disease research in pathology informatics requires archiving , retrieving , organizing , sharing , and analyzing diverse pathology - related data sources . the most important data source for pathologists is the data collected by anatomic and clinical pathologists ( e.g. , blood tests , surgical pathology reports , autopsy reports , annotated images , and specialized studies ) . it is a sad irony that the data collected by pathologists is seldom available for serious scientific inquiry . many pathologists can not freely access the full set of anatomic and clinical data collected within their own departments , though de - identified pathology data is exempted from regulation by hipaa and the common rule . any pathology department could , without violating federal laws , de - identify and distribute their archived anatomic and clinical pathology datasets to the scientific community . for reasons economic , legalistic , and psychologic , no pathology departments have , as yet , distributed de - identified collections of their collected datasets to the public . as a result , there is virtually no scientific research currently being conducted on large , multi - institutional collections of pathology data . moreover , the primary data for research done on collections of single - institution data is seldom released to the public . when research is conducted on pathology data , and the data is withheld from the public , there is no way to validate the conclusions . hence , the u.s . national academy of sciences , along with the editors of many scientific journals , have established a policy requiring authors to release the primary data supporting their conclusions . because pathology departments have not released their de - identified research records to the public , record - based pathology informatics research can not be published in journals that conform to the national academy of sciences recommendations . in the rare instance where an institution has published scientific results based on global analyses of their own datasets , the raw data , upon which those results are based , scientific results have no value unless the results are backed by data that can be openly examined . where pathology departments have failed , the u.s . enormous sources of individualized but de - identified death records and cancer records are available at no cost to medical researchers . eighty - five million de - identified death records are available from the cdc ( center for disease control and prevention ) . each record contains basic demographics on the decedent ( age , race , gender , place of death ) , the cause of death , and ( if provided in the death record ) the underlying causes of death and significant additional medical conditions . each annual data file provides about 2 million byte - indexed sequential - line records . this means that there is one death record per line , and each line contains coded data indexed for ranges of bytes that are designated by a data dictionary . for example , the icd-10 code of the underlying cause of death , for deaths occurring in 1999 , are found in bytes 142 - 145 of each record . this may seem like an awkward way of organizing data , when you consider the ease with which modern specifications ( such as rdf ) can encapsulate data with metadata . nonetheless , the byte - indexed sequential files can be parsed very efficiently with just a few lines of code . though pathologists prefer autopsy data over death certificate data , autopsies are performed on a very small fraction of decedents . despite early efforts to standardize and collect autopsy data , pathologists have not succeeded in sharing their autopsy data in a national database . consequently , death certificates are the most important source of mortality data available to medical researchers . seer offers a public use dataset , containing de - identified records on about 4 million cancers that have occurred since 1973 . each seer record is a single cancer case . with about 4 million carefully curated cases , scientists can draw certain types of inferences that could not possibly be made with the data accumulated at a single medical institution . to get the seer public use data files , you must first complete a data access request available at : seer.cancer.gov/data/request.html seer sends you a username and password that you will need to access the data files . each seer record is a line on a data file , and consists of 264 alphanumeric characters . byte data includes the patient 's race , gender , age at diagnosis , primary tumor site , diagnosis , and information related to tumor size and occurrence of metastases . a data dictionary provides the byte location of the various field values contained in each record . in the past several years , i have written hundreds of blog entries explaining how open source materials ( i.e. , data , algorithms , nomenclature , metadata specifications , and software ) can be used to collect , organize , integrate , and analyze pathology - related information . the blogs are available at : julesberman.blogspot.com a blog tutorial for seer data files appeared on november 14 , 2008 . a blog tutorial on the cdc mortality files appeared on december 2 , 2008 . all of the 300 + blogs entries for the blog site can be accessed through a linked archive web page at : http://www.julesberman.info/blog_in.htm blog entries related to the cdc and seer data include topics such as data mashups ( e.g. , mapping the geographic locations of disease occurrences ) , age distributions ( e.g. , following the average age of occurrence of diseases that progress through diagnostic categories over time ) , and trends in disease incidence ( e.g. , measuring the annual incidence of genetically screened diseases ) . there are blog entries of general interest to those working with pathology datasets , including entries on de - identification methods , image manipulation and annotation , data specification methods , and various computational algorithms .