-powered metadata.",dwinston,dwinston,,,
dbeaton,<NAME>,"The Ontario Neurodegenerative Disease Research Initiative's Neuroinformatics & Biostatistics teams pipelines, partnerships, and products.","The Ontario Neurodegenerative Disease Research Initiative (ONDRI) is a large-scale longitudinal project across five disorders spanning 520 participants, each with 3 or more annual assessments, and from 14 sites across the province. ONDRI’s data span lab measures, genetics, ocular imaging, eye-tracking tasks, many cognitive assessments, various brain imaging modalities, and an extensive array of clinical assessments. These data come in many types, shapes, and sizes. Different domain and data experts have different expectations of data standards, how data are curated, and how data are analyzed. However, ONDRI is more than data: it is also 100s of researchers and clinicians coming together to better understand neurodegenerative and cerebrovascular disorders. So, ONDRI’s Neuroinformatics & Biostatistics (NIBS) team built both an infrastructure (data) and a culture (people) around the standardization and integrity of such diverse data. We did this with (1) pipelines: supporting curation from preparation through to analyses and reporting, (2) partnerships: working closely with others on training & collaboration, and (3) products: the tools we build and use; many of whichare publicly available: https://github.com/ondri-nibs. ONDRI’s data standards and outlier analyses are central to the pipelines. Our standards revolve around “data packages” that include data and dictionaries with specific nomenclature and features (e.g., predefined and unambiguous missing data codes). We built the standards around tabular (.csv format) data, then extended standards to “non-tabular” or file-based data, such as neuroimaging. We also perform a battery of (mostly) multivariate outlier analyses. We share standardized reports (written in RMarkdown) for standards and outliers with research and clinical experts to review. These steps identify anomalies and allow us to correct errors. We were able to build a data-focused culture based on shared clinical & research goals, recognizing the difficulty with & importance of data curation, and with an eye toward public release of the data.",dbeaton,derekbeaton,derek__beaton,,
kbreseman,<NAME>,Environmental Enforcement Watch: Environmental Data Justice through Participatory Data Science,"The U.S. EPA maintains an open dataset of information about permits issued under the Clean Air, Clean Water, and Resource Conservation and Recovery Acts, among others. This dataset describes, for instance, how much pollution an industrial facility is permitted to discharge into waterways and how many times they have been fined by the EPA—important information for environmental justice. But though there is a lot of information about permits and pollution, it is not organized in such a way as to answer questions about health or aggregate impacts—the kinds of questions people living in the area might find most meaningful. In the Environmental Enforcement Watch (EEW) project, we work with communities potentially impacted by facilities’ (non)compliance with their permits to co-develop open source Jupyter Notebooks. We use these to explore questions partners have about their environment and their communities’ health. In this presentation, we’ll explain our approach, show Notebooks we’ve developed, and share stories and outcomes from this attempt to model a more participatory approach to environmental governance.",kbreseman,edgi-govdata-archiving,EEW_Network,,
atravitz,<NAME>,"Scaling computations and maintaining reproducible projects with signac, an open-source framework","Data science projects are often messy, with complex workflows and large, heterogeneous parameter spaces. Bash scripting and long file names can only get you so far, and can result in a project that is incomprehensible to collaborators or your future self. The open-source data management framework, signac, (https://signac.io) makes it easy to maintain organized and reproducible projects, and integrates with existing file-based workflows. The serverless data management and lightweight workflow model ensure that projects are easily transferable between laptops and high-performance computing environments, and the data model is well suited for high-dimensional parameter searches or hyperparameter optimization of machine learning models. In this talk, we will demonstrate the signac approach to managing data science projects, emphasizing its use in real-world scenarios. The signac framework not only increases research efficiency, but makes best practices and scalability easy and intuitive.",atravitz,atravitz,,,
alsteele,<NAME>,Data visualization and crowdsourced research: experiments in collective storytelling,"These days, everyone seems to trying to harness the power of the crowd. Amnesty International’s “Decoders” brings together “digital volunteers to tackle human rights abuses around the world”. Bellingcat crowdsources information for their online investigations, and has transformed their newsroom into an open source project. Amazon’s Mechanical Turk has long altered how research is conducted through their crowdsourcing marketplace. These tools have paved the way for new kinds of journalism and research, where theoretically anyone can contribute to projects from anywhere around the world. However, while crowdsourcing has grown as a source of data, visual storytelling has usually been left out in the process. Online tools like Datawrapper and Tableau have made data visualization easier than ever, but because storytelling with data is computationally expensive – and usually custom-made for the story – the crowdsourcing process has usually been completed before it is turned into a visual design. They don’t usually walk in lockstep with each other. This talk will attempt to bridge this gap, if only just a little. It reviews a project called supply-chains.us, which is building a sustainable open-source database that anyone can contribute to, with an integrated visualization that updates over time. This talk will describe how the project was built, why visual storytelling is important for research, and how open-source projects can be an opportunity for collective storytelling.",alsteele,aleesteele,aleesteele,,
kawhite,<NAME>,"WordPress as Data, 5 Years In","In 2016,WordPress introduced a REST API to expose site content as JSON. Also in 2016, we had the opportunity to present at csv,conf about this paradigm shift: what would it mean for all the content in any WordPress website to be accessible as data? Since then, WordPress has fundamentally reinvented itself using that new API. The project has been reimagined from the inside out, with the humble content block as the foundational unit of site data. But, where was that explosion of data-driven WordPress applications we hoped for? In this talk we will look at what worked as we integrated this new API into WordPress, and at where we stumbled. We'll explore how roles, access, and authentication limit the utility of data. We'll candidly discuss how burnout affects project stewardship. And we'll also celebrate a tremendous, successful change of course for a venerable open source project, and think ahead to what ""WordPress as Data"" may mean in five more years' time!",kawhite,kadamwhite,kadamwhite,,
hsenyondo,<NAME>,"Rdataretriever:  A platform for downloading, cleaning, and installing publicly available datasets","The rdataretriever provides an R interface to the Python-based Data Retriever software. The Data Retriever automates the core steps of data preprocessing including downloading, cleaning, standardizing, and importing datasets into a variety of relational databases and flat file formats. The rdataretriever additionally supports provenance tracking for these steps of the analysis workflow by taking snapshots of the datasets to be committed at the time of installation and allowing them to be reinstalled with the same data and processing steps in the future. Finally, the rdataretriever supports the installation of spatial datasets into relational databases with spatial support. The rdataretriever provides an R interface to this functionality and also supports importing of datasets directly into R for immediate analysis. These tools are focused on scientific data applications including several widely used but difficult to work with, datasets in ecology and the environmental sciences. The rdataretriever allows R users to access the Python Data Retriever processing platform through a combination of the reticulate package and custom features developed for working in R. Because many R users, including the domain researchers most strongly supported by this package, are not familiar with Python and its package management systems, a strong emphasis has been placed on simplifying the installation process for this package so that it can be done entirely from R. Installation requires no direct use of Python or the command line. Detailed documentation has been developed to support users in both installation and use of the software. A Docker-based testing system and associated test suite have also been implemented to ensure that the interoperability of the R package and Python package are maintained, which is challenging due to frequent changes in reticulate and complexities in supporting cross-language functionality across multiple operating systems and R programming environments (terminal-based R and RStudio).",hsenyondo,henrykironde,henrykironde,,
mtrizna,<NAME>,Making Smithsonian Open Access Accessible with Python and Dask,"In February 2020, the Smithsonian Institution released almost 3 million images and over 12 million collections metadata records under the Creative Commons Zero (CC0) license. The release was made available via a web API, a GitHub repository, and via the Registry of Open Data on Amazon Web Services (AWS). The format of the release on the GitHub and AWS sources made the data well-suited for parallelized analysis, but only with deep knowledge of the complex data structures. In this talk we will discuss how we used the Python Dask library to unlock this parallelization and make the data more accessible, as well as a student intern project that used Python tools to uncover insights specifically into the holdings of the National Museum of American History.",mtrizna,MikeTrizna,MikeTrizna,,
gvdishington,<NAME>,Using GitHub Actions to Accelerate your Data Efforts,"Data science work often requires computing resources that isn’t available to practitioners from disadvantaged backgrounds or located in “data deserts” with low technology access. Cloud computing is an option in such situations but can be costly and not friendly to beginners who don’t know what infrastructure to pick. Most of the data science community thinks of GitHub as only a code storage repository with some management features, but GitHub Actions provide a powerful and often free resource for computing that can perform many data collection and analysis needs. This introduction to GitHub Actions will provide an overview of everything needed for GitHub users to get started applying GitHub Actions to their projects. We’ll see both simple and advanced examples of the YAML format that controls various workflows and how it ties into traditional data science needs, like testing and automation. Time permitting, we’ll also see how these workflows can be used for special applications, such as open source intelligence and machine learning.",gvdishington,GrantRVD,usethespacebar,,
cstihler,<NAME>,"Better sharing, brighter future","2021 marks the 20th anniversary for Creative Commons. For 20 years we have helped build a commons of 'open' creative content free of most copyright restrictions. With CC licenses and tools, we created a simple way for creators to opt into a more permissive model of sharing. As we reflect on the past and think of the future, we know that today we must pursue a commons of knowledge and culture that is inclusive, just and which inspires reciprocity - a commons that serves the public interest. To that end we must transition from promoting more sharing to fostering better sharing. In this talk, I want to share what better sharing means in practice and how we can achieve this together.",cstihler,,C_Stihler,,
rharter,<NAME>,Getting Credit for Invisible Work,"A lot of the hard important work that goes into good data work is invisible. For example, most of an exploratory data analysis is testing *and discarding* hypotheses. We explore complex data, so we can distill our findings into a simple narrative. We never talk about all the hypotheses we’ve discarded!If we’re doing it right, we make our work look simple. This is super valuable, but can cause problems when we try to demonstrate our value. This talk covers some strategies for getting credit for this super valuable but invisible work.",rharter,harterrt,harterrt,,
bgtzovaras,<NAME>,Using wearables to detect infections: A co-created & community-led pandemic response,"In March 2020 – during the early days of the COVID-19 pandemic – a big question popped up in the heads of the data nerds of the Open Humans & Quantified Self community: We are wearing all these sophisticated wearable devices that measure our heart rate, respiratory rate and even sometimes even body temperature - can these things be used to find out when we’re falling sick even before we consciously notice having symptoms? Starting from this initial question a community quickly formed to iteratively and collaboratively create “Quantified Flu” – an open source online-tool to facilitate individual learning and insights at the intersection of early physiological warning signs and self-observed symptoms. The final prototype enables people to report over 15 different symptoms through daily reporting while monitoring physiological signals from a large variety of the most common wearable devices. We’ll not only showcase the final prototype, but also investigate how the open and iterative development process unfolded and how this helped to create a tool that fits the needs of the self-tracking community. We furthermore see how this fit of digital affordances & needs enabled a level of user engagement that’s highly untypical for the “mobile health” space.",bgtzovaras,gedankenstuecke,gedankenstuecke,,
hlapiroff,<NAME>,An API for Tracking Press Freedom,"In 2017, as the Trump administration was ramping up a war of open hostility against the press, a collaborative of press freedom organizations launch a database and website to track aggressions against journalists. The U.S. Press Freedom Tracker records incidents include arrests, border stops, physical assaults, subpoenas, equipment damage, and more. In addition to detailed articles, all four years (and counting) of the Tracker data is made available through an API that can be used to track trends, aggregate incidents related to specific events, and more. We'll show off examples of how the API is already used to power interactive data visualizations and give an overview of how anyone can access and use the data.",hlapiroff,harrislapiroff,harrislapiroff,,
bherman,<NAME>,Static datasets aren't enough: where deployed systems differ from research,"The focus on static datasets in machine learning and AI training fails to translate to how these systems are being deployed in industry. As a result, data scientists and engineers aren't considering how these systems perform in changing, real world environments nor the feedback mechanisms and societal implications that these systems can cause. In the session, we will highlight existing tools that work with dynamic (and perhaps streaming) data. We will suggest some preliminary studies of activities and lessons that may bridge the gap in data science training for realistic data.The goal of the talk is to:<br> - Point to resources for AI practitioners to engage with dynamic datasets<br> - Engage in discussion about the impact of feedback loops and other consequences on the real world<br> - Brainstorm new approaches to teaching skills on dynamic datasets",bherman,bernease,bernease,,
jmiller,<NAME>,Making Police Call Data Readable using Pandas and Eland,Many cities in the United States have public data as a part of the oversight initiatives. These datasets often make the data incredibly hard to analyze. This talk breaks down how I compiled data from the San Diego Police Department into dataframes and make it observable in Elasticsearch Kibana.,jmiller,kjaymiller,kjaymiller,,
dmcarthy,<NAME>,From tweet to sheet: crowdsourcing a global survey of Open GLAM,"Three years ago a simple tweet between friends caught fire, inspiring Douglas and Andrea to start surveying open access in the GLAM (Gallery, Library, Archive, Museum) sector. Since that moment, the tiny acorn of a Google Sheet has matured into a oak tree, growing in scale, complexity and prominence. In this talk, Douglas and Andrea will share the remarkable story of the Open GLAM survey's development and impact. They'll share actionable insight into crowdsourcing and managing data with a global community of researchers, activists, copyright experts and GLAM professionals. Finally, Douglas and Andrea will discuss how they've leveraged the Open GLAM survey into Wikidata and other domains. Twitter: https://twitter.com/CultureDoug + https://twitter.com/AndeeWallace",dmcarthy,,,,
kmontgomery,<NAME>,Save your sanity with Synapse,"Synapse is a freely available data repository to organize your research, get credit for it and collaborate with colleagues and the public. Data exploration provokes a curiosity that can lead the analyst through a labyrinth of possible outcomes. Synapse contains features to track file versions with an immutable identifier, string together file transformations, manage the visibility of sensitive data, make data discoverable with structured metadata, describe experimental context with unstructured copy and connect your output with code. All of this can be accessed from the web or programmatically with R, Python or the Command Line, thus integrating seamlessly into your scientific analysis. Allow Synapse to hold the memory of the places you have been to instead focus your energy on testing creative hypotheses.",kmontgomery,kelshmo,,,
jaclark,<NAME>,Algorithms as Data: A Case Study for Turning Algorithmic User Experiences into Research Data Objects,"Why does your technology seem to know what you want before you do? Increasingly, our digital experiences are mediated by obscure algorithms. But what are algorithms and how can we audit or quantify them? This session introduces an ""algorithmic awareness"" research module and a generic code application for auditing algorithmic user experiences and quantifying those experiences as datasets for analysis. What if algorithms weren’t the Ghost in the Machine? Can algorithms be understood as part of the open data continuum? Montana State University Library, with grant funding from the Institute of Museum and Library Services, has conducted research in order to develop software and a curriculum to support the teaching of ""Algorithmic Awareness"": an understanding around the rules that govern our software and shape our digital experiences. Taking our inspiration from investigative data journalists, like The Markup, we are looking to introduce our research module for algorithm auditing practices using code, web scraping methods, and structured data formats to uncover proprietary algorithms and turn them into research data objects for analysis. (Code is available in our #AlgorithmicAwareness GitHub repository.) Our case study for the module will be the YouTube Video Recommendation Algorithm which has come under criticism for its tactics in drawing parents’ and childrens’ attention to their videos. Our goal will be to show the generic patterns, data points, and scripts one can use to analyze algorithmic user experiences and demonstrate how code can be used to turn algorithms into datasets for analysis. In the end, attendees will be able to realize actionable steps for seeing algorithms as data objects, gain a sense of the first steps one can take to programmatically audit these systems with code, and take away investigative data techniques for their own work.",jaclark,jasonclark,jaclark,,
styrrell,<NAME>,Frictionless Data for Wheat,"The international wheat community has embraced the omics era and is producing larger and more heterogeneous datasets at a rapid pace in order to produce better varieties via breeding programmes. These programmes, especially in the pre-breeding space, have encouraged wheat communities to make these datasets available more openly. This is a positive step, but the consistent and standardised collection and dissemination of data based on rich metadata remains difficult, as so much of this information is stored in papers and supplementary information. Furthermore, whilst ontologies exist for data descriptions, e.g. the Environmental Factor Ontology, the Crop Ontology, etc., use of these ontology terms to annotate key development characteristics across disparate data generation methods and growing sites is rarely routine or harmonised. Therefore, we built Grassroots, an infrastructure including portals to deliver large scale datasets with semantically marked-up metadata to power FAIR data in crop research. As part of the UK Designing Future Wheat (DFW) programme, we generate a variety of data ranging from field trial experimental information, sequencing data and phenotyping images, through to molecular biology data about host and pathogen interactions, nitrogen use efficiency, and other key treatment factors. As such, there is an increasing need to be able to manage this data and its metadata to allow for consistent, easy dissemination and integration with other datasets and within analytical tools and workflows. We decided that Frictionless Data was the right framework to use due to its ease of use and open standards, so we developed open source tools for FAIR data sharing to automatically generate Frictionless Data Packages for these datasets on both our Apache/iRODS and CKAN portals.",styrrell,billyfish,,,
gsantarsieri-psoriano,"<NAME>, <NAME>",(Machine) Learning from Open Data platforms,"Today, open data platforms host a wide and heterogeneous catalog of datasets. However, these datasets are often neglected in Machine Learning (ML) and other related tasks. This mainly happens because there are few available open data catalogs specialized in ML applications and because it is often unclear whether Machine Learning algorithms would be adequate and well performing on such datasets. Therefore, several open datasets go unused while they could be leveraged by the ML community to explain, evaluate, and challenge existing methods on real open data.
For instance, these real-world data could be used by professors teaching ML courses, by students taking these courses, by researchers testing current and novel ML approaches, and possibly to promote the intersection of open data, ML and public policy. In this talk we will show you how we are tackling this issue working on datasets from data.gouv.fr (DGF),  the French open data government platform. We aim to answer the question of what makes a dataset suitable and well performing for Machine Learning tasks by leveraging open source tools. Our goal is to establish a first small empirical assessment of the characteristics of a dataset (size, balance of its categorical variables and so on) that make it a “good fit” for Machine Learning algorithms. Specifically,  we first manually select an adequate subset of datasets from DGF. Then we perform a statistic profiling on each of these datasets. Thirdly, we automatically train and validate a set of ML algorithms on them and we cluster the datasets according to their evaluation results. These steps help us to better understand the nature of each dataset and thus determine which ones seem suitable for ML applications. Based on these datasets, and inspired by existing resources, we build the first version of a catalog of open datasets for ML. We hope that this platform will be a first stepping stone towards the reuse of open datasets in  Machine Learning contexts.",gsantarsieri,,,,
scanelon-ehare,"<NAME>, <NAME>",Revealing Room for Improvement in Accessibility within a Social Media Data Visualization Learning Community,"We all aim to use data to tell a compelling story, and many of us enjoy sharing how we got there by open-sourcing our code, but we don't always share our story with everyone. Even kind, supportive, and open communities like the #TidyTuesday R learning community on Twitter has a ways to go before the content shared can be accessible to everyone. Lived experiences of blind R users tell us that most data visualizations shared for TidyTuesday are inaccessible to screen reading technology because they lack alternative text (i.e. alt text) descriptions. Our goal was to bring this hidden lack of accessibility to the surface by examining the alternative text accompanying data visualizations shared as part of the TidyTuesday social project.  We scraped the alternative text from 6,443 TidyTuesday images posted on Twitter between April 2, 2018 and January 31, 2021. The first image attached to each tweet was considered the primary image and was scraped for alternative text. Manual web inspection revealed the CSS class and HTML element corresponding to the primary image, as well as the attribute containing the alternative text. We used this information and the ROpenSci {RSelenium} package to scrape the alternative text. Our preliminary analysis found that only 2.4% of the images contained a text description entered by the tweet author compared to 84% which were described by default as ""Image"". This small group of intentional alternative text descriptions had a median word count of 18 (range: 1-170), and a median character count of 83 (range: 8-788). As a reference point, Twitter allows 240 characters in a single tweet and 1,000 characters for image descriptions. This analysis was made possible thanks to a dataset of historical TidyTuesday tweet data collected using the ROpenSci {rtweet} package, and openly available in the TidyTuesday GitHub repository (https://github.com/rfordatascience/tidytuesday). Twitter: @spcanelon; @DogGeneticsLLC GithUB: @spcanelon; @LizHareDogs",scanelon,,,,
ksill,<NAME>,The Water Point Data Exchange - using Open Data for Universal Water Access,"Water points are still the main - or sometimes only - source of water for millions of people living in rural areas of the world. The Water Point Data Exchange (WPDx) unlocks the potential of water point data to improve the information available to governments, service providers, researchers, and NGOs, and create data-based decision-making for increasing rural water access. Governments and their partners are already collecting substantial amounts of water point data. However, data is often too fragmented to use, with different approaches for data collection and storage. Without harmonization among these different data sources, the true potential of this information remains untapped so it is only used for reporting and not decision support. By establishing a platform for sharing water point data throughout the global water sector, WPDx adds value to the data already being collected. It brings together diverse data sets and establishes an unprecedented understanding of water services. All stakeholders can easily use the WPDx Data Standard to harmonize their existing data structure. Formatting existing data into the WPDx Data Standard typically takes less than 30 minutes. The WPDx Data Standard fits most water point data, even if it was collected with no knowledge of the standard. After formatting, files can be uploaded and published using the WPDx ingestion engine. It removes duplicate entries, links multiple updates to the same water point, and integrates the data into the Global Data Repository. Using this data and advanced GIS and machine learning analysis, several decision support tools were built. Designed in partnership with governments and data scientists, these tools provide concrete insights, like which water point to send a technician to rehabilitate next to reach the most people. In this presentation we will share some of the challenges we face and the methods we use in making this possible.",ksill,,,,
zselvans,<NAME>,Distributing Power with Open Data,"Some lessons learned from our work getting electricity system data into the hands of activists and researchers who are trying to shift power in US energy policy making. Under what circumstances can data help drive policy changes, and how does that change happen? Is open data always preferable in an advocacy context? What does it really mean for data to be accessible to advocates? US energy regulation is extremely technocratic, and largely happens out of public view. For years we’ve tried to address the information asymmetry that exists between advocates and large utilities by publishing cleaned open datasets detailing the inner workings of the US electricity system. We use Python data science tools to prepare the data and they serve us well, but most advocates -- even those with a lot of domain expertise -- are either hardcore spreadsheet users, or are just starting to dabble in Jupyter Notebooks. Now that we have a decent data pipeline, we are turning our attention to improving the data’s accessibility for our target users, using Datasette, Docker containers that run Jupyter, and Intake data catalogs.",zselvans,zaneselvans,ZaneSelvans,,
kbroman,<NAME>,Data cleaning principles,"Why don't we teach data cleaning? It has been said that it is difficult to generalize: that what we learn from cleaning Medicare data cannot be readily applied to the cleaning of RNA-seq data. To the contrary, I think there are important general principles for cleaning data, and there are more commonalities in the creative process of data cleaning than in other aspects of data analysis. I will seek to delineate and illustrate these principles, which include: <br>(1) think about what might have gone wrong and how it might be revealed<br>(2) study the pattern of missing data and ask why they are missing<br>(3) use care when merging datasets and focus on labels not position<br>(4) if things are supposed to match, check that they do<br>(5) if things are supposed to be distinct, check that they are<br>(6) look for outliers and other oddities by making lots of plots, particularly scatterplots<br>(7) look for batch effects by making lots of plots, particularly plots against time and scatterplots colored by batch<br>(8) ask for the primary data and also metadata<br>(9) don’t be shy about asking questions<br>(10) document not just what you did but also why you did it<br>(11) don’t trust anyone (even yourself)<br>(12) allocate sufficient time and energy to the effort",kbroman,kbroman,kwbroman,,
pashlock,<NAME>,"The State of Open Government Data Standards","This talk will provide a follow-up to the CSVConf v5 talk titled “The State of Open Government Data Infrastructure” but this time with a focus more specifically on the work to provide more institutional capacity and documentation around data standards and data interoperability from the perspective of the US Federal Government. The talk will provide an update on the work to establish a catalog and repository of resources around data standards as called for by the US Federal Data Strategy and it will highlight a few examples of data standards collaboration across agencies, across sectors, across levels of government, and across international borders. Beyond the discussion of the DCAT metadata standards from last year this talk may cover a variety of different case studies including data standards developed to support state by state standardized election information look-up and election results for US voters, efforts to standardize information about government benefits, services, and eligibility criteria to improve access to government services by those who need them most, and the Work Zone Data Exchange project to standardize information about roadway construction and other disruptions for transportation route planning",pashlock,philipashlock,philipashlock,slides_url,talk_url<commit_msg>no images yet for co-presenters<commit_after>id,name,title,abstract,img,github,twitter,slides_url,talk_url
agupta,<NAME>,The Lab Notebook: Bringing Science Back to Data Science,"All of us deal with data. A lot of us do data science. And yet only some of us get a chance to really infuse science into that data science work. Ever visit one of your old experiments and find that you want to pull out your hair because you are not sure how you arrived at some of the models that you ended up selecting, why you transformed your data the way you did, and other choices that now seem arbitrary but were perhaps perfectly reasonable then? While we invent a time machine that allows us to go back and inspect our previous (more brilliant?) selves, i have a simpler proposal: the humble lab notebook. Remember those ruled notebooks we carried around in physical labs at school diligently writing things down as we figured out how to build the best soda volcanoes? Turns out they can help us solve this problem of tracking our decisions as we arrive at different configurations that we run in our data science work so that we don’t need to curse at our past selves for making poor choices. Not only do they act as great supplements for existing AI lifecycle management tools, but they also help us share our learnings better with our colleagues (and future selves!). Come join me on this journey and let’s explore how the lab notebook can bring back science into data science. We’ll look at why you should have a lab notebook for all your data science work, how you should go about maintaining that lab notebook, and what you should and what you should NOT include in that lab notebook.",agupta,atg-abhishek,atg_abhishek,,
wlyon,<NAME>,Will You Be My Graph Data-bae? A Graph Data Love Story,"This talk tells the story of two technologies that were always meant to be together but came from different worlds, yet finally were able to come together and help the world make sense of data through APIs: GraphQL and graph databases. We'll discuss graph databases like Neo4j - what they are, why and how to use them, as well as the tradeoffs of building applications with graph databases. We'll review GraphQL and how to build GraphQL APIs. Then we'll see how GraphQL and Neo4j are even more powerful when used together, each leveraging features of the other (the property graph data model, a strict type system, index-free adjacency, extensibility through schema directives, and the Cypher query language) - resulting in a truly symbiotic and mutually beneficial relationship.",wlyon,johnymontana,lyonwj,,
jjanssens,<NAME>,Set your code free; turn it into a command-line tool,"If your data analyses involve coding, then you know how liberating it is to use and create functions. They hide complexity, improve testability, and enable reusability. In this talk I explain how you can really set your code free: by turning it into a command-line tool. The command line can be a very flexible and efficient environment for working with data. It's specialized in combining tools that are written in all sorts of languages (including Python and R), running them in parallel, and applying them to massive amounts of (streaming) data. Although the command line itself has quite a learning curve, turning your existing code into a tool is, as I demonstrate, a matter of a few steps. I discuss how your new tool can be combined with existing tools in order to obtain, scrub, explore, and model data at the command line. Finally, I share some best practices regarding interface design and distribution.",jjanssens,jeroenjanssens,jeroenhjanssens,,
bvezina,<NAME>,Should open content be used to train artificial intelligence?,"Developments in artificial intelligence (AI) raise several questions when it comes to the use of copyright material and Creative Commons-licensed content in particular. One of them is whether CC-licensed content (photographs, artworks, text, music, etc.) should be used as input to train AI. This question illustrates the tension between the value of open data vs. legitimate concerns about ethical, moral and responsible use of openly licensed content, which this session will explore.",bvezina,,Brigitte_Vezina,,
jborghi,<NAME>,"Identifying the who, what, and (sometimes) where of research data sharing at an academic institution","In many areas of academic research, there is a growing emphasis on sharing data and other materials. However, tracking which researchers affiliated with a given institution are sharing, what they are sharing, and what tools they are using to share remains extremely challenging. To help inform the support services offered to affiliated researchers, I attempted to track the research data shared by Stanford Medicine in 2020. In this talk, I will describe the methods I used, the (incomplete but hopefully representative) information I was able to collect, and how this information will be used moving forward. Spoiler alert, I sorted through thousands of journal articles and data availability statements and discovered data in some unexpected places.",jborghi,,JohnBorghi,,
vwarmerdam,<NAME>,Human-Learn: Let's Draw Machine Learning Models and Train Humans Instead,"Back in the old days, it was common to write rule-based systems. <br><br>Systems that do; data -> rules -> labels. <br><br> Nowadays, it's much more fashionable to use machine learning instead. <br><br>Something like; (data, labels) -> ML -> rules. <br><br>I started wondering if we might have lost something in this transition. So I made an open source package in an attempt to keep the hype at bay. Maybe natural intelligence is just enough and maybe enough is plenty. In this talk I'll present a toolbox called ""human-learn"" that allows you to, literally, draw out a machine learning model. I'll demonstrate the benefits of the approach. Not only do we allows the machine to learn from a domain expert, we also force the algorithm designer to actually look at the data.",vwarmerdam,koaning,fishnets88,,
kshaw,<NAME>,From opaque to open: untangling apparel supply chains with open data,"Historically, the apparel sector has been shrouded in secrecy and mystique, using this to generate an impression of exclusivity and glamour. As the tragic Rana Plaza building collapse in Dhaka, Bangladesh in 2013 showed (in which well over 1,000 workers died manufacturing clothing for many major western brands) the reality is anything but. The aftermath of the Rana Plaza disaster shone a light on how few brands were aware of where their products were being manufactured, let alone the conditions in which they were being made. In response to this, a growing trend has developed for supply chain disclosure in the apparel sector, spurred on by groups such as the Transparency Pledge coalition and Fashion Revolution. However, in response to these calls for greater transparency, supply chain disclosure has been inconsistent, difficult to track from one website to another and data is often locked away in non-machine readable formats such as PDFs or tables embedded in websites. A lack of standard formatting for information as basic as name and address data (coupled with the poor quality of this data) makes it difficult and costly for anyone to compare across datasets and understand shared connections to facilities. Data has been stuck in silos and lacked a universal, central ID through which systems could synchronize, making interoperability between systems impossible. Enter the Open Apparel Registry (OAR). The OAR was launched in March 2019 to address this data challenge. At its heart, the OAR exists to drive improvements in data quality for the benefit of all stakeholders in the apparel sector. As well as many other efficiency and process benefits, the way the OAR organizes and presents data ultimately improves the lives of some of the most vulnerable workers in global supply chains. During our talk, we’ll share details about the challenges facing the apparel sector, including low levels of technical sophistication and understanding of open data; collaborative work that’s being done to educate the sector on the power of open data, including the launch of the Open Data Standard for the Apparel Sector (ODSAS); before finishing up with inspiring examples of how data from the Open Apparel Registry being freely shared and used is creating meaningful changes in the lives of some of global society’s most oppressed people.",kshaw,open-apparel-registry,openapparelreg,,
psoriano,<NAME>,csv-detective: solving some of the misteries of open data,"Over the last few years, the emphasis on data quality has evolved from being a nice to have to an absolute necessity. As already stated in past editions of this venue, the quality of open data is essential to *truly fulfill its promise as a channel of empowerment*. In practical terms, data quality for tabular data entails, among other tasks, checking the structural integrity and schematic consistency of its contents. In order to satisfy these requirements, we need to look into the files content and first determine whether our files are indeed CSV files, and secondly, and more importantly, we want to discover the type of data we have in order to properly validate it and thus evaluate and then hopefully improve the quality of our datasets. In this talk, we present our work on the automatic detection of data types within the columns of CSV files. Going beyond classic computer science data types (float, integer, date), we are also interested in detecting more specific in-domain data categories. Specifically, given a CSV file, we look into its columns and determine whether it contains postal codes, enterprise identifiers, geographic coordinates, days of the week, and so on. We will show how applied to data from the French open data platform that we maintain (data.gouv.fr.), these specific types allow users to better control data in order to join or link datasets between them. We see our work as a first step and important stepping stone towards data integrity and validation checks while also facilitating the discoverability and contextualization of open datasets. Our approaches are evaluated by annotating and testing over thousands of columns extracted from more than 15 000 CSV files found in data.gouv.fr.",psoriano,etalab,Etalab,,
dwinston,<NAME>,CSV-LD: Spreadsheet-based Linked Data,"Comma-separated-values (CSV) is a useful data serialization and sharing format. This talk introduces the idea of CSV-LD as a CSV-based format to serialize Linked Data, mirroring the way that JSON-LD is a JSON-based format to serialize Linked Data. ""CSV"" here includes any dialect that uses a different delimiter, such as tab-separated-values (TSV). The syntax of CSV-LD is designed to easily integrate into workflows that already use CSV, and provides a smooth upgrade path from CSV to CSV-LD. It is primarily intended to be a way to use Linked Data as part of spreadsheet-based data entry; to facilitate data validation, display, and conversion of CSV into other formats via use of CSV on the Web (CSVW) metadata; and to build FAIR data services. The term ""CSV-LD"" was previously used to describe a now-obsoleted precursor to the CSVW specifications; both approaches require a second file, a JSON-LD template document, to be shared along with a CSV file. The approach described here, in contrast, requires only a CSV file from the data producer, one that includes links to CSVW-powered metadata.",dwinston,dwinston,,,
dbeaton,<NAME>,"The Ontario Neurodegenerative Disease Research Initiative's Neuroinformatics & Biostatistics teams pipelines, partnerships, and products.","The Ontario Neurodegenerative Disease Research Initiative (ONDRI) is a large-scale longitudinal project across five disorders spanning 520 participants, each with 3 or more annual assessments, and from 14 sites across the province. ONDRI’s data span lab measures, genetics, ocular imaging, eye-tracking tasks, many cognitive assessments, various brain imaging modalities, and an extensive array of clinical assessments. These data come in many types, shapes, and sizes. Different domain and data experts have different expectations of data standards, how data are curated, and how data are analyzed. However, ONDRI is more than data: it is also 100s of researchers and clinicians coming together to better understand neurodegenerative and cerebrovascular disorders. So, ONDRI’s Neuroinformatics & Biostatistics (NIBS) team built both an infrastructure (data) and a culture (people) around the standardization and integrity of such diverse data. We did this with (1) pipelines: supporting curation from preparation through to analyses and reporting, (2) partnerships: working closely with others on training & collaboration, and (3) products: the tools we build and use; many of whichare publicly available: https://github.com/ondri-nibs. ONDRI’s data standards and outlier analyses are central to the pipelines. Our standards revolve around “data packages” that include data and dictionaries with specific nomenclature and features (e.g., predefined and unambiguous missing data codes). We built the standards around tabular (.csv format) data, then extended standards to “non-tabular” or file-based data, such as neuroimaging. We also perform a battery of (mostly) multivariate outlier analyses. We share standardized reports (written in RMarkdown) for standards and outliers with research and clinical experts to review. These steps identify anomalies and allow us to correct errors. We were able to build a data-focused culture based on shared clinical & research goals, recognizing the difficulty with & importance of data curation, and with an eye toward public release of the data.",dbeaton,derekbeaton,derek__beaton,,
kbreseman,<NAME>,Environmental Enforcement Watch: Environmental Data Justice through Participatory Data Science,"The U.S. EPA maintains an open dataset of information about permits issued under the Clean Air, Clean Water, and Resource Conservation and Recovery Acts, among others. This dataset describes, for instance, how much pollution an industrial facility is permitted to discharge into waterways and how many times they have been fined by the EPA—important information for environmental justice. But though there is a lot of information about permits and pollution, it is not organized in such a way as to answer questions about health or aggregate impacts—the kinds of questions people living in the area might find most meaningful. In the Environmental Enforcement Watch (EEW) project, we work with communities potentially impacted by facilities’ (non)compliance with their permits to co-develop open source Jupyter Notebooks. We use these to explore questions partners have about their environment and their communities’ health. In this presentation, we’ll explain our approach, show Notebooks we’ve developed, and share stories and outcomes from this attempt to model a more participatory approach to environmental governance.",kbreseman,edgi-govdata-archiving,EEW_Network,,
atravitz,<NAME>,"Scaling computations and maintaining reproducible projects with signac, an open-source framework","Data science projects are often messy, with complex workflows and large, heterogeneous parameter spaces. Bash scripting and long file names can only get you so far, and can result in a project that is incomprehensible to collaborators or your future self. The open-source data management framework, signac, (https://signac.io) makes it easy to maintain organized and reproducible projects, and integrates with existing file-based workflows. The serverless data management and lightweight workflow model ensure that projects are easily transferable between laptops and high-performance computing environments, and the data model is well suited for high-dimensional parameter searches or hyperparameter optimization of machine learning models. In this talk, we will demonstrate the signac approach to managing data science projects, emphasizing its use in real-world scenarios. The signac framework not only increases research efficiency, but makes best practices and scalability easy and intuitive.",atravitz,atravitz,,,
alsteele,<NAME>,Data visualization and crowdsourced research: experiments in collective storytelling,"These days, everyone seems to trying to harness the power of the crowd. Amnesty International’s “Decoders” brings together “digital volunteers to tackle human rights abuses around the world”. Bellingcat crowdsources information for their online investigations, and has transformed their newsroom into an open source project. Amazon’s Mechanical Turk has long altered how research is conducted through their crowdsourcing marketplace. These tools have paved the way for new kinds of journalism and research, where theoretically anyone can contribute to projects from anywhere around the world. However, while crowdsourcing has grown as a source of data, visual storytelling has usually been left out in the process. Online tools like Datawrapper and Tableau have made data visualization easier than ever, but because storytelling with data is computationally expensive – and usually custom-made for the story – the crowdsourcing process has usually been completed before it is turned into a visual design. They don’t usually walk in lockstep with each other. This talk will attempt to bridge this gap, if only just a little. It reviews a project called supply-chains.us, which is building a sustainable open-source database that anyone can contribute to, with an integrated visualization that updates over time. This talk will describe how the project was built, why visual storytelling is important for research, and how open-source projects can be an opportunity for collective storytelling.",alsteele,aleesteele,aleesteele,,
kawhite,<NAME>,"WordPress as Data, 5 Years In","In 2016,WordPress introduced a REST API to expose site content as JSON. Also in 2016, we had the opportunity to present at csv,conf about this paradigm shift: what would it mean for all the content in any WordPress website to be accessible as data? Since then, WordPress has fundamentally reinvented itself using that new API. The project has been reimagined from the inside out, with the humble content block as the foundational unit of site data. But, where was that explosion of data-driven WordPress applications we hoped for? In this talk we will look at what worked as we integrated this new API into WordPress, and at where we stumbled. We'll explore how roles, access, and authentication limit the utility of data. We'll candidly discuss how burnout affects project stewardship. And we'll also celebrate a tremendous, successful change of course for a venerable open source project, and think ahead to what ""WordPress as Data"" may mean in five more years' time!",kawhite,kadamwhite,kadamwhite,,
hsenyondo,<NAME>,"Rdataretriever:  A platform for downloading, cleaning, and installing publicly available datasets","The rdataretriever provides an R interface to the Python-based Data Retriever software. The Data Retriever automates the core steps of data preprocessing including downloading, cleaning, standardizing, and importing datasets into a variety of relational databases and flat file formats. The rdataretriever additionally supports provenance tracking for these steps of the analysis workflow by taking snapshots of the datasets to be committed at the time of installation and allowing them to be reinstalled with the same data and processing steps in the future. Finally, the rdataretriever supports the installation of spatial datasets into relational databases with spatial support. The rdataretriever provides an R interface to this functionality and also supports importing of datasets directly into R for immediate analysis. These tools are focused on scientific data applications including several widely used but difficult to work with, datasets in ecology and the environmental sciences. The rdataretriever allows R users to access the Python Data Retriever processing platform through a combination of the reticulate package and custom features developed for working in R. Because many R users, including the domain researchers most strongly supported by this package, are not familiar with Python and its package management systems, a strong emphasis has been placed on simplifying the installation process for this package so that it can be done entirely from R. Installation requires no direct use of Python or the command line. Detailed documentation has been developed to support users in both installation and use of the software. A Docker-based testing system and associated test suite have also been implemented to ensure that the interoperability of the R package and Python package are maintained, which is challenging due to frequent changes in reticulate and complexities in supporting cross-language functionality across multiple operating systems and R programming environments (terminal-based R and RStudio).",hsenyondo,henrykironde,henrykironde,,
mtrizna,<NAME>,Making Smithsonian Open Access Accessible with Python and Dask,"In February 2020, the Smithsonian Institution released almost 3 million images and over 12 million collections metadata records under the Creative Commons Zero (CC0) license. The release was made available via a web API, a GitHub repository, and via the Registry of Open Data on Amazon Web Services (AWS). The format of the release on the GitHub and AWS sources made the data well-suited for parallelized analysis, but only with deep knowledge of the complex data structures. In this talk we will discuss how we used the Python Dask library to unlock this parallelization and make the data more accessible, as well as a student intern project that used Python tools to uncover insights specifically into the holdings of the National Museum of American History.",mtrizna,MikeTrizna,MikeTrizna,,
gvdishington,<NAME>,Using GitHub Actions to Accelerate your Data Efforts,"Data science work often requires computing resources that isn’t available to practitioners from disadvantaged backgrounds or located in “data deserts” with low technology access. Cloud computing is an option in such situations but can be costly and not friendly to beginners who don’t know what infrastructure to pick. Most of the data science community thinks of GitHub as only a code storage repository with some management features, but GitHub Actions provide a powerful and often free resource for computing that can perform many data collection and analysis needs. This introduction to GitHub Actions will provide an overview of everything needed for GitHub users to get started applying GitHub Actions to their projects. We’ll see both simple and advanced examples of the YAML format that controls various workflows and how it ties into traditional data science needs, like testing and automation. Time permitting, we’ll also see how these workflows can be used for special applications, such as open source intelligence and machine learning.",gvdishington,GrantRVD,usethespacebar,,
cstihler,<NAME>,"Better sharing, brighter future","2021 marks the 20th anniversary for Creative Commons. For 20 years we have helped build a commons of 'open' creative content free of most copyright restrictions. With CC licenses and tools, we created a simple way for creators to opt into a more permissive model of sharing. As we reflect on the past and think of the future, we know that today we must pursue a commons of knowledge and culture that is inclusive, just and which inspires reciprocity - a commons that serves the public interest. To that end we must transition from promoting more sharing to fostering better sharing. In this talk, I want to share what better sharing means in practice and how we can achieve this together.",cstihler,,C_Stihler,,
rharter,<NAME>,Getting Credit for Invisible Work,"A lot of the hard important work that goes into good data work is invisible. For example, most of an exploratory data analysis is testing *and discarding* hypotheses. We explore complex data, so we can distill our findings into a simple narrative. We never talk about all the hypotheses we’ve discarded!If we’re doing it right, we make our work look simple. This is super valuable, but can cause problems when we try to demonstrate our value. This talk covers some strategies for getting credit for this super valuable but invisible work.",rharter,harterrt,harterrt,,
bgtzovaras,<NAME>,Using wearables to detect infections: A co-created & community-led pandemic response,"In March 2020 – during the early days of the COVID-19 pandemic – a big question popped up in the heads of the data nerds of the Open Humans & Quantified Self community: We are wearing all these sophisticated wearable devices that measure our heart rate, respiratory rate and even sometimes even body temperature - can these things be used to find out when we’re falling sick even before we consciously notice having symptoms? Starting from this initial question a community quickly formed to iteratively and collaboratively create “Quantified Flu” – an open source online-tool to facilitate individual learning and insights at the intersection of early physiological warning signs and self-observed symptoms. The final prototype enables people to report over 15 different symptoms through daily reporting while monitoring physiological signals from a large variety of the most common wearable devices. We’ll not only showcase the final prototype, but also investigate how the open and iterative development process unfolded and how this helped to create a tool that fits the needs of the self-tracking community. We furthermore see how this fit of digital affordances & needs enabled a level of user engagement that’s highly untypical for the “mobile health” space.",bgtzovaras,gedankenstuecke,gedankenstuecke,,
hlapiroff,<NAME>,An API for Tracking Press Freedom,"In 2017, as the Trump administration was ramping up a war of open hostility against the press, a collaborative of press freedom organizations launch a database and website to track aggressions against journalists. The U.S. Press Freedom Tracker records incidents include arrests, border stops, physical assaults, subpoenas, equipment damage, and more. In addition to detailed articles, all four years (and counting) of the Tracker data is made available through an API that can be used to track trends, aggregate incidents related to specific events, and more. We'll show off examples of how the API is already used to power interactive data visualizations and give an overview of how anyone can access and use the data.",hlapiroff,harrislapiroff,harrislapiroff,,
bherman,<NAME>,Static datasets aren't enough: where deployed systems differ from research,"The focus on static datasets in machine learning and AI training fails to translate to how these systems are being deployed in industry. As a result, data scientists and engineers aren't considering how these systems perform in changing, real world environments nor the feedback mechanisms and societal implications that these systems can cause. In the session, we will highlight existing tools that work with dynamic (and perhaps streaming) data. We will suggest some preliminary studies of activities and lessons that may bridge the gap in data science training for realistic data.The goal of the talk is to:<br> - Point to resources for AI practitioners to engage with dynamic datasets<br> - Engage in discussion about the impact of feedback loops and other consequences on the real world<br> - Brainstorm new approaches to teaching skills on dynamic datasets",bherman,bernease,bernease,,
jmiller,<NAME>,Making Police Call Data Readable using Pandas and Eland,Many cities in the United States have public data as a part of the oversight initiatives. These datasets often make the data incredibly hard to analyze. This talk breaks down how I compiled data from the San Diego Police Department into dataframes and make it observable in Elasticsearch Kibana.,jmiller,kjaymiller,kjaymiller,,
dmcarthy,<NAME>,From tweet to sheet: crowdsourcing a global survey of Open GLAM,"Three years ago a simple tweet between friends caught fire, inspiring Douglas and Andrea to start surveying open access in the GLAM (Gallery, Library, Archive, Museum) sector. Since that moment, the tiny acorn of a Google Sheet has matured into a oak tree, growing in scale, complexity and prominence. In this talk, Douglas and Andrea will share the remarkable story of the Open GLAM survey's development and impact. They'll share actionable insight into crowdsourcing and managing data with a global community of researchers, activists, copyright experts and GLAM professionals. Finally, Douglas and Andrea will discuss how they've leveraged the Open GLAM survey into Wikidata and other domains. Twitter: https://twitter.com/CultureDoug + https://twitter.com/AndeeWallace",dmcarthy,,,,
kmontgomery,<NAME>,Save your sanity with Synapse,"Synapse is a freely available data repository to organize your research, get credit for it and collaborate with colleagues and the public. Data exploration provokes a curiosity that can lead the analyst through a labyrinth of possible outcomes. Synapse contains features to track file versions with an immutable identifier, string together file transformations, manage the visibility of sensitive data, make data discoverable with structured metadata, describe experimental context with unstructured copy and connect your output with code. All of this can be accessed from the web or programmatically with R, Python or the Command Line, thus integrating seamlessly into your scientific analysis. Allow Synapse to hold the memory of the places you have been to instead focus your energy on testing creative hypotheses.",kmontgomery,kelshmo,,,
jaclark,<NAME>,Algorithms as Data: A Case Study for Turning Algorithmic User Experiences into Research Data Objects,"Why does your technology seem to know what you want before you do? Increasingly, our digital experiences are mediated by obscure algorithms. But what are algorithms and how can we audit or quantify them? This session introduces an ""algorithmic awareness"" research module and a generic code application for auditing algorithmic user experiences and quantifying those experiences as datasets for analysis. What if algorithms weren’t the Ghost in the Machine? Can algorithms be understood as part of the open data continuum? Montana State University Library, with grant funding from the Institute of Museum and Library Services, has conducted research in order to develop software and a curriculum to support the teaching of ""Algorithmic Awareness"": an understanding around the rules that govern our software and shape our digital experiences. Taking our inspiration from investigative data journalists, like The Markup, we are looking to introduce our research module for algorithm auditing practices using code, web scraping methods, and structured data formats to uncover proprietary algorithms and turn them into research data objects for analysis. (Code is available in our #AlgorithmicAwareness GitHub repository.) Our case study for the module will be the YouTube Video Recommendation Algorithm which has come under criticism for its tactics in drawing parents’ and childrens’ attention to their videos. Our goal will be to show the generic patterns, data points, and scripts one can use to analyze algorithmic user experiences and demonstrate how code can be used to turn algorithms into datasets for analysis. In the end, attendees will be able to realize actionable steps for seeing algorithms as data objects, gain a sense of the first steps one can take to programmatically audit these systems with code, and take away investigative data techniques for their own work.",jaclark,jasonclark,jaclark,,
styrrell,<NAME>,Frictionless Data for Wheat,"The international wheat community has embraced the omics era and is producing larger and more heterogeneous datasets at a rapid pace in order to produce better varieties via breeding programmes. These programmes, especially in the pre-breeding space, have encouraged wheat communities to make these datasets available more openly. This is a positive step, but the consistent and standardised collection and dissemination of data based on rich metadata remains difficult, as so much of this information is stored in papers and supplementary information. Furthermore, whilst ontologies exist for data descriptions, e.g. the Environmental Factor Ontology, the Crop Ontology, etc., use of these ontology terms to annotate key development characteristics across disparate data generation methods and growing sites is rarely routine or harmonised. Therefore, we built Grassroots, an infrastructure including portals to deliver large scale datasets with semantically marked-up metadata to power FAIR data in crop research. As part of the UK Designing Future Wheat (DFW) programme, we generate a variety of data ranging from field trial experimental information, sequencing data and phenotyping images, through to molecular biology data about host and pathogen interactions, nitrogen use efficiency, and other key treatment factors. As such, there is an increasing need to be able to manage this data and its metadata to allow for consistent, easy dissemination and integration with other datasets and within analytical tools and workflows. We decided that Frictionless Data was the right framework to use due to its ease of use and open standards, so we developed open source tools for FAIR data sharing to automatically generate Frictionless Data Packages for these datasets on both our Apache/iRODS and CKAN portals.",styrrell,billyfish,,,
gsantarsieri-psoriano,"<NAME>, <NAME>",(Machine) Learning from Open Data platforms,"Today, open data platforms host a wide and heterogeneous catalog of datasets. However, these datasets are often neglected in Machine Learning (ML) and other related tasks. This mainly happens because there are few available open data catalogs specialized in ML applications and because it is often unclear whether Machine Learning algorithms would be adequate and well performing on such datasets. Therefore, several open datasets go unused while they could be leveraged by the ML community to explain, evaluate, and challenge existing methods on real open data. For instance, these real-world data could be used by professors teaching ML courses, by students taking these courses, by researchers testing current and novel ML approaches, and possibly to promote the intersection of open data, ML and public policy. In this talk we will show you how we are tackling this issue working on datasets from data.gouv.fr (DGF),  the French open data government platform. We aim to answer the question of what makes a dataset suitable and well performing for Machine Learning tasks by leveraging open source tools. Our goal is to establish a first small empirical assessment of the characteristics of a dataset (size, balance of its categorical variables and so on) that make it a “good fit” for Machine Learning algorithms. Specifically,  we first manually select an adequate subset of datasets from DGF. Then we perform a statistic profiling on each of these datasets. Thirdly, we automatically train and validate a set of ML algorithms on them and we cluster the datasets according to their evaluation results. These steps help us to better understand the nature of each dataset and thus determine which ones seem suitable for ML applications. Based on these datasets, and inspired by existing resources, we build the first version of a catalog of open datasets for ML. We hope that this platform will be a first stepping stone towards the reuse of open datasets in  Machine Learning contexts.",,,,,
scanelon-ehare,"<NAME>, <NAME>",Revealing Room for Improvement in Accessibility within a Social Media Data Visualization Learning Community,"We all aim to use data to tell a compelling story, and many of us enjoy sharing how we got there by open-sourcing our code, but we don't always share our story with everyone. Even kind, supportive, and open communities like the #TidyTuesday R learning community on Twitter has a ways to go before the content shared can be accessible to everyone. Lived experiences of blind R users tell us that most data visualizations shared for TidyTuesday are inaccessible to screen reading technology because they lack alternative text (i.e. alt text) descriptions. Our goal was to bring this hidden lack of accessibility to the surface by examining the alternative text accompanying data visualizations shared as part of the TidyTuesday social project.  We scraped the alternative text from 6,443 TidyTuesday images posted on Twitter between April 2, 2018 and January 31, 2021. The first image attached to each tweet was considered the primary image and was scraped for alternative text. Manual web inspection revealed the CSS class and HTML element corresponding to the primary image, as well as the attribute containing the alternative text. We used this information and the ROpenSci {RSelenium} package to scrape the alternative text. Our preliminary analysis found that only 2.4% of the images contained a text description entered by the tweet author compared to 84% which were described by default as ""Image"". This small group of intentional alternative text descriptions had a median word count of 18 (range: 1-170), and a median character count of 83 (range: 8-788). As a reference point, Twitter allows 240 characters in a single tweet and 1,000 characters for image descriptions. This analysis was made possible thanks to a dataset of historical TidyTuesday tweet data collected using the ROpenSci {rtweet} package, and openly available in the TidyTuesday GitHub repository (https://github.com/rfordatascience/tidytuesday). Twitter: @spcanelon; @DogGeneticsLLC GitHub: @spcanelon; @LizHareDogs",,,,,
ksill,<NAME>,The Water Point Data Exchange - using Open Data for Universal Water Access,"Water points are still the main - or sometimes only - source of water for millions of people living in rural areas of the world. The Water Point Data Exchange (WPDx) unlocks the potential of water point data to improve the information available to governments, service providers, researchers, and NGOs, and create data-based decision-making for increasing rural water access. Governments and their partners are already collecting substantial amounts of water point data. However, data is often too fragmented to use, with different approaches for data collection and storage. Without harmonization among these different data sources, the true potential of this information remains untapped so it is only used for reporting and not decision support. By establishing a platform for sharing water point data throughout the global water sector, WPDx adds value to the data already being collected. It brings together diverse data sets and establishes an unprecedented understanding of water services. All stakeholders can easily use the WPDx Data Standard to harmonize their existing data structure. Formatting existing data into the WPDx Data Standard typically takes less than 30 minutes. The WPDx Data Standard fits most water point data, even if it was collected with no knowledge of the standard. After formatting, files can be uploaded and published using the WPDx ingestion engine. It removes duplicate entries, links multiple updates to the same water point, and integrates the data into the Global Data Repository. Using this data and advanced GIS and machine learning analysis, several decision support tools were built. Designed in partnership with governments and data scientists, these tools provide concrete insights, like which water point to send a technician to rehabilitate next to reach the most people. In this presentation we will share some of the challenges we face and the methods we use in making this possible.",ksill,,,,
zselvans,<NAME>,Distributing Power with Open Data,"Some lessons learned from our work getting electricity system data into the hands of activists and researchers who are trying to shift power in US energy policy making. Under what circumstances can data help drive policy changes, and how does that change happen? Is open data always preferable in an advocacy context? What does it really mean for data to be accessible to advocates? US energy regulation is extremely technocratic, and largely happens out of public view. For years we’ve tried to address the information asymmetry that exists between advocates and large utilities by publishing cleaned open datasets detailing the inner workings of the US electricity system. We use Python data science tools to prepare the data and they serve us well, but most advocates -- even those with a lot of domain expertise -- are either hardcore spreadsheet users, or are just starting to dabble in Jupyter Notebooks. Now that we have a decent data pipeline, we are turning our attention to improving the data’s accessibility for our target users, using Datasette, Docker containers that run Jupyter, and Intake data catalogs.",zselvans,zaneselvans,ZaneSelvans,,
kbroman,<NAME>,Data cleaning principles,"Why don't we teach data cleaning? It has been said that it is difficult to generalize: that what we learn from cleaning Medicare data cannot be readily applied to the cleaning of RNA-seq data. To the contrary, I think there are important general principles for cleaning data, and there are more commonalities in the creative process of data cleaning than in other aspects of data analysis. I will seek to delineate and illustrate these principles, which include: <br>(1) think about what might have gone wrong and how it might be revealed<br>(2) study the pattern of missing data and ask why they are missing<br>(3) use care when merging datasets and focus on labels not position<br>(4) if things are supposed to match, check that they do<br>(5) if things are supposed to be distinct, check that they are<br>(6) look for outliers and other oddities by making lots of plots, particularly scatterplots<br>(7) look for batch effects by making lots of plots, particularly plots against time and scatterplots colored by batch<br>(8) ask for the primary data and also metadata<br>(9) don’t be shy about asking questions<br>(10) document not just what you did but also why you did it<br>(11) don’t trust anyone (even yourself)<br>(12) allocate sufficient time and energy to the effort",kbroman,kbroman,kwbroman,,
pashlock,<NAME>,"The State of Open Government Data Standards","This talk will provide a follow-up to the CSVConf v5 talk titled “The State of Open Government Data Infrastructure” but this time with a focus more specifically on the work to provide more institutional capacity and documentation around data standards and data interoperability from the perspective of the US Federal Government. The talk will provide an update on the work to establish a catalog and repository of resources around data standards as called for by the US Federal Data Strategy and it will highlight a few examples of data standards collaboration across agencies, across sectors, across levels of government, and across international borders. Beyond the discussion of the DCAT metadata standards from last year this talk may cover a variety of different case studies including data standards developed to support state by state standardized election information look-up and election results for US voters, efforts to standardize information about government benefits, services, and eligibility criteria to improve access to government services by those who need them most, and the Work Zone Data Exchange project to standardize information about roadway construction and other disruptions for transportation route planning",pashlock,philipashlock,philipashlock,slides_url,talk_url
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