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published: 2021-05-10
 
This dataset contains data used in publication "Institutional Data Repository Development, a Moving Target" submitted to Code4Lib Journal. It is a tabular data file describing attributes of data files in datasets published in Illinois Data Bank 2016-04-01 to 2021-04-01.
published: 2021-05-01
 
This is the first version of the dataset. This dataset contains anonymize data collected during the experiments mentioned in the publication: “I can show what I really like.”: Eliciting Preferences via Quadratic Voting that would appear in April 2021. Once the publication link is public, we would provide an update here. These data were collected through our open-source online systems that are available at (experiment1)[https://github.com/a2975667/QV-app] and (experiment 2)[https://github.com/a2975667/QV-buyback] There are two folders in this dataset. The first folder (exp1_data) contains data collected during experiment 1; the second folder (exp2_data) contains data collected during experiment 2.
keywords: Quadratic Voting; Likert scale; Empirical studies; Collective decision-making
published: 2021-04-15
 
To generate the bibliographic and survey data to support a data reuse study conducted by several Library faculty and accepted for publication in the Journal of Academic Librarianship, the project team utilized a series of web-based online scripts that employed several different endpoints from the Scopus API. The related dataset: "Data for: An Examination of Data Reuse Practices within Highly Cited Articles of Faculty at a Research University" contains survey design and results. <br /> 1) <b>getScopus_API_process_dmp_IDB.asp</b>: used the search API query the Scopus database API for papers by UIUC authors published in 2015 -- limited to one of 9 pre-defined Scopus subject areas -- and retrieve metadata results sorted highest to lowest by the number of times the retrieved articles were cited. The URL for the basic searches took the following form: https://api.elsevier.com/content/search/scopus?query=(AFFIL%28(urbana%20OR%20champaign) AND univ*%29) OR (AF-ID(60000745) OR AF-ID(60005290))&apikey=xxxxxx&start=" & nstart & "&count=25&date=2015&view=COMPLETE&sort=citedby-count&subj=PHYS<br /> Here, the variable nstart was incremented by 25 each iteration and 25 records were retrieved in each pass. The subject area was renamed (e.g. from PHYS to COMP for computer science) in each of the 9 runs. This script does not use the Scopus API cursor but downloads 25 records at a time for up to 28 times -- or 675 maximum bibliographic records. The project team felt that looking at the most 675 cited articles from UIUC faculty in each of the 9 subject areas was sufficient to gather a robust, representative sample of articles from 2015. These downloaded records were stored in a temporary table that was renamed for each of the 9 subject areas. <br /> 2) <b>get_citing_from_surveys_IDB.asp</b>: takes a Scopus article ID (eid) from the 49 UIUC author returned surveys and retrieves short citing article references, 200 at a time, into a temporary composite table. These citing records contain only one author, no author affiliations, and no author email addresses. This script uses the Scopus API cursor=* feature and is able to download all the citing references of an article 200 records at a time. <br /> 3) <b>put_in_all_authors_affil_IDB.asp</b>: adds important data to the short citing records. The script adds all co-authors and their affiliations, the corresponding author, and author email addresses. <br /> 4) <b>process_for_final_IDB.asp</b>: creates a relational database table with author, title, and source journal information for each of the citing articles that can be copied as an Excel file for processing by the Qualtrics survey software. This was initially 4,626 citing articles over the 49 UIUC authored articles, but was reduced to 2,041 entries after checking for available email addresses and eliminating duplicates.
keywords: Scopus API; Citing Records; Most Cited Articles
published: 2021-04-06
 
This dataset includes three files. Descriptions of the files are given as follows: <b>FILENAME: PubMed_retracted_publication_full_v3.tsv</b> - Bibliographic data of retracted papers indexed in PubMed (retrieved on August 20, 2020, searched with the query "retracted publication" [PT] ). - Except for the information in the "cited_by" column, all the data is from PubMed. ROW EXPLANATIONS - Each row is a retracted paper. There are 7,813 retracted papers. COLUMN HEADER EXPLANATIONS 1) PMID - PubMed ID 2) Title - Paper title 3) Authors - Author names 4) Citation - Bibliographic information of the paper 5) First Author - First author's name 6) Journal/Book - Publication name 7) Publication Year 8) Create Date - The date the record was added to the PubMed database 9) PMCID - PubMed Central ID (if applicable, otherwise blank) 10) NIHMS ID - NIH Manuscript Submission ID (if applicable, otherwise blank) 11) DOI - Digital object identifier (if applicable, otherwise blank) 12) retracted_in - Information of retraction notice (given by PubMed) 13) retracted_yr - Retraction year identified from "retracted_in" (if applicable, otherwise blank) 14) cited_by - PMIDs of the citing papers. (if applicable, otherwise blank) Data collected from iCite. 15) retraction_notice_pmid - PMID of the retraction notice (if applicable, otherwise blank) <b>FILENAME: PubMed_retracted_publication_CitCntxt_withYR_v3.tsv</b> - This file contains citation contexts (i.e., citing sentences) where the retracted papers were cited. The citation contexts were identified from the XML version of PubMed Central open access (PMCOA) articles. - This is part of the data from: Hsiao, T.-K., & Torvik, V. I. (manuscript in preparation). Citation contexts identified from PubMed Central open access articles: A resource for text mining and citation analysis. ROW EXPLANATIONS - Each row is a citation context associated with one retracted paper that's cited. - In the manuscript, we count each citation context once, even if it cites multiple retracted papers. COLUMN HEADER EXPLANATIONS 1) pmcid - PubMed Central ID of the citing paper 2) pmid - PubMed ID of the citing paper 3) year - Publication year of the citing paper 4) location - Location of the citation context (abstract = abstract, body = main text, back = supporting material, tbl_fig_caption = table/figure captions) 5) IMRaD - IMRaD section of the citation context (I = Introduction, M = Methods, R = Results, D = Discussions/Conclusion, NoIMRaD = not identified) 6) sentence_id - The ID of the citation context in a given location. For location information, please see column 4. The first sentence in the location gets the ID 1, and subsequent sentences are numbered consecutively. 7) total_sentences - Total number of sentences in a given location 8) intxt_id - Identifier of a cited paper. Here, a cited paper is the retracted paper. 9) intxt_pmid - PubMed ID of a cited paper. Here, a cited paper is the retracted paper. 10) citation - The citation context 11) progression - Position of a citation context by centile within the citing paper. 12) retracted_yr - Retraction year of the retracted paper 13) post_retraction - 0 = not post-retraction citation; 1 = post-retraction citation. A post-retraction citation is a citation made after the calendar year of retraction. <b>FILENAME: 613_knowingly_post_retraction_cit.tsv</b> - The 613 post-retraction citation contexts that we determined knowingly cited the 7,813 retracted papers in "PubMed_retracted_publication_full_v3.tsv". ROW EXPLANATIONS - Each row is a citation context. COLUMN HEADER EXPLANATIONS 1) pmcid - PubMed Central ID of the citing paper 2) pmid - PubMed ID of the citing paper 3) pub_type - Publication type collected from the metadata in the PMCOA XML files. 4) pub_type2 - Specific article types. Please see the manuscript for explanations. 5) year - Publication year of the citing paper 6) location - Location of the citation context (abstract = abstract, body = main text, back = supporting material, tbl_fig_caption = table/figure captions) 7) intxt_id - Identifier of a cited paper. Here, a cited paper is the retracted paper. 8) intxt_pmid - PubMed ID of a cited paper. Here, a cited paper is the retracted paper. 9) citation - The citation context 10) retracted_yr - Retraction year of the retracted paper 11) cit_purpose - Purpose of citing the retracted paper. This is from human annotations. Please see the manuscript for further information about annotation. 12) longer_context - A extended version of the citation context. (if applicable, otherwise blank) Manually pulled from the full-texts in the process of annotation. <b>FILENAME: Annotation manual.pdf</b> - The manual for annotating the citation purposes in column 11) of the 613_knowingly_post_retraction_cit.tsv.
keywords: citation context; in-text citation; citation to retracted papers; retraction
published: 2020-12-16
 
Terrorism is among the most pressing challenges to democratic governance around the world. The Responsible Terrorism Coverage (or ResTeCo) project aims to address a fundamental dilemma facing 21st century societies: how to give citizens the information they need without giving terrorists the kind of attention they want. The ResTeCo hopes to inform best practices by using extreme-scale text analytic methods to extract information from more than 70 years of terrorism-related media coverage from around the world and across 5 languages. Our goal is to expand the available data on media responses to terrorism and enable the development of empirically-validated models for socially responsible, effective news organizations. This particular dataset contains information extracted from terrorism-related stories in the Summary of World Broadcasts published between 1979 and 2019. It includes variables that measure the relative share of terrorism-related topics, the valence and intensity of emotional language, as well as the people, places, and organizations mentioned. This dataset contains 3 files: 1. "ResTeCo Project SWB Dataset Variable Descriptions.pdf" A detailed codebook containing a summary of the Responsible Terrorism Coverage (ResTeCo) Project BBC Summary of World Broadcasts (SWB) Dataset and descriptions of all variables. 2. "resteco-swb.csv" This file contains the data extracted from terrorism-related media coverage in the BBC Summary of World Broadcasts (SWB) between 1979 and 2019. It includes variables that measure the relative share of topics, sentiment, and emotion present in this coverage. There are also variables that contain metadata and list the people, places, and organizations mentioned in these articles. There are 53 variables and 438,373 observations. The variable "id" uniquely identifies each observation. Each observation represents a single news article. Please note that care should be taken when using "resteco-swb.csv". The file may not be suitable to use in a spreadsheet program like Excel as some of the values get to be quite large. Excel cannot handle some of these large values, which may cause the data to appear corrupted within the software. It is encouraged that a user of this data use a statistical package such as Stata, R, or Python to ensure the structure and quality of the data remains preserved. 3. "README.md" This file contains useful information for the user about the dataset. It is a text file written in markdown language Citation Guidelines 1) To cite this codebook please use the following citation: Althaus, Scott, Joseph Bajjalieh, Marc Jungblut, Dan Shalmon, Subhankar Ghosh, and Pradnyesh Joshi. 2020. Responsible Terrorism Coverage (ResTeCo) Project BBC Summary of World Broadcasts (SWB) Dataset Variable Descriptions. Responsible Terrorism Coverage (ResTeCo) Project BBC Summary of World Broadcasts (SWB) Dataset. Cline Center for Advanced Social Research. December 16. University of Illinois Urbana-Champaign. doi: https://doi.org/10.13012/B2IDB-2128492_V1 2) To cite the data please use the following citation: Althaus, Scott, Joseph Bajjalieh, Marc Jungblut, Dan Shalmon, Subhankar Ghosh, and Pradnyesh Joshi. 2020. Responsible Terrorism Coverage (ResTeCo) Project Summary of World Broadcasts (SWB) Dataset. Cline Center for Advanced Social Research. December 16. University of Illinois Urbana-Champaign. doi: https://doi.org/10.13012/B2IDB-2128492_V1
keywords: Terrorism, Text Analytics, News Coverage, Topic Modeling, Sentiment Analysis
published: 2020-12-16
 
The Cline Center Global News Index is a searchable database of textual features extracted from millions of news stories, specifically designed to provide comprehensive coverage of events around the world. In addition to searching documents for keywords, users can query metadata and features such as named entities extracted using Natural Language Processing (NLP) methods and variables that measure sentiment and emotional valence. Archer is a web application purpose-built by the Cline Center to enable researchers to access data from the Global News Index. Archer provides a user-friendly interface for querying the Global News Index (with the back-end indexing still handled by Solr). By default, queries are built using icons and drop-down menus. More technically-savvy users can use Lucene/Solr query syntax via a ‘raw query’ option. Archer allows users to save and iterate on their queries, and to visualize faceted query results, which can be helpful for users as they refine their queries. <b>Additional Resources:</b> - Access to Archer and the Global News Index is limited to account-holders. If you are interested in signing up for an account, you can fill out the <a href="https://docs.google.com/forms/d/1Vx_PpkIV4U1mt2FrPlfmdTC19VSidWQ8OC3D0lLNnvs/edit"><b>Archer User Information Form</b></a>. - Current users who would like to provide feedback, such as reporting a bug or requesting a feature, can fill out the <a href="https://forms.gle/6eA2yJUGFMtj5swY7"><b>Archer User Feedback Form</b></a>. - The Cline Center sends out periodic email newsletters to the Archer Users Group. Please fill out this form to <a href="https://groups.webservices.illinois.edu/subscribe/123172"><b>subscribe to Archer Users Group</b></a>. <b>Citation Guidelines:</b> 1) To cite the GNI codebook (or any other documentation associated with the Global News Index and Archer) please use the following citation: Cline Center for Advanced Social Research. 2020. Global News Index and Extracted Features Repository [codebook], v1.0.1. Champaign, IL: University of Illinois. Dec. 16. doi:10.13012/B2IDB-5649852_V2 2) To cite data from the Global News Index (accessed via Archer or otherwise) please use the following citation (filling in the correct date of access): Cline Center for Advanced Social Research. 2020. Global News Index and Extracted Features Repository [database], v1.0.1. Champaign, IL: University of Illinois. Dec. 16. Accessed Month, DD, YYYY. doi:10.13012/B2IDB-5649852_V2
keywords: Cline Center; Cline Center for Advanced Social Research; political; social; political science; Global News Index; Archer; news; mass communication; journalism;
published: 2021-03-17
 
This dataset was developed as part of a study that assessed data reuse. Through bibliometric analysis, corresponding authors of highly cited papers published in 2015 at the University of Illinois at Urbana-Champaign in nine STEM disciplines were identified and then surveyed to determine if data were generated for their article and their knowledge of reuse by other researchers. Second, the corresponding authors who cited those 2015 articles were identified and surveyed to ascertain whether they reused data from the original article and how that data was obtained. The project goal was to better understand data reuse in practice and to explore if research data from an initial publication was reused in subsequent publications.
keywords: data reuse; data sharing; data management; data services; Scopus API
published: 2021-03-14
 
This dataset contains all the code, notebooks, datasets used in the study conducted to measure the spatial accessibility of COVID-19 healthcare resources with a particular focus on Illinois, USA. Specifically, the dataset measures spatial access for people to hospitals and ICU beds in Illinois. The spatial accessibility is measured by the use of an enhanced two-step floating catchment area (E2FCA) method (Luo & Qi, 2009), which is an outcome of interactions between demands (i.e, # of potential patients; people) and supply (i.e., # of beds or physicians). The result is a map of spatial accessibility to hospital beds. It identifies which regions need more healthcare resources, such as the number of ICU beds and ventilators. This notebook serves as a guideline of which areas need more beds in the fight against COVID-19. ## What's Inside A quick explanation of the components of the zip file * `COVID-19Acc.ipynb` is a notebook for calculating spatial accessibility and `COVID-19Acc.html` is an export of the notebook as HTML. * `Data` contains all of the data necessary for calculations: &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; * `Chicago_Network.graphml`/`Illinois_Network.graphml` are GraphML files of the OSMNX street networks for Chicago and Illinois respectively. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; * `GridFile/` has hexagonal gridfiles for Chicago and Illinois &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; * `HospitalData/` has shapefiles for the hospitals in Chicago and Illinois &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; * `IL_zip_covid19/COVIDZip.json` has JSON file which contains COVID cases by zip code from IDPH &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; * `PopData/` contains population data for Chicago and Illinois by census tract and zip code. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; * `Result/` is where we write out the results of the spatial accessibility measures &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; * `SVI/`contains data about the Social Vulnerability Index (SVI) * `img/` contains some images and HTML maps of the hospitals (the notebook generates the maps) * `README.md` is the document you're currently reading! * `requirements.txt` is a list of Python packages necessary to use the notebook (besides Jupyter/IPython). You can install the packages with `python3 -m pip install -r requirements.txt`
keywords: COVID-19; spatial accessibility; CyberGISX
published: 2021-02-23
 
Coups d'état are important events in the life of a country. They constitute an important subset of irregular transfers of political power that can have significant and enduring consequences for national well-being. There are only a limited number of datasets available to study these events (Powell and Thyne 2011, Marshall and Marshall 2019). Seeking to facilitate research on post-WWII coups by compiling a more comprehensive list and categorization of these events, the Cline Center for Advanced Social Research (previously the Cline Center for Democracy) initiated the Coup D'état Project (CDP) as part of its Societal Infrastructures and Development (SID) project. More specifically, this dataset identifies the outcomes of coup events (i.e. realized or successful coups, unrealized coup attempts, or thwarted conspiracies) the type of actor(s) who initiated the coup (i.e. military, rebels, etc.), as well as the fate of the deposed leader. This is version 2.0.0 of this dataset. The first version, <a href="https://clinecenter.illinois.edu/project/research-themes/democracy-and-development/coup-detat-project-cdp ">v.1.0.0</a>, was released in 2013. Since then, the Cline Center has taken several steps to improve on the previously-released data. These changes include: <ol> <li>Filling in missing event data values</li> <li>Removing events with no identifiable dates</li> <li>Reconciling event dates from sources that have conflicting information</li> <li>Removing events with insufficient sourcing (each event now has at least two sources)</li> <li>Removing events that were inaccurately coded and did not meet our definition of a coup event</li> <li>Extending the time period covered from 1945-2005 to 1945-2019</li> <li>Removing certain variables that fell below the threshold of inter-coder reliability required by the project</li> <li>The spreadsheet ‘CoupInventory.xls’ was removed because of inadequate attribution and citation in the event summaries</li></ol> <b>Items in this Dataset</b> 1. <i>CDP v.2.0.2 Codebook.pdf</i> <ul><li>This 14-page document provides a description of the Cline Center Coup D’état Project Dataset. The first section of this codebook provides a succinct definition of a coup d’état used by the CDP and an overview of the categories used to differentiate the wide array of events that meet the CDP definition. It also defines coup outcomes. The second section describes the methodology used to produce the data. <i>Created November 2020. Revised February 2021 to add some additional information about how the Cline Center edited some values in the COW country codes."</i> </li></ul> 2. <i>Coup_Data_v2.0.0.csv</i> <ul><li>This CSV (Comma Separated Values) file contains all of the coup event data from the Cline Center Coup D’etat Project. It contains 29 variables and 943 observations. <i>Created November 2020</i></li></ul> 3. <i>Source Document v2.0.0.pdf</i> <ul><li>This 305-page document provides the sources used for each of the coup events identified in this dataset. Please use the value in the coup_id variable to identify the sources used to identify each particular event. <i>Created November 2020</i> </li></ul> 4. <i>README.md</i> <ul><li>This file contains useful information for the user about the dataset. It is a text file written in mark down language. <i>Created November 2020</i> </li></ul> <br> <b> Citation Guidelines</b> 1) To cite this codebook please use the following citation: Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, and Jonathan Bonaguro. 2021. “Cline Center Coup D’état Project Dataset Codebook”. Cline Center Coup D’état Project Dataset. Cline Center for Advanced Social Research. V.2.0.2. February 23. University of Illinois Urbana-Champaign. doi: <a href="https://doi.org/10.13012/B2IDB-9651987_V2">10.13012/B2IDB-9651987_V3</a> 2) To cite the data please use the following citation: Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, and Jonathan Bonaguro. 2020. Cline Center Coup D’état Project Dataset. Cline Center for Advanced Social Research. V.2.0.0. November 16. University of Illinois Urbana-Champaign. doi: <a href="https://doi.org/10.13012/B2IDB-9651987_V2">10.13012/B2IDB-9651987_V3</a>
keywords: Coup d'état; event data; Cline Center; Cline Center for Advanced Social Research; political science
published: 2020-12-16
 
Terrorism is among the most pressing challenges to democratic governance around the world. The Responsible Terrorism Coverage (or ResTeCo) project aims to address a fundamental dilemma facing 21st century societies: how to give citizens the information they need without giving terrorists the kind of attention they want. The ResTeCo hopes to inform best practices by using extreme-scale text analytic methods to extract information from more than 70 years of terrorism-related media coverage from around the world and across 5 languages. Our goal is to expand the available data on media responses to terrorism and enable the development of empirically-validated models for socially responsible, effective news organizations. This particular dataset contains information extracted from terrorism-related stories in the Foreign Broadcast Information Service (FBIS) published between 1995 and 2013. It includes variables that measure the relative share of terrorism-related topics, the valence and intensity of emotional language, as well as the people, places, and organizations mentioned. This dataset contains 3 files: 1. "ResTeCo Project FBIS Dataset Variable Descriptions.pdf" A detailed codebook containing a summary of the Responsible Terrorism Coverage (ResTeCo) Project Foreign Broadcast Information Service (FBIS) Dataset and descriptions of all variables. 2. "resteco-fbis.csv" This file contains the data extracted from terrorism-related media coverage in the Foreign Broadcast Information Service (FBIS) between 1995 and 2013. It includes variables that measure the relative share of topics, sentiment, and emotion present in this coverage. There are also variables that contain metadata and list the people, places, and organizations mentioned in these articles. There are 53 variables and 750,971 observations. The variable "id" uniquely identifies each observation. Each observation represents a single news article. Please note that care should be taken when using "resteco-fbis.csv". The file may not be suitable to use in a spreadsheet program like Excel as some of the values get to be quite large. Excel cannot handle some of these large values, which may cause the data to appear corrupted within the software. It is encouraged that a user of this data use a statistical package such as Stata, R, or Python to ensure the structure and quality of the data remains preserved. 3. "README.md" This file contains useful information for the user about the dataset. It is a text file written in mark down language Citation Guidelines 1) To cite this codebook please use the following citation: Althaus, Scott, Joseph Bajjalieh, Marc Jungblut, Dan Shalmon, Subhankar Ghosh, and Pradnyesh Joshi. 2020. Responsible Terrorism Coverage (ResTeCo) Project Foreign Broadcast Information Service (FBIS) Dataset Variable Descriptions. Responsible Terrorism Coverage (ResTeCo) Project Foreign Broadcast Information Service (FBIS) Dataset. Cline Center for Advanced Social Research. December 16. University of Illinois Urbana-Champaign. doi: https://doi.org/10.13012/B2IDB-6360821_V1 2) To cite the data please use the following citation: Althaus, Scott, Joseph Bajjalieh, Marc Jungblut, Dan Shalmon, Subhankar Ghosh, and Pradnyesh Joshi. 2020. Responsible Terrorism Coverage (ResTeCo) Project Foreign Broadcast Information Service (FBIS) Dataset. Cline Center for Advanced Social Research. December 16. University of Illinois Urbana-Champaign. doi: https://doi.org/10.13012/B2IDB-6360821_V1
keywords: Terrorism, Text Analytics, News Coverage, Topic Modeling, Sentiment Analysis
published: 2020-04-22
 
Data on Croatian restaurant allergen disclosures on restaurant websites, on-line menus and social media comments
keywords: restaurant; allergen; disclosure; tourism
published: 2020-10-11
 
This dataset contains the publication record of 6429 computer science researchers collected from the Microsoft Academic dataset provided through their Knowledge Service API (http://bit.ly/microsoft-data).
published: 2020-09-27
 
This dataset contains R codes used to produce the figures submitted in the manuscript titled "Understanding the multifaceted geospatial software ecosystem: a survey approach". The raw survey data used to populate these charts cannot be shared due to the survey consent agreement.
keywords: R; figures; geospatial software
published: 2020-09-02
 
Citation context annotation. This dataset is a second version (V2) and part of the supplemental data for Jodi Schneider, Di Ye, Alison Hill, and Ashley Whitehorn. (2020) "Continued post-retraction citation of a fraudulent clinical trial report, eleven years after it was retracted for falsifying data". Scientometrics. In press, DOI: 10.1007/s11192-020-03631-1 Publications were selected by examining all citations to the retracted paper Matsuyama 2005, and selecting the 35 citing papers, published 2010 to 2019, which do not mention the retraction, but which mention the methods or results of the retracted paper (called "specific" in Ye, Di; Hill, Alison; Whitehorn (Fulton), Ashley; Schneider, Jodi (2020): Citation context annotation for new and newly found citations (2006-2019) to retracted paper Matsuyama 2005. University of Illinois at Urbana-Champaign. <a href="https://doi.org/10.13012/B2IDB-8150563_V1">https://doi.org/10.13012/B2IDB-8150563_V1</a> ). The annotated citations are second-generation citations to the retracted paper Matsuyama 2005 (RETRACTED: Matsuyama W, Mitsuyama H, Watanabe M, Oonakahara KI, Higashimoto I, Osame M, Arimura K. Effects of omega-3 polyunsaturated fatty acids on inflammatory markers in COPD. Chest. 2005 Dec 1;128(6):3817-27.), retracted in 2008 (Retraction in: Chest (2008) 134:4 (893) <a href="https://doi.org/10.1016/S0012-3692(08)60339-6">https://doi.org/10.1016/S0012-3692(08)60339-6<a/> ). <b>OVERALL DATA for VERSION 2 (V2)</b> FILES/FILE FORMATS Same data in two formats: 2010-2019 SG to specific not mentioned FG.csv - Unicode CSV (preservation format only) - same as in V1 2010-2019 SG to specific not mentioned FG.xlsx - Excel workbook (preferred format) - same as in V1 Additional files in V2: 2G-possible-misinformation-analyzed.csv - Unicode CSV (preservation format only) 2G-possible-misinformation-analyzed.xlsx - Excel workbook (preferred format) <b>ABBREVIATIONS: </b> 2G - Refers to the second-generation of Matsuyama FG - Refers to the direct citation of Matsuyama (the one the second-generation item cites) <b>COLUMN HEADER EXPLANATIONS </b> File name: 2G-possible-misinformation-analyzed. Other column headers in this file have same meaning as explained in V1. The following are additional header explanations: Quote Number - The order of the quote (citation context citing the first generation article given in "FG in bibliography") in the second generation article (given in "2G article") Quote - The text of the quote (citation context citing the first generation article given in "FG in bibliography") in the second generation article (given in "2G article") Translated Quote - English translation of "Quote", automatically translation from Google Scholar Seriousness/Risk - Our assessment of the risk of misinformation and its seriousness 2G topic - Our assessment of the topic of the cited article (the second generation article given in "2G article") 2G section - The section of the citing article (the second generation article given in "2G article") in which the cited article(the first generation article given in "FG in bibliography") was found FG in bib type - The type of article (e.g., review article), referring to the cited article (the first generation article given in "FG in bibliography") FG in bib topic - Our assessment of the topic of the cited article (the first generation article given in "FG in bibliography") FG in bib section - The section of the cited article (the first generation article given in "FG in bibliography") in which the Matsuyama retracted paper was cited
keywords: citation context annotation; retraction; diffusion of retraction; second-generation citation context analysis
published: 2020-08-21
 
# WikiCSSH If you are using WikiCSSH please cite the following: > Han, Kanyao; Yang, Pingjing; Mishra, Shubhanshu; Diesner, Jana. 2020. “WikiCSSH: Extracting Computer Science Subject Headings from Wikipedia.” In Workshop on Scientific Knowledge Graphs (SKG 2020). https://skg.kmi.open.ac.uk/SKG2020/papers/HAN_et_al_SKG_2020.pdf > Han, Kanyao; Yang, Pingjing; Mishra, Shubhanshu; Diesner, Jana. 2020. "WikiCSSH - Computer Science Subject Headings from Wikipedia". University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-0424970_V1 Download the WikiCSSH files from: https://doi.org/10.13012/B2IDB-0424970_V1 More details about the WikiCSSH project can be found at: https://github.com/uiuc-ischool-scanr/WikiCSSH This folder contains the following files: WikiCSSH_categories.csv - Categories in WikiCSSH WikiCSSH_category_links.csv - Links between categories in WikiCSSH Wikicssh_core_categories.csv - Core categories as mentioned in the paper WikiCSSH_category_links_all.csv - Links between categories in WikiCSSH (includes a dummy category called <ROOT> which is parent of isolates and top level categories) WikiCSSH_category2page.csv - Links between Wikipedia pages and Wikipedia Categories in WikiCSSH WikiCSSH_page2redirect.csv - Links between Wikipedia pages and Wikipedia page redirects in WikiCSSH This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit <a href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</a> or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
keywords: wikipedia; computer science;
published: 2020-08-18
 
These data and code enable replication of the findings and robustness checks in "No buzz for bees: Media coverage of pollinator decline," published in Proceedings of the National Academy of Sciences of the United States of America (2020)". In this paper, we find that although widespread declines in insect biomass and diversity are increasing concern within the scientific community, it remains unclear whether attention to pollinator declines has also increased within information sources serving the general public. Examining patterns of journalistic attention to the pollinator population crisis can also inform efforts to raise awareness about the importance of declines of insect species providing ecosystem services beyond pollination. We used the Global News Index developed by the Cline Center for Advanced Social Research at the University of Illinois at Urbana-Champaign to track news attention to pollinator topics in nearly 25 million news items published by two American national newspapers and four international wire services over the past four decades. We provide a link to documentation of the Global News Index in the "relationships with articles, code, o. We found vanishingly low levels of attention to pollinator population topics relative to coverage of climate change, which we use as a comparison topic. In the most recent subset of ~10 million stories published from 2007 to 2019, 1.39% (137,086 stories) refer to climate change/global warming, while only 0.02% (1,780) refer to pollinator populations in all contexts and just 0.007% (679) refer to pollinator declines. Substantial increases in news attention were detectable only in U.S. national newspapers. We also find that while climate change stories appear primarily in newspaper “front sections”, pollinator population stories remain largely marginalized in “science” and “back section” reports. At the same time, news reports about pollinator populations increasingly link the issue to climate change, which might ultimately help raise public awareness to effect needed policy changes.
keywords: News Coverage; Text Analytics; Insects; Pollinator; Cline Center; Cline Center for Advanced Social Research; political; social; political science; Global News Index; Archer; news; mass communication; journalism
published: 2020-08-10
 
These are text files downloaded from the Web of Science for the bibliographic analyses found in Zinnen et al. (2020) in Applied Vegetation Science. They represent the papers and reference lists from six expert-based indicator systems: Floristic Quality Assessment, hemeroby, naturalness indicator values (& social behaviors), Ellenberg indicator values, grassland utilization values, and urbanity indicator values. To examine data, download VOSviewer and see instructrions from van Eck & Waltman (2019) for how to upload data. Although we used bibliographic coupling, there are a number of other interesting bibliographic analyses you can use with these data (e.g., visualizing citations between journals from this set of documents). Note: There are two caveats to note about these data and Supplements 1 & 2 associated with our paper. First, there are some overlapping papers in these text files (i.e., raw data). When added individually, the papers sum to more than the numbers we give. However, when combined VOSviewer recognizes these as repeats, and matches the numbers we list in S1 and the manuscript. Second, we labelled the downloaded papers in S2 with their respective systems. In some cases, the labels do not completely match our counts listed in S1 and raw data. This is because some of these papers use another system, but were not captured in our systematic literature search (e.g., a paper may have used hemeroby, but was not picked up by WoS, so this paper is not listed as one of the 52 hemeroby papers).
keywords: Web of Science; bibliographic analyses; vegetation; VOSviewer
published: 2020-07-16
 
Dataset to be for SocialMediaIE tutorial
keywords: social media; deep learning; natural language processing
published: 2020-02-12
 
This dataset contains the results of a three month audit of housing advertisements. It accompanies the 2020 ICWSM paper "Auditing Race and Gender Discrimination in Online Housing Markets". It covers data collected between Dec 7, 2018 and March 19, 2019. There are two json files in the dataset: The first contains a list of json objects representing advertisements separated by newlines. Each object includes the date and time it was collected, the image and title (if collected) of the ad, the page on which it was displayed, and the training treatment it received. The second file is a list of json objects representing a visit to a housing lister separated by newlines. Each object contains the url, training treatment applied, the location searched, and the metadata of the top sites scraped. This metadata includes location, price, and number of rooms. The dataset also includes the raw images of ads collected in order to code them by interest and targeting. These were captured by selenium and named using a perceptive hash to de-duplicate images.
keywords: algorithmic audit; advertisement audit;
published: 2020-06-19
 
This dataset include data pulled from the World Bank 2009, the World Values Survey wave 6, Transparency International from 2009. The data were used to measure perceptions of expertise from individuals in nations that are recipients of development aid as measured by the World Bank.
keywords: World Values Survey; World Bank; expertise; development
published: 2020-06-12
 
This is a network of 14 systematic reviews on the salt controversy and their included studies. Each edge in the network represents an inclusion from one systematic review to an article. Systematic reviews were collected from Trinquart (Trinquart, L., Johns, D. M., & Galea, S. (2016). Why do we think we know what we know? A metaknowledge analysis of the salt controversy. International Journal of Epidemiology, 45(1), 251–260. https://doi.org/10.1093/ije/dyv184 ). <b>FILE FORMATS</b> 1) Article_list.csv - Unicode CSV 2) Article_attr.csv - Unicode CSV 3) inclusion_net_edges.csv - Unicode CSV 4) potential_inclusion_link.csv - Unicode CSV 5) systematic_review_inclusion_criteria.csv - Unicode CSV 6) Supplementary Reference List.pdf - PDF <b>ROW EXPLANATIONS</b> 1) Article_list.csv - Each row describes a systematic review or included article. 2) Article_attr.csv - Each row is the attributes of a systematic review/included article. 3) inclusion_net_edges.csv - Each row represents an inclusion from a systematic review to an article. 4) potential_inclusion_link.csv - Each row shows the available evidence base of a systematic review. 5) systematic_review_inclusion_criteria.csv - Each row is the inclusion criteria of a systematic review. 6) Supplementary Reference List.pdf - Each item is a bibliographic record of a systematic review/included paper. <b>COLUMN HEADER EXPLANATIONS</b> <b>1) Article_list.csv:</b> ID - Numeric ID of a paper paper assigned ID - ID of the paper from Trinquart et al. (2016) Type - Systematic review / primary study report Study Groupings - Groupings for related primary study reports from the same report, from Trinquart et al. (2016) (if applicable, otherwise blank) Title - Title of the paper year - Publication year of the paper Attitude - Scientific opinion about the salt controversy from Trinquart et al. (2016) Doi - DOIs of the paper. (if applicable, otherwise blank) Retracted (Y/N) - Whether the paper was retracted or withdrawn (Y). Blank if not retracted or withdrawn. <b>2) Article_attr.csv:</b> ID - Numeric ID of a paper year - Publication year Attitude - Scientific opinion about the salt controversy from Trinquart et al. (2016) Type - Systematic review/ primary study report <b>3) inclusion_net_edges.csv:</b> citing_ID - The numeric ID of a systematic review cited_ID - The numeric ID of the included articles <b>4) potential_inclusion_link.csv:</b> This data was translated from the Sankey diagram given in Trinquart et al. (2016) as Web Figure 4. Each row indicates a systematic review and each column indicates a primary study. In the matrix, "p" indicates that a given primary study had been published as of the search date of a given systematic review. <b>5)systematic_review_inclusion_criteria.csv:</b> ID - The numeric IDs of systematic reviews paper assigned ID - ID of the paper from Trinquart et al. (2016) attitude - Its scientific opinion about the salt controversy from Trinquart et al. (2016) No. of studies included - Number of articles included in the systematic review Study design - Study designs to include, per inclusion criteria population - Populations to include, per inclusion criteria Exposure/Intervention - Exposures/Interventions to include, per inclusion criteria outcome - Study outcomes required for inclusion, per inclusion criteria Language restriction - Report languages to include, per inclusion criteria follow-up period - Follow-up period required for inclusion, per inclusion criteria
keywords: systematic reviews; evidence synthesis; network visualization; tertiary studies
published: 2020-05-17
 
Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying Our approach is described in our paper titled: Mishra, Sudhanshu, Shivangi Prasad, and Shubhanshu Mishra. 2020. “Multilingual Joint Fine-Tuning of Transformer Models for Identifying Trolling, Aggression and Cyberbullying at TRAC 2020.” In Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying (TRAC-2020). The source code for training this model and more details can be found on our code repository: https://github.com/socialmediaie/TRAC2020 NOTE: These models are retrained for uploading here after our submission so the evaluation measures may be slightly different from the ones reported in the paper.
keywords: Social Media; Trolling; Aggression; Cyberbullying; text classification; natural language processing; deep learning; open source;
published: 2020-05-20
 
This dataset is a snapshot of the presence and structure of entrepreneurship education in U.S. four-year colleges and universities in 2015, including co-curricular activities and related infrastructure. Public, private not-for-profit and for-profit institutions are included, as are specialized four-year institutions. The dataset provides insight into the presence of entrepreneurship education both within business units and in other units of college campuses. Entrepreneurship is defined broadly, to include small business management and related career-focused options.
keywords: Entrepreneurship education; Small business education; Ewing Marion Kauffman Foundation; csv
published: 2020-05-15
 
Trained models for multi-task multi-dataset learning for sequence prediction in tweets Tasks include POS, NER, Chunking, and SuperSenseTagging Models were trained using: https://github.com/napsternxg/SocialMediaIE/blob/master/experiments/multitask_multidataset_experiment.py See https://github.com/napsternxg/SocialMediaIE for details.
keywords: twitter; deep learning; machine learning; trained models; multi-task learning; multi-dataset learning;
published: 2020-05-15
 
This data has tweets collected in paper Shubhanshu Mishra, Sneha Agarwal, Jinlong Guo, Kirstin Phelps, Johna Picco, and Jana Diesner. 2014. Enthusiasm and support: alternative sentiment classification for social movements on social media. In Proceedings of the 2014 ACM conference on Web science (WebSci '14). ACM, New York, NY, USA, 261-262. DOI: https://doi.org/10.1145/2615569.2615667 The data only contains tweet IDs and the corresponding enthusiasm and support labels by two different annotators.
keywords: Twitter; text classification; enthusiasm; support; social causes; LGBT; Cyberbullying; NFL