<commit_before>id,name,title,img,twitter,slides_url,abstract
ebecker,<NAME>,Empowering people by democratizing data skills,,ErinSBecker,," Although petabytes of data are now available, most scientific disciplines are failing to translate this sea of data into scientific advances. The missing step between data collection and research progress is a lack of training for scientists in crucial skills for effectively and reproducibly managing and analyzing large amounts of data. Already faced with a deluge of data, researchers themselves are demanding this training and need to learn while on-the-job. They require training that is immediate, accessible, appropriate for their level and relevant to their domain. This training needs to include not only technical skills, but ways of thinking about data to provide learners with the knowledge of what is possible along with the confidence to continue self-guided learning. Short, intensive, hands-on Software and Data Carpentry workshops give researchers the opportunity to engage in deliberate practice as they learn these skills, starting with strong foundational skills and receiving feedback as they learn. This model has been shown to be effective, with the vast majority (more than 90%), of learners saying that participating in the workshop was worth their time and led to improvements in their data management and data analysis skills. We have trained over 20,000 learners since 2014 on 6 continents with over 700 volunteer instructors, with the goal of providing effective training that empowers researchers to turn data into knowledge and discovery."
mhertzfeld,<NAME>,Designing with data: prototyping at the speed of learning,https://michellehertzfeld.com/assets/img/michelle_hertzfeld.jpg,meiqimichelle,,"We've all done it. Mocked up a perfect dashboard with evenly-spaced bars on the bar charts, each with a readable label because of course all the bars are big enough for that. All the boxes look so nice, evenly spaced. Then, in comes the data, and it's a jungle out there! The biggest value on the bar chart is huge, rendering the rest of the bars unreadable. We forgot to design for what we thought were mere edge cases (data-equals-zero versus data-equals-no-data, anyone?). Everything lining up evenly still? Ha! With some labels coming in at one word, and others hefting across at seven, forget it! ...But it doesn't have to be this way. I'm going to tell you about the user-research- and data-driven design and prototyping process that created https://useiti.doi.gov/. By creating a team prototyping process that moved from testing core ideas in words and quick data prototypes; to making more robust, visually-designed prototypes to test with core users; to incorporating those learnings back into the next prototypes and research plans, we were able to enjoy a team flow that created designs that fit the data, and data that communicated clearly. A++, would employ process again."
dwhalen,<NAME>,The evolution of a GTFS data pipeline,https://pbs.twimg.com/profile_images/507955752346976256/o7jSxiIT.jpeg,invisiblefunnel,,"The General Transit Feed Specification is one of the primary formats that public transit agencies use to communicate changes to their systems. The specification is simple and flexible, leaving much to be interpreted by the publisher. In order to build a GTFS data pipeline that is similarly flexible you need to understand what makes a particular transit system unique from others, and how those differences are expressed through through the data standard. I’d like to share stories of a few fascinating transit systems and the wonderful people that work hard to share them with the world. (This is a big part of what we do at Remix.)"
dbrawley,<NAME>,Urban/Information: Mapping the US Postal Service Vacancy Survey,http://c4sr.columbia.edu/sites/default/files/styles/square-crop-for-button/public/CSR_Dare-square_0.jpg?itok=u1KTHr7K,darebrawley,,"In this talk I will use one dataset, and 165 maps, as a lens to examine the interplay between information landscapes and urban landscapes. In an era when the largest technology companies have established urban think tanks which seek to develop modes of urban governance by algorithm, the talk aims to reveal the ways that sources of data often invisibly, and perniciously, shape public policy through the methods behind their collection. The dataset – the ‘U.S. Postal Service Vacancy Survey’ – is collected by US postal workers on their daily routes delivering mail and was used to help determine which cities received federal aid in the aftermath of the 2008 foreclosure crisis. At each home or business on her route, a mail carrier notes whether a house is occupied (whether the occupants have been picking up their mail). The residences left empty for 90 days or longer are represented in this dataset. The USPS Vacancy Survey powerfully reveals the logic of abstraction at work in all “data”—in this case, the “national vacancy rate” is an abstraction that is both fully dependent on and masks the daily routines of individual postal workers making observations about specific bundles of mail left to languish. "
hazhar, <NAME>,"✊s, 🌹s, and major 🔑s: Emoji data science & journalism",http://www.hamdanazhar.com/uploads/2/5/5/1/25511129/hamdanazhar-yp2014-1.jpg,hamdanazhar,Emojis have been called a "new type of language". According to statistics cited by Ad Week as much as 92% of the online population uses emojis. Twitter reports that since 2014 alone over 110 billion emojis have been tweeted. Yet despite the profusion of emojis in digital life little research has been done that leverages emojis to understand popular sentiment around current issues. I’ll talk about how emoji data science (a largely unexplored field) might be a powerful new methodology for both the computational social sciences as well as fast data journalism. I'll share preliminary research based on an analysis of millions of tweets that explores the relevance of emoji analytics to fields ranging from pop culture (i.e. the Kanye West vs. Taylor Swift dispute) to politics (the US presidential elections as well as Brexit) to gender norms to the Olympics and more.
cbrosseau,<NAME>,How do I get that? Strategies for requesting data and negotiating for it,https://www.google.com/imgres?imgurl=https%3A%2F%2Flh6.googleusercontent.com%2F-BTw4_3ARJ4g%2FAAAAAAAAAAI%2FAAAAAAAAAEY%2F2Tvf51mEzNs%2Fphoto.jpg&imgrefurl=https%3A%2F%2Fplus.google.com%2F104823147284278372388%2Fabout&docid=ba61zjXO5qAKDM&tbnid=LnAxBtI8GxPXWM%3A&vet=1&w=512&h=512&itg=1&bih=1240&biw=1943&q=carli%20brosseau&ved=0ahUKEwj3noiG8I3SAhUO24MKHQEsCRUQMwghKAUwBQ&iact=mrc&uact=8#h=512&imgrc=LnAxBtI8GxPXWM:&vet=1&w=512,carlibrosseau,,"Governments of all sizes create and maintain data sets that they don’t put on the internet. But all that data is public, and it could be yours to analyze and visualize – well, most of it. We’ll talk about how to identify what public data sets exist and how to access them using state and federal public records laws. We’ll also survey common roadblocks and how to navigate around them."
dleem, <NAME>em,"Smelly London: visualising historical smells through text-mining, geo-referencing and mapping.",,1208DL,Smelly London project (www.londonsmells.co.uk) aims to bring together historical data with modern digitisation and visualisation to give us a unique revealing and visceral glimpse into a London of the past and what it tells us about London today. Text-mining and analysing the MOH reports tells the intimate narratives of the everyday experiences of 19th and 20th century Londoners through the 'smellscape'. The Smelly London project provides a great opportunity to demonstrate how new knowledge and insights have risen from the use of powerful digital applications. All outputs generated from the project will be open access and open source. Our data is available in a public repository GitHub (https://github.com/Smelly-London) and other platforms such as Layers of London (http://layersoflondon.blogs.sas.ac.uk/about-the-project/) and Smelly Maps: Good City Life (http://goodcitylife.org/index.html).,
wlyon,<NAME>,Analyzing The Trumpworld Graph: Applying Network Analysis to Public Data,https://pbs.twimg.com/profile_images/502931400882794496/Dy9gq9V2.jpeg,lyonwj,,"A few weeks ago BuzzFeed released a public dataset of people and organizations connected to <NAME> and members of his administration. As they say in their blog post announcing the data:

    No American president has taken office with a giant network of businesses,    investments, and corporate connections like that amassed by <NAME>. His family and advisers have touched a staggering number of ventures, from a hotel in Azerbaijan to a poker company in Las Vegas.

In this talk we will show how to model this data as a graph, write Cypher queries to find interesting connections and visualize the results. In addition, we will show how to add public data from USASpending.gov on government contracts and campaign finance from the FEC, allowing us to answer questions like:

* How are members of the Trump administration connected to vendors of government contracts?

* Who are the most influential people in the network and how are they connected to Trump?"
mpclaypotch,<NAME>' Claypotch,A Better 'Edit' Button for GitHub-Hosted Data,http://bits.potch.me/headshot.jpg,potch,,
eschles,<NAME>,Changing the world with data. With combatting human trafficking as an example.,https://avatars2.githubusercontent.com/u/928957?v=3&s=460,EricSchles,,"My talk is about how to set up national networks, support national networks and international networks that work on a specific issue.  I'll pick specific examples from my work in the anti trafficking space and how I worked nationally and internationally.  I'll choose specific tools I built and the problems they solved with in the anti trafficking space and talk about how these tools helped to further long term objectives within the space.  "
npenfold,Naomi Penfold,Innovation to facilitate data sharing in the life sciences and biomedicine,https://drive.google.com/open?id=0B_a2JekZMrW8LW5TdUdhMjZvZGs,eLifeInnovation,,"The drive for more open data in the life sciences and biomedical research is supported by research funders and policymakers as a means to address the ‘reproducibility crisis’, derive more value from research projects, and avoid duplication of effort in research. Journal and funder policies encouraging the deposition of datasets in repositories, where appropriate, help to enforce data-sharing practises but do not ensure that the data itself is of sufficient quality. The physics, computer science, and computational biology communities have developed tools that facilitate the publication of reproducible computational analysis, but these are geared at users with a high degree of digital literacy. For biologists who rely on basic spreadsheets and desktop statistical analysis software, the barrier to entry to these tools remains high. Further, the effort required to translate a myriad of data objects into a well-curated dataset for deposition is not directly paired to the benefits that may come from sharing such data.

At eLife, we are interested in tools and technologies that will help and incentivise life science and biomedical researchers to share their data more openly. We are actively seeking and showcasing open-source tools, such as Binder, that augment the static research article with richer, dynamic and reproducible artefacts, and cater for biologists in doing so. We are also interested in processes that reduce the work required to share datasets, improve their discovery, facilitate their reuse, and pair the effort of sharing with meaningful rewards, such as credit, citation, and reputation gain. In this talk, I will discuss specific examples where data sharing has gained traction in the biology community, and outline key pain points remaining. I invite the community to join me in a conversation about how research technologists and publishers can best contribute towards open data in the life sciences and biomedicine."
tallen,<NAME>,Mantle: A Proposal for Automated Metadata and Linked Data for Scientific Users,https://www.dropbox.com/s/sf6cql6410hip7o/F70CADD5-EAF3-443B-A0E6-278337D8B9EA.JPG?raw=1,toph,,"Scientists work with increasing volumes of data, and managing that data is a growing part of their day-to-day workload. One set of time-consuming tasks is data integration; that is, bridging gaps of incompatibility to merge datasets from different sources. Even datasets in the same format may refer to the same conceptual entity with different language. The way to solve this problem is to connect datasets via their metadata. Much progress has been made in metadata standards, practices, and formats (CSV on the Web a.k.a. WCSV, Ecological Metadata Language (EML), and JSON-Linked Data). However, much data lacks robust metadata due to the limits of manual metadata curation. We propose an automated approach to metadata discovery that aims to facilitate data discovery and linking, joining, and merging of heterogeneous datasets. We outline the challenges to this problem and our strategy for addressing them, including a set of machine-learning classifiers and heuristics to identify the correct ontology or ontologies for fields and entities in datasets (e.g., location names, species taxonomies), and link these data across data sets with different sources and formats. We discuss how this tool can fit into scientific workflows and an open data ecosystem."
cmoravec, <NAME>,Location - Trends in Time and Place,http://www.moraveclabs.com/wp-content/uploads/2017/02/profile.png,Mor3Havoc,,
msyed,Moiz Syed,Data & Abuse of Power,https://pbs.twimg.com/profile_images/593124707949359104/G8p4Cn0A.jpg,moizsyed,,"We need data on public institutions so that we can keep them accountable. But when that data is collected and shared by the institution itself, they are in a powerful position to control the narrative. 

What can we as data practitioners do to help identify potentially biased narratives that could exist in this type of data? Because blindly trusting a biased dataset is just as harmful as blindly trusting a biased source.

I would like to talk a little about a vetting process, and about a few important types of biases in data that you should have in mind. This list is by no means complete and is still a work in progress, but it includes some of the more major types of bias that I’ve noticed in data, and what I think you can do about them."
hparker,<NAME>,Opinionated Analysis Development,https://goo.gl/photos/C7Rxys6qGeLfs3xp8,hspter,,"Traditionally, statistical training has focused primarily on mathematical derivations and proofs of statistical tests. The process of developing the technical artifact -- that is, the paper, dashboard, or other deliverable -- is much less frequently taught, presumably because of an aversion to cookbookery or prescribing specific software choices. In this talk, I argue that it's critical to teach generalized opinions for how to go about developing an analysis in order to maximize the probability that an analysis is reproducible, accurate and collaborative. By encouraging the use of and fluency in tooling that implements these opinions, we as a community can foster the growth of processes that fail the practitioners as infrequently as possible."
bobrien,<NAME>,Data Lovers in in a Dangerous Time,https://avatars2.githubusercontent.com/u/1154390?v=3&u=3f19a50e1302649a314ce3298481e23caeb86bba&s=400,formalfiction,,"In recent months archive-a-thons aimed at saving government data have sprung up all around the country in response to a shift in politics. These ""DataRescues"" have drawn together librarians, anthropologists, developers, data scientists, scientist-scientists, all in the name of preserving a shared interest in open knowledge. Hear a first-hand account of how this group is growing to meet the challenge of classifying, monitoring, and archiving everything "".gov""."
mreeve,Mouse Reeve,The Art and Science of Generative Nonsense,https://pbs.twimg.com/profile_images/807784644682752001/TjYal1Zv.jpg,tripofmice,,"Teaching machines to understand and communicate in human language is one of the great challenges of computer science. But what about the reverse -- what role can computers play in taking perfectly reasonable text and rendering it unreasonable? Consider Lewis Carroll's Jabberwocky, asemic writing, or invented languages. The tools for discerning semantic meaning in text can be used to dismantle it, and the corpora of written text leveraged towards generative confusion. This talk explores basic computational linguistics concepts, how to use them to make things make less sense, and the simple joys, subversive charms, and conceptual merits of nonsense."<commit_msg>Update 2017_speakers.csv<commit_after>id,name,title,img,twitter,slides_url,abstract
ebecker,<NAME>,Empowering people by democratizing data skills,,ErinSBecker,," Although petabytes of data are now available, most scientific disciplines are failing to translate this sea of data into scientific advances. The missing step between data collection and research progress is a lack of training for scientists in crucial skills for effectively and reproducibly managing and analyzing large amounts of data. Already faced with a deluge of data, researchers themselves are demanding this training and need to learn while on-the-job. They require training that is immediate, accessible, appropriate for their level and relevant to their domain. This training needs to include not only technical skills, but ways of thinking about data to provide learners with the knowledge of what is possible along with the confidence to continue self-guided learning. Short, intensive, hands-on Software and Data Carpentry workshops give researchers the opportunity to engage in deliberate practice as they learn these skills, starting with strong foundational skills and receiving feedback as they learn. This model has been shown to be effective, with the vast majority (more than 90%), of learners saying that participating in the workshop was worth their time and led to improvements in their data management and data analysis skills. We have trained over 20,000 learners since 2014 on 6 continents with over 700 volunteer instructors, with the goal of providing effective training that empowers researchers to turn data into knowledge and discovery."
mhertzfeld,<NAME>,Designing with data: prototyping at the speed of learning,https://michellehertzfeld.com/assets/img/michelle_hertzfeld.jpg,meiqimichelle,,"We've all done it. Mocked up a perfect dashboard with evenly-spaced bars on the bar charts, each with a readable label because of course all the bars are big enough for that. All the boxes look so nice, evenly spaced. Then, in comes the data, and it's a jungle out there! The biggest value on the bar chart is huge, rendering the rest of the bars unreadable. We forgot to design for what we thought were mere edge cases (data-equals-zero versus data-equals-no-data, anyone?). Everything lining up evenly still? Ha! With some labels coming in at one word, and others hefting across at seven, forget it! ...But it doesn't have to be this way. I'm going to tell you about the user-research- and data-driven design and prototyping process that created https://useiti.doi.gov/. By creating a team prototyping process that moved from testing core ideas in words and quick data prototypes; to making more robust, visually-designed prototypes to test with core users; to incorporating those learnings back into the next prototypes and research plans, we were able to enjoy a team flow that created designs that fit the data, and data that communicated clearly. A++, would employ process again."
dwhalen,<NAME>,The evolution of a GTFS data pipeline,https://pbs.twimg.com/profile_images/507955752346976256/o7jSxiIT.jpeg,invisiblefunnel,,"The General Transit Feed Specification is one of the primary formats that public transit agencies use to communicate changes to their systems. The specification is simple and flexible, leaving much to be interpreted by the publisher. In order to build a GTFS data pipeline that is similarly flexible you need to understand what makes a particular transit system unique from others, and how those differences are expressed through through the data standard. I’d like to share stories of a few fascinating transit systems and the wonderful people that work hard to share them with the world. (This is a big part of what we do at Remix.)"
dbrawley,<NAME>,Urban/Information: Mapping the US Postal Service Vacancy Survey,http://c4sr.columbia.edu/sites/default/files/styles/square-crop-for-button/public/CSR_Dare-square_0.jpg?itok=u1KTHr7K,darebrawley,,"In this talk I will use one dataset, and 165 maps, as a lens to examine the interplay between information landscapes and urban landscapes. In an era when the largest technology companies have established urban think tanks which seek to develop modes of urban governance by algorithm, the talk aims to reveal the ways that sources of data often invisibly, and perniciously, shape public policy through the methods behind their collection. The dataset – the ‘U.S. Postal Service Vacancy Survey’ – is collected by US postal workers on their daily routes delivering mail and was used to help determine which cities received federal aid in the aftermath of the 2008 foreclosure crisis. At each home or business on her route, a mail carrier notes whether a house is occupied (whether the occupants have been picking up their mail). The residences left empty for 90 days or longer are represented in this dataset. The USPS Vacancy Survey powerfully reveals the logic of abstraction at work in all “data”—in this case, the “national vacancy rate” is an abstraction that is both fully dependent on and masks the daily routines of individual postal workers making observations about specific bundles of mail left to languish. "
hazhar, <NAME>,"✊s, 🌹s, and major 🔑s: Emoji data science & journalism",http://www.hamdanazhar.com/uploads/2/5/5/1/25511129/hamdanazhar-yp2014-1.jpg,hamdanazhar,Emojis have been called a new type of language. According to statistics cited by Ad Week as much as 92% of the online population uses emojis. Twitter reports that since 2014 alone over 110 billion emojis have been tweeted. Yet despite the profusion of emojis in digital life little research has been done that leverages emojis to understand popular sentiment around current issues. I’ll talk about how emoji data science (a largely unexplored field) might be a powerful new methodology for both the computational social sciences as well as fast data journalism. I'll share preliminary research based on an analysis of millions of tweets that explores the relevance of emoji analytics to fields ranging from pop culture (i.e. the Kanye West vs. Taylor Swift dispute) to politics (the US presidential elections as well as Brexit) to gender norms to the Olympics and more.
cbrosseau,<NAME>,How do I get that? Strategies for requesting data and negotiating for it,https://www.google.com/imgres?imgurl=https%3A%2F%2Flh6.googleusercontent.com%2F-BTw4_3ARJ4g%2FAAAAAAAAAAI%2FAAAAAAAAAEY%2F2Tvf51mEzNs%2Fphoto.jpg&imgrefurl=https%3A%2F%2Fplus.google.com%2F104823147284278372388%2Fabout&docid=ba61zjXO5qAKDM&tbnid=LnAxBtI8GxPXWM%3A&vet=1&w=512&h=512&itg=1&bih=1240&biw=1943&q=carli%20brosseau&ved=0ahUKEwj3noiG8I3SAhUO24MKHQEsCRUQMwghKAUwBQ&iact=mrc&uact=8#h=512&imgrc=LnAxBtI8GxPXWM:&vet=1&w=512,carlibrosseau,,"Governments of all sizes create and maintain data sets that they don’t put on the internet. But all that data is public, and it could be yours to analyze and visualize – well, most of it. We’ll talk about how to identify what public data sets exist and how to access them using state and federal public records laws. We’ll also survey common roadblocks and how to navigate around them."
dleem, <NAME>,"Smelly London: visualising historical smells through text-mining, geo-referencing and mapping.",,1208DL,Smelly London project (www.londonsmells.co.uk) aims to bring together historical data with modern digitisation and visualisation to give us a unique revealing and visceral glimpse into a London of the past and what it tells us about London today. Text-mining and analysing the MOH reports tells the intimate narratives of the everyday experiences of 19th and 20th century Londoners through the 'smellscape'. The Smelly London project provides a great opportunity to demonstrate how new knowledge and insights have risen from the use of powerful digital applications. All outputs generated from the project will be open access and open source. Our data is available in a public repository GitHub (https://github.com/Smelly-London) and other platforms such as Layers of London (http://layersoflondon.blogs.sas.ac.uk/about-the-project/) and Smelly Maps: Good City Life (http://goodcitylife.org/index.html).,
wlyon,<NAME>,Analyzing The Trumpworld Graph: Applying Network Analysis to Public Data,https://pbs.twimg.com/profile_images/502931400882794496/Dy9gq9V2.jpeg,lyonwj,,"A few weeks ago BuzzFeed released a public dataset of people and organizations connected to <NAME> and members of his administration. As they say in their blog post announcing the data:

    No American president has taken office with a giant network of businesses,    investments, and corporate connections like that amassed by <NAME>. His family and advisers have touched a staggering number of ventures, from a hotel in Azerbaijan to a poker company in Las Vegas.

In this talk we will show how to model this data as a graph, write Cypher queries to find interesting connections and visualize the results. In addition, we will show how to add public data from USASpending.gov on government contracts and campaign finance from the FEC, allowing us to answer questions like:

* How are members of the Trump administration connected to vendors of government contracts?

* Who are the most influential people in the network and how are they connected to Trump?"
mpclaypotch,Matt 'Potch' Claypotch,A Better 'Edit' Button for GitHub-Hosted Data,http://bits.potch.me/headshot.jpg,potch,,
eschles,<NAME>,Changing the world with data. With combatting human trafficking as an example.,https://avatars2.githubusercontent.com/u/928957?v=3&s=460,EricSchles,,"My talk is about how to set up national networks, support national networks and international networks that work on a specific issue.  I'll pick specific examples from my work in the anti trafficking space and how I worked nationally and internationally.  I'll choose specific tools I built and the problems they solved with in the anti trafficking space and talk about how these tools helped to further long term objectives within the space.  "
npenfold,<NAME>,Innovation to facilitate data sharing in the life sciences and biomedicine,https://drive.google.com/open?id=0B_a2JekZMrW8LW5TdUdhMjZvZGs,eLifeInnovation,,"The drive for more open data in the life sciences and biomedical research is supported by research funders and policymakers as a means to address the ‘reproducibility crisis’, derive more value from research projects, and avoid duplication of effort in research. Journal and funder policies encouraging the deposition of datasets in repositories, where appropriate, help to enforce data-sharing practises but do not ensure that the data itself is of sufficient quality. The physics, computer science, and computational biology communities have developed tools that facilitate the publication of reproducible computational analysis, but these are geared at users with a high degree of digital literacy. For biologists who rely on basic spreadsheets and desktop statistical analysis software, the barrier to entry to these tools remains high. Further, the effort required to translate a myriad of data objects into a well-curated dataset for deposition is not directly paired to the benefits that may come from sharing such data.

At eLife, we are interested in tools and technologies that will help and incentivise life science and biomedical researchers to share their data more openly. We are actively seeking and showcasing open-source tools, such as Binder, that augment the static research article with richer, dynamic and reproducible artefacts, and cater for biologists in doing so. We are also interested in processes that reduce the work required to share datasets, improve their discovery, facilitate their reuse, and pair the effort of sharing with meaningful rewards, such as credit, citation, and reputation gain. In this talk, I will discuss specific examples where data sharing has gained traction in the biology community, and outline key pain points remaining. I invite the community to join me in a conversation about how research technologists and publishers can best contribute towards open data in the life sciences and biomedicine."
tallen,<NAME>,Mantle: A Proposal for Automated Metadata and Linked Data for Scientific Users,https://www.dropbox.com/s/sf6cql6410hip7o/F70CADD5-EAF3-443B-A0E6-278337D8B9EA.JPG?raw=1,toph,,"Scientists work with increasing volumes of data, and managing that data is a growing part of their day-to-day workload. One set of time-consuming tasks is data integration; that is, bridging gaps of incompatibility to merge datasets from different sources. Even datasets in the same format may refer to the same conceptual entity with different language. The way to solve this problem is to connect datasets via their metadata. Much progress has been made in metadata standards, practices, and formats (CSV on the Web a.k.a. WCSV, Ecological Metadata Language (EML), and JSON-Linked Data). However, much data lacks robust metadata due to the limits of manual metadata curation. We propose an automated approach to metadata discovery that aims to facilitate data discovery and linking, joining, and merging of heterogeneous datasets. We outline the challenges to this problem and our strategy for addressing them, including a set of machine-learning classifiers and heuristics to identify the correct ontology or ontologies for fields and entities in datasets (e.g., location names, species taxonomies), and link these data across data sets with different sources and formats. We discuss how this tool can fit into scientific workflows and an open data ecosystem."
cmoravec, <NAME>,Location - Trends in Time and Place,http://www.moraveclabs.com/wp-content/uploads/2017/02/profile.png,Mor3Havoc,,
msyed,<NAME>,Data & Abuse of Power,https://pbs.twimg.com/profile_images/593124707949359104/G8p4Cn0A.jpg,moizsyed,,"We need data on public institutions so that we can keep them accountable. But when that data is collected and shared by the institution itself, they are in a powerful position to control the narrative. 

What can we as data practitioners do to help identify potentially biased narratives that could exist in this type of data? Because blindly trusting a biased dataset is just as harmful as blindly trusting a biased source.

I would like to talk a little about a vetting process, and about a few important types of biases in data that you should have in mind. This list is by no means complete and is still a work in progress, but it includes some of the more major types of bias that I’ve noticed in data, and what I think you can do about them."
hparker,<NAME>,Opinionated Analysis Development,https://goo.gl/photos/C7Rxys6qGeLfs3xp8,hspter,,"Traditionally, statistical training has focused primarily on mathematical derivations and proofs of statistical tests. The process of developing the technical artifact -- that is, the paper, dashboard, or other deliverable -- is much less frequently taught, presumably because of an aversion to cookbookery or prescribing specific software choices. In this talk, I argue that it's critical to teach generalized opinions for how to go about developing an analysis in order to maximize the probability that an analysis is reproducible, accurate and collaborative. By encouraging the use of and fluency in tooling that implements these opinions, we as a community can foster the growth of processes that fail the practitioners as infrequently as possible."
bobrien,<NAME>,Data Lovers in in a Dangerous Time,https://avatars2.githubusercontent.com/u/1154390?v=3&u=3f19a50e1302649a314ce3298481e23caeb86bba&s=400,formalfiction,,"In recent months archive-a-thons aimed at saving government data have sprung up all around the country in response to a shift in politics. These ""DataRescues"" have drawn together librarians, anthropologists, developers, data scientists, scientist-scientists, all in the name of preserving a shared interest in open knowledge. Hear a first-hand account of how this group is growing to meet the challenge of classifying, monitoring, and archiving everything "".gov""."
mreeve,<NAME>,The Art and Science of Generative Nonsense,https://pbs.twimg.com/profile_images/807784644682752001/TjYal1Zv.jpg,tripofmice,,"Teaching machines to understand and communicate in human language is one of the great challenges of computer science. But what about the reverse -- what role can computers play in taking perfectly reasonable text and rendering it unreasonable? Consider <NAME>'s Jabberwocky, asemic writing, or invented languages. The tools for discerning semantic meaning in text can be used to dismantle it, and the corpora of written text leveraged towards generative confusion. This talk explores basic computational linguistics concepts, how to use them to make things make less sense, and the simple joys, subversive charms, and conceptual merits of nonsense."
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