Dataset Search Results

This dataset consists of the 286 publications retrieved from Web of Science and Scopus on July 6, 2023 as citations for Willoughby et al., 2014: Patrick H. Willoughby, Matthew J. Jansma, and Thomas R. Hoye (2014). A guide to small-molecule structure assignment through computation of (¹H and ¹³C) NMR chemical shifts. Nature Protocols, 9(3), Article 3. https://doi.org/10.1038/nprot.2014.042 We added the DOIs of the citing publications into a Zotero collection. Then we exported all 286 DOIs in two formats: a .csv file (data export) and an .rtf file (bibliography). <b>Willoughby2014_286citing_publications.csv</b> is a Zotero data export of the citing publications. <b>Willoughby2014_286citing_publications.rtf</b> is a bibliography of the citing publications, using a variation of the American Psychological Association style (7th edition) with full names instead of initials. To create <b>Willoughby2014_citation_contexts.csv</b>, HZ manually extracted the paragraphs that contain a citation marker of Willoughby et al., 2014. We refer to these paragraphs as the citation contexts of Willoughby et al., 2014. Manual extraction started with 286 citing publications but excluded 2 publications that are not in English, those with DOIs 10.13220/j.cnki.jipr.2015.06.004 and 10.19540/j.cnki.cjcmm.20200604.201 The silver standard aimed to triage the citing publications of Willoughby et al., 2014 that are at risk of propagating unreliability due to a code glitch in a computational chemistry protocol introduced in Willoughby et al., 2014. The silver standard was created stepwise: First one chemistry expert (YF) manually annotated the corpus of 284 citing publications in English, using their full text and citation contexts. She manually categorized publications as either at risk of propagating unreliability or not at risk of propagating unreliability, with a rationale justifying each category. Then we selected a representative sample of citation contexts to be double annotated. To do this, MJS turned the full dataset of citation contexts (Willoughby2014_citation_contexts.csv) into word embeddings, clustered them using similarity measures using BERTopic's HDBS, and selected representative citation contexts based on the centroids of the clusters. Next the second chemistry expert (EV) annotated the 77 publications associated with the citation contexts, considering the full text as well as the citation contexts. <b>double_annotated_subset_77_before_reconciliation.csv</b> provides EV and YF's annotation before reconciliation. To create the silver standard YF, EV, and JS discussed differences and reconciled most differences. YF and EV had principled reasons for disagreeing on 9 publications; to handle these, YF updated the annotations, to create the silver standard we use for evaluation in the remainder of our JCDL 2024 paper (<b>silver_standard.csv</b>) <b>Inter_Annotator_Agreement.xlsx</b> indicates publications where the two annotators made opposite decisions and calculates the inter-annotator agreement before and after reconciliation together. <b>double_annotated_subset_77_before_reconciliation.csv</b> provides EV and YF's annotation after reconciliation, including applying the reconciliation policy.

MapAffil 2016 dataset -- PubMed author affiliations mapped to cities and their geocodes worldwide. Prepared by Vetle Torvik 2018-04-05 The dataset comes as a single tab-delimited Latin-1 encoded file (only the City column uses non-ASCII characters), and should be about 3.5GB uncompressed. &bull; How was the dataset created? The dataset is based on a snapshot of PubMed (which includes Medline and PubMed-not-Medline records) taken in the first week of October, 2016. Check here for information to get PubMed/MEDLINE, and NLMs data <a href ="https://www.nlm.nih.gov/databases/download/pubmed_medline.html">Terms and Conditions</a> &bull; Affiliations are linked to a particular author on a particular article. Prior to 2014, NLM recorded the affiliation of the first author only. However, MapAffil 2016 covers some PubMed records lacking affiliations that were harvested elsewhere, from PMC (e.g., PMID 22427989), NIH grants (e.g., 1838378), and Microsoft Academic Graph and ADS (e.g. 5833220). &bull; Affiliations are pre-processed (e.g., transliterated into ASCII from UTF-8 and html) so they may differ (sometimes a lot; see PMID 27487542) from PubMed records. &bull; All affiliation strings where processed using the MapAffil procedure, to identify and disambiguate the most specific place-name, as described in: <i>Torvik VI. MapAffil: A bibliographic tool for mapping author affiliation strings to cities and their geocodes worldwide. D-Lib Magazine 2015; 21 (11/12). 10p</i> &bull; Look for <a href="https://doi.org/10.1186/s41182-017-0073-6">Fig. 4</a> in the following article for coverage statistics over time: <i>Palmblad M, Torvik VI. Spatiotemporal analysis of tropical disease research combining Europe PMC and affiliation mapping web services. Tropical medicine and health. 2017 Dec;45(1):33.</i> Expect to see big upticks in coverage of PMIDs around 1988 and for non-first authors in 2014. &bull; The code and back-end data is periodically updated and made available for query by PMID at <a href="http://abel.ischool.illinois.edu/">Torvik Research Group</a> &bull; What is the format of the dataset? The dataset contains 37,406,692 rows. Each row (line) in the file has a unique PMID and author postition (e.g., 10786286_3 is the third author name on PMID 10786286), and the following thirteen columns, tab-delimited. All columns are ASCII, except city which contains Latin-1. 1. PMID: positive non-zero integer; int(10) unsigned 2. au_order: positive non-zero integer; smallint(4) 3. lastname: varchar(80) 4. firstname: varchar(80); NLM started including these in 2002 but many have been harvested from outside PubMed 5. year of publication: 6. type: EDU, HOS, EDU-HOS, ORG, COM, GOV, MIL, UNK 7. city: varchar(200); typically 'city, state, country' but could inlude further subvisions; unresolved ambiguities are concatenated by '|' 8. state: Australia, Canada and USA (which includes territories like PR, GU, AS, and post-codes like AE and AA) 9. country 10. journal 11. lat: at most 3 decimals (only available when city is not a country or state) 12. lon: at most 3 decimals (only available when city is not a country or state) 13. fips: varchar(5); for USA only retrieved by lat-lon query to https://geo.fcc.gov/api/census/block/find

Prepared by Vetle Torvik 2021-05-07 The dataset comes as a single tab-delimited Latin-1 encoded file (only the City column uses non-ASCII characters). • How was the dataset created? The dataset is based on a snapshot of PubMed (which includes Medline and PubMed-not-Medline records) taken in December, 2018. (NLMs baseline 2018 plus updates throughout 2018). Affiliations are linked to a particular author on a particular article. Prior to 2014, NLM recorded the affiliation of the first author only. However, MapAffil 2018 covers some PubMed records lacking affiliations that were harvested elsewhere, from PMC (e.g., PMID 22427989), NIH grants (e.g., 1838378), and Microsoft Academic Graph and ADS (e.g. 5833220). Affiliations are pre-processed (e.g., transliterated into ASCII from UTF-8 and html) so they may differ (sometimes a lot; see PMID 27487542) from PubMed records. All affiliation strings where processed using the MapAffil procedure, to identify and disambiguate the most specific place-name, as described in: Torvik VI. MapAffil: A bibliographic tool for mapping author affiliation strings to cities and their geocodes worldwide. D-Lib Magazine 2015; 21 (11/12). 10p • Look for Fig. 4 in the following article for coverage statistics over time: Palmblad, M., Torvik, V.I. Spatiotemporal analysis of tropical disease research combining Europe PMC and affiliation mapping web services. Trop Med Health 45, 33 (2017). <a href="https://doi.org/10.1186/s41182-017-0073-6">https://doi.org/10.1186/s41182-017-0073-6</a> Expect to see big upticks in coverage of PMIDs around 1988 and for non-first authors in 2014. • The code and back-end data is periodically updated and made available for query by PMID at http://abel.ischool.illinois.edu/cgi-bin/mapaffil/search.py • What is the format of the dataset? The dataset contains 52,931,957 rows (plus a header row). Each row (line) in the file has a unique PMID and author order, and contains the following eighteen columns, tab-delimited. All columns are ASCII, except city which contains Latin-1. 1. PMID: positive non-zero integer; int(10) unsigned 2. au_order: positive non-zero integer; smallint(4) 3. lastname: varchar(80) 4. firstname: varchar(80); NLM started including these in 2002 but many have been harvested from outside PubMed 5. initial_2: middle name initial 6. orcid: From 2019 ORCID Public Data File https://orcid.org/ and from PubMed XML 7. year: year of the publication 8. journal: name of journal that the publication is published 9. affiliation: author's affiliation?? 10. disciplines: extracted from departments, divisions, schools, laboratories, centers, etc. that occur on at least unique 100 affiliations across the dataset, some with standardization (e.g., 1770799), English translations (e.g., 2314876), or spelling corrections (e.g., 1291843) 11. grid: inferred using a high-recall technique focused on educational institutions (but, for experimental purposes, includes a few select hospitals, national institutes/centers, international companies, governmental agencies, and 200+ other IDs [RINGGOLD, Wikidata, ISNI, VIAF, http] for institutions not in GRID). Based on 2019 GRID version https://www.grid.ac/ 12. type: EDU, HOS, EDU-HOS, ORG, COM, GOV, MIL, UNK 13. city: varchar(200); typically 'city, state, country' but could include further subdivisions; unresolved ambiguities are concatenated by '|' 14. state: Australia, Canada and USA (which includes territories like PR, GU, AS, and post-codes like AE and AA) 15. country 16. lat: at most 3 decimals (only available when city is not a country or state) 17. lon: at most 3 decimals (only available when city is not a country or state) 18. fips: varchar(5); for USA only retrieved by lat-lon query to https://geo.fcc.gov/api/census/block/find

This project investigates retraction indexing agreement among data sources: Crossref, Retraction Watch, Scopus, and Web of Science. As of July 2024, this reassesses the April 2023 union list of Schneider et al. (2023): https://doi.org/10.55835/6441e5cae04dbe5586d06a5f. As of April 2023, over 1 in 5 DOIs had discrepancies in retraction indexing among the 49,924 DOIs indexed as retracted in at least one of Crossref, Retraction Watch, Scopus, and Web of Science (Schneider et al., 2023). Here, we determine what changed in 15 months. Pipeline code to get the results files can be found in the GitHub repository https://github.com/infoqualitylab/retraction-indexing-agreement in the iPython notebook 'MET-STI2024_Reassessment_of_retraction_indexing_agreement.ipynb' Some files have been redacted to remove proprietary data, as noted in README.txt. Among our sources, data is openly available only for Crossref and Retraction Watch. FILE FORMATS: 1) unionlist_completed_2023-09-03-crws-ressess.csv - UTF-8 CSV file 2) unionlist_completed-ria_2024-07-09-crws-ressess.csv - UTF-8 CSV file 3) unionlist-15months-period_sankey.png - Portable Network Graphics (PNG) file 4) unionlist_ria_proportion_comparison.png - Portable Network Graphics (PNG) file 5) README.txt - text file FILE DESCRIPTION: Description of the files can be found in README.txt

These data are social media posts on Facebook and Twitter, as identified by SCOPES and healthfeedback.org as misinformation. We independently pulled social media data using Brandwatch’s (previously Crimson Hexagon) historical Twitter database and CrowdTangle, a public insights tool owned and operated by Facebook. Each of these databases only store publicly tagged posts and both databases have been used as Twitter and Facebook data sources in previous academic research studies (see, for example, Yun, Pamuksuz, and Duff 2019; Jernigan and Rushman 2014). The period on which we searched was January 1, 2020, to March 31, 2021. The original misinformation links were screenshots of posts or memes, links to native Facebook, Twitter, or Reddit posts and links to articles/websites containing misinformation.These links were passed through CrowdTangle to verify that they were not labeled. This process gave us a dataset of posts of unlabeled misinformation links. We found 12,184 instances of HF’s COVID-19 misinformation links being shared on Twitter versus 6,388 instances of the same links being shared on Facebook.

Author-ity 2018 dataset Prepared by Vetle Torvik Apr. 22, 2021 The dataset is based on a snapshot of PubMed taken in December 2018 (NLMs baseline 2018 plus updates throughout 2018). A total of 29.1 million Article records and 114.2 million author name instances. Each instance of an author name is uniquely represented by the PMID and the position on the paper (e.g., 10786286_3 is the third author name on PMID 10786286). Thus, each cluster is represented by a collection of author name instances. The instances were first grouped into "blocks" by last name and first name initial (including some close variants), and then each block was separately subjected to clustering. The resulting clusters are provided in two different formats, the first in a file with only IDs and PMIDs, and the second in a file with cluster summaries: #################### File 1: au2id2018.tsv #################### Each line corresponds to an author name instance (PMID and Author name position) with an Author ID. It has the following tab-delimited fields: 1. Author ID 2. PMID 3. Author name position ######################## File 2: authority2018.tsv ######################### Each line corresponds to a predicted author-individual represented by cluster of author name instances and a summary of all the corresponding papers and author name variants. Each cluster has a unique Author ID (the PMID of the earliest paper in the cluster and the author name position). The summary has the following tab-delimited fields: 1. Author ID (or cluster ID) e.g., 3797874_1 represents a cluster where 3797874_1 is the earliest author name instance. 2. cluster size (number of author name instances on papers) 3. name variants separated by '|' with counts in parenthesis. Each variant of the format lastname_firstname middleinitial, suffix 4. last name variants separated by '|' 5. first name variants separated by '|' 6. middle initial variants separated by '|' ('-' if none) 7. suffix variants separated by '|' ('-' if none) 8. email addresses separated by '|' ('-' if none) 9. ORCIDs separated by '|' ('-' if none). From 2019 ORCID Public Data File https://orcid.org/ and from PubMed XML 10. range of years (e.g., 1997-2009) 11. Top 20 most frequent affiliation words (after stoplisting and tokenizing; some phrases are also made) with counts in parenthesis; separated by '|'; ('-' if none) 12. Top 20 most frequent MeSH (after stoplisting) with counts in parenthesis; separated by '|'; ('-' if none) 13. Journal names with counts in parenthesis (separated by '|'), 14. Top 20 most frequent title words (after stoplisting and tokenizing) with counts in parenthesis; separated by '|'; ('-' if none) 15. Co-author names (lowercased lastname and first/middle initials) with counts in parenthesis; separated by '|'; ('-' if none) 16. Author name instances (PMID_auno separated by '|') 17. Grant IDs (after normalization; '-' if none given; separated by '|'), 18. Total number of times cited. (Citations are based on references harvested from open sources such as PMC). 19. h-index 20. Citation counts (e.g., for h-index): PMIDs by the author that have been cited (with total citation counts in parenthesis); separated by '|'

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 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 current version, Version 2.1.2, adds 6 additional coup events that occurred in 2022 and updates the coding of an attempted coup event in Kazakhstan in January 2022. Version 2.1.1 corrects a mistake in version 2.1.0, where the designation of “dissident coup” had been dropped in error for coup_id: 00201062021. Version 2.1.1 fixes this omission by marking the case as both a dissident coup and an auto-coup. Version 2.1.0 added 36 cases to the data set and removes two cases from the v2.0.0 data. This update also added actor coding for 46 coup events and adds executive outcomes to 18 events from version 2.0.0. A few other changes were made to correct inconsistencies in the coup ID variable and the date of the event. Changes from the previously released data (v2.0.0) also include: 1. Adding additional events and expanding the period covered to 1945-2022 2. Filling in missing actor information 3. Filling in missing information on the outcomes for the incumbent executive 4. Dropping events that were incorrectly coded as coup events <br> <b>Items in this Dataset</b> 1. <i>Cline Center Coup d'État Codebook v.2.1.2 Codebook.pdf</i> - This 16-page document provides a description of the Cline Center Coup d’État Project Dataset. The first section of this codebook provides a summary of the different versions of the data. The second section provides a succinct definition of a coup d’état used by the Coup d’État Project and an overview of the categories used to differentiate the wide array of events that meet the project's definition. It also defines coup outcomes. The third section describes the methodology used to produce the data. <i>Revised February 2023</i> 2. <i>Coup Data v2.1.2.csv</i> - This CSV (Comma Separated Values) file contains all of the coup event data from the Cline Center Coup d’État Project. It contains 29 variables and 981 observations. <i>Revised February 2023</i> 3. <i>Source Document v2.1.2.pdf</i> - This 315-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 that particular event. <i>Revised February 2023</i> 4. <i>README.md</i> - This file contains useful information for the user about the dataset. It is a text file written in markdown language. <i>Revised February 2023</i> <br> <b> Citation Guidelines</b> 1. To cite the codebook (or any other documentation associated with the Cline Center Coup d’État Project Dataset) please use the following citation: Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Scott Althaus. 2023. “Cline Center Coup d’État Project Dataset Codebook”. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.1.2. February 23. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V6 2. To cite data from the Cline Center Coup d’État Project Dataset please use the following citation (filling in the correct date of access): Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Emilio Soto. 2023. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.1.2. February 23. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V6

This is a sentence-level parallel corpus in support of research on OCR quality. The source data comes from: (1) Project Gutenberg for human-proofread "clean" sentences; and, (2) HathiTrust Digital Library for the paired sentences with OCR errors. In total, this corpus contains 167,079 sentence pairs from 189 sampled books in four domains (i.e., agriculture, fiction, social science, world war history) published from 1793 to 1984. There are 36,337 sentences that have two OCR views paired with each clean version. In addition to sentence texts, this corpus also provides the location (i.e., sentence and chapter index) of each sentence in its belonging Gutenberg volume.

Diversity - PubMed dataset Contact: Apratim Mishra (Oct, 2024) This dataset presents article-level (pmid) and author-level (auid) diversity data for PubMed articles. The chosen selection includes articles retrieved from Authority 2018 [1], 907 024 papers, and 1 316 838 authors, and is an expanded dataset of V1. The sample of articles consists of the top 40 journals in the dataset, limited to 2-12 authors published between 1991 – 2014, which are article type "journal type" written in English. Files are 'gzip' compressed and separated by tab space, and V3 includes the correct author count for the included papers (pmids) and updated results with no NaNs. ################################################ File1: auids_plos_3.csv.gz (Important columns defined, 5 in total) • AUID: a unique ID for each author • Genni: gender prediction • Ethnea: ethnicity prediction ################################################# File2: pmids_plos_3.csv.gz (Important columns defined) • pmid: unique paper • auid: all unique auids (author-name unique identification) • year: Year of paper publication • no_authors: Author count • journal: Journal name • years: first year of publication for every author • Country-temporal: Country of affiliation for every author • h_index: Journal h-index • TimeNovelty: Paper Time novelty [2] • nih_funded: Binary variable indicating funding for any author • prior_cit_mean: Mean of all authors’ prior citation rate • Insti_impact: All unique institutions’ citation rate • mesh_vals: Top MeSH values for every author of that paper • relative_citation_ratio: RCR The ‘Readme’ includes a description for all columns. [1] Torvik, Vetle; Smalheiser, Neil (2021): Author-ity 2018 - PubMed author name disambiguated dataset. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-2273402_V1 [2] Mishra, Shubhanshu; Torvik, Vetle I. (2018): Conceptual novelty scores for PubMed articles. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-5060298_V1

A set of chemical entity mentions derived from an NERC dataset analyzing 900 synthetic biology articles published by the ACS. This data is associated with the Synthetic Biology Knowledge System repository (https://web.synbioks.org/). The data in this dataset are raw mentions from the NERC data.

Related to the raw entity mentions (https://doi.org/10.13012/B2IDB-4163883_V1), this dataset represents the effects of the data cleaning process and collates all of the entity mentions which were too ambiguous to successfully link to the ChEBI ontology.

Citation context annotation for papers citing 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/> ). This is part of the supplemental data for Jodi Schneider, Di Ye, Alison Hill, and Ashley Whitehorn. "Continued Citation of a Fraudulent Clinical Trial Report, Eleven Years after it was retracted for Falsifying Data" [R&R under review with Scientometrics]. Overall we found 148 citations to the retracted paper from 2006 to 2019, However, this dataset does not include the annotations described in the 2015. in Ashley Fulton, Alison Coates, Marie Williams, Peter Howe, and Alison Hill. "Persistent citation of the only published randomized controlled trial of omega-3 supplementation in chronic obstructive pulmonary disease six years after its retraction." Publications 3, no. 1 (2015): 17-26. In this dataset 70 new and newly found citations are listed: 66 annotated citations and 4 pending citations (non-annotated since we don't have full-text). "New citations" refer to articles published from March 25, 2014 to 2019, found in Google Scholar and Web of Science. "Newly found citations" refer articles published 2006-2013, found in Google Scholar and Web of Science, but not previously covered in Ashley Fulton, Alison Coates, Marie Williams, Peter Howe, and Alison Hill. "Persistent citation of the only published randomised controlled trial of omega-3 supplementation in chronic obstructive pulmonary disease six years after its retraction." Publications 3, no. 1 (2015): 17-26. NOTES: This is Unicode data. Some publication titles & quotes are in non-Latin characters and they may contain commas, quotation marks, etc. FILES/FILE FORMATS Same data in two formats: 2006-2019-new-citation-contexts-to-Matsuyama.csv - Unicode CSV (preservation format only) 2006-2019-new-citation-contexts-to-Matsuyama.xlsx - Excel workbook (preferred format) ROW EXPLANATIONS 70 rows of data - one citing publication per row COLUMN HEADER EXPLANATIONS Note - processing notes Annotation pending - Y or blank Year Published - publication year ID - ID corresponding to the network analysis. See Ye, Di; Schneider, Jodi (2019): Network of First and Second-generation citations to Matsuyama 2005 from Google Scholar and Web of Science. University of Illinois at Urbana-Champaign. <a href="https://doi.org/10.13012/B2IDB-1403534_V2">https://doi.org/10.13012/B2IDB-1403534_V2</a> Title - item title (some have non-Latin characters, commas, etc.) Official Translated Title - item title in English, as listed in the publication Machine Translated Title - item title in English, translated by Google Scholar Language - publication language Type - publication type (e.g., bachelor's thesis, blog post, book chapter, clinical guidelines, Cochrane Review, consumer-oriented evidence summary, continuing education journal article, journal article, letter to the editor, magazine article, Master's thesis, patent, Ph.D. thesis, textbook chapter, training module) Book title for book chapters - Only for a book chapter - the book title University for theses - for bachelor's thesis, Master's thesis, Ph.D. thesis - the associated university Pre/Post Retraction - "Pre" for 2006-2008 (means published before the October 2008 retraction notice or in the 2 months afterwards); "Post" for 2009-2019 (considered post-retraction for our analysis) Identifier where relevant - ISBN, Patent ID, PMID (only for items we considered hard to find/identify, e.g. those without a DOI-based URL) URL where available - URL, ideally a DOI-based URL Reference number/style - reference Only in bibliography - Y or blank Acknowledged - If annotated, Y, Not relevant as retraction not published yet, or N (blank otherwise) Positive / "Poor Research" (Negative) - P for positive, N for negative if annotated; blank otherwise Human translated quotations - Y or blank; blank means Google scholar was used to translate quotations for Translated Quotation X Specific/in passing (overall) - Specific if any of the 5 quotations are specific [aggregates Specific / In Passing (Quotation X)] Quotation 1 - First quotation (or blank) (includes non-Latin characters in some cases) Translated Quotation 1 - English translation of "Quotation 1" (or blank) Specific / In Passing (Quotation 1) - Specific if "Quotation 1" refers to methods or results of the Matsuyama paper (or blank) What is referenced from Matsuyama (Quotation 1) - Methods; Results; or Methods and Results - blank if "Quotation 1" not specific, no associated quotation, or not yet annotated Quotation 2 - Second quotation (includes non-Latin characters in some cases) Translated Quotation 2 - English translation of "Quotation 2" Specific / In Passing (Quotation 2) - Specific if "Quotation 2" refers to methods or results of the Matsuyama paper (or blank) What is referenced from Matsuyama (Quotation 2) - Methods; Results; or Methods and Results - blank if "Quotation 2" not specific, no associated quotation, or not yet annotated Quotation 3 - Third quotation (includes non-Latin characters in some cases) Translated Quotation 3 - English translation of "Quotation 3" Specific / In Passing (Quotation 3) - Specific if "Quotation 3" refers to methods or results of the Matsuyama paper (or blank) What is referenced from Matsuyama (Quotation 3) - Methods; Results; or Methods and Results - blank if "Quotation 3" not specific, no associated quotation, or not yet annotated Quotation 4 - Fourth quotation (includes non-Latin characters in some cases) Translated Quotation 4 - English translation of "Quotation 4" Specific / In Passing (Quotation 4) - Specific if "Quotation 4" refers to methods or results of the Matsuyama paper (or blank) What is referenced from Matsuyama (Quotation 4) - Methods; Results; or Methods and Results - blank if "Quotation 4" not specific, no associated quotation, or not yet annotated Quotation 5 - Fifth quotation (includes non-Latin characters in some cases) Translated Quotation 5 - English translation of "Quotation 5" Specific / In Passing (Quotation 5) - Specific if "Quotation 5" refers to methods or results of the Matsuyama paper (or blank) What is referenced from Matsuyama (Quotation 5) - Methods; Results; or Methods and Results - blank if "Quotation 5" not specific, no associated quotation, or not yet annotated Further Notes - additional notes

This dataset includes five 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. - PMIDs in the "cited_by" column that meet either of the two conditions below have been excluded from analyses: [1] PMIDs of the citing papers are from retraction notices (i.e., those in the “retraction_notice_PMID.csv” file). [2] Citing paper and the cited retracted paper have the same PMID. 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. - Citation contexts that meet either of the two conditions below have been excluded from analyses: [1] PMIDs of the citing papers are from retraction notices (i.e., those in the “retraction_notice_PMID.csv” file). [2] Citing paper and the cited retracted paper have the same PMID. 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 = tables and 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: 724_knowingly_post_retraction_cit.csv</b> (updated) - The 724 post-retraction citation contexts that we determined knowingly cited the 7,813 retracted papers in "PubMed_retracted_publication_full_v3.tsv". - Two citation contexts from retraction notices have been excluded from analyses. 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, table_or_figure_caption = tables and 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 724_knowingly_post_retraction_cit.tsv. <b>FILENAME: retraction_notice_PMID.csv</b> (new file added for this version) - A list of 8,346 PMIDs of retraction notices indexed in PubMed (retrieved on August 20, 2020, searched with the query "retraction of publication" [PT] ).

Impact assessment is an evolving area of research that aims at measuring and predicting the potential effects of projects or programs. Measuring the impact of scientific research is a vibrant subdomain, closely intertwined with impact assessment. A recurring obstacle pertains to the absence of an efficient framework which can facilitate the analysis of lengthy reports and text labeling. To address this issue, we propose a framework for automatically assessing the impact of scientific research projects by identifying pertinent sections in project reports that indicate the potential impacts. We leverage a mixed-method approach, combining manual annotations with supervised machine learning, to extract these passages from project reports. This is a repository to save datasets and codes related to this project. Please read and cite the following paper if you would like to use the data: Becker M., Han K., Werthmann A., Rezapour R., Lee H., Diesner J., and Witt A. (2024). Detecting Impact Relevant Sections in Scientific Research. The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING). This folder contains the following files: evaluation_20220927.ods: Annotated German passages (Artificial Intelligence, Linguistics, and Music) - training data annotated_data.big_set.corrected.txt: Annotated German passages (Mobility) - training data incl_translation_all.csv: Annotated English passages (Artificial Intelligence, Linguistics, and Music) - training data incl_translation_mobility.csv: Annotated German passages (Mobility) - training data ttparagraph_addmob.txt: German corpus (unannotated passages) model_result_extraction.csv: Extracted impact-relevant passages from the German corpus based on the model we trained rf_model.joblib: The random forest model we trained to extract impact-relevant passages Data processing codes can be found at: https://github.com/khan1792/texttransfer

This lexicon is the expanded/enhanced version of the Moral Foundation Dictionary created by Graham and colleagues (Graham et al., 2013). Our Enhanced Morality Lexicon (EML) contains a list of 4,636 morality related words. This lexicon was used in the following paper - please cite this paper if you use this resource in your work. Rezapour, R., Shah, S., & Diesner, J. (2019). Enhancing the measurement of social effects by capturing morality. Proceedings of the 10th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA). Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), Minneapolis, MN. In addition, please consider citing the original MFD paper: <a href="https://doi.org/10.1016/B978-0-12-407236-7.00002-4">Graham, J., Haidt, J., Koleva, S., Motyl, M., Iyer, R., Wojcik, S. P., & Ditto, P. H. (2013). Moral foundations theory: The pragmatic validity of moral pluralism. In Advances in experimental social psychology (Vol. 47, pp. 55-130)</a>.

This dataset builds on an existing dataset which captures artists’ demographics who are represented by top tier galleries in the 2016–2017 New York art season (Case-Leal, 2017, https://web.archive.org/web/20170617002654/http://www.havenforthedispossessed.org/) with a census of reviews and catalogs about those exhibitions to assess proportionality of media coverage across race and gender. The readme file explains variables, collection, relationship between the datasets, and an example of how the Case-Leal dataset was transformed. The ArticleDataset.csv provides all articles with citation information as well as artist, artistic identity characteristic, and gallery. The ExhibitionCatalog.csv provides exhibition catalog citation information for each identified artist.

The organizations that contribute to the longevity of 67 long-lived molecular biology databases published in Nucleic Acids Research (NAR) between 1991-2016 were identified to address two research questions 1) which organizations fund these databases? and 2) which organizations maintain these databases? Funders were determined by examining funding acknowledgements in each database's most recent NAR Database Issue update article published (prior to 2017) and organizations operating the databases were determine through review of database websites.

The data are provided to illustrate methods in evaluating systematic transactional data reuse in machine learning. A library account-based recommender system was developed using machine learning processing over transactional data of 383,828 transactions (or check-outs) sourced from a large multi-unit research library. The machine learning process utilized the FP-growth algorithm over the subject metadata associated with physical items that were checked-out together in the library. The purpose of this research is to evaluate the results of systematic transactional data reuse in machine learning. The analysis herein contains a large-scale network visualization of 180,441 subject association rules and corresponding node metrics.

The XSEDE program manages the database of allocation awards for the portfolio of advanced research computing resources funded by the National Science Foundation (NSF). The database holds data for allocation awards dating to the start of the TeraGrid program in 2004 to present, with awards continuing through the end of the second XSEDE award in 2021. The project data include lead researcher and affiliation, title and abstract, field of science, and the start and end dates. Along with the project information, the data set includes resource allocation and usage data for each award associated with the project. The data show the transition of resources over a fifteen year span along with the evolution of researchers, fields of science, and institutional representation.

Coups d'Ètat 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 as part of its Societal Infrastructures and Development (SID) project. More specifically, this dataset identifies the outcomes of coup events (i.e., realized, unrealized, or conspiracy) the type of actor(s) who initiated the coup (i.e., military, rebels, etc.), as well as the fate of the deposed leader. Version 2.1.3 adds 19 additional coup events to the data set, corrects the date of a coup in Tunisia, and reclassifies an attempted coup in Brazil in December 2022 to a conspiracy. Version 2.1.2 added 6 additional coup events that occurred in 2022 and updated the coding of an attempted coup event in Kazakhstan in January 2022. Version 2.1.1 corrected a mistake in version 2.1.0, where the designation of “dissident coup” had been dropped in error for coup_id: 00201062021. Version 2.1.1 fixed this omission by marking the case as both a dissident coup and an auto-coup. Version 2.1.0 added 36 cases to the data set and removed two cases from the v2.0.0 data. This update also added actor coding for 46 coup events and added executive outcomes to 18 events from version 2.0.0. A few other changes were made to correct inconsistencies in the coup ID variable and the date of the event. Version 2.0.0 improved several aspects of the previous version (v1.0.0) and incorporated additional source material to include: • Reconciling missing event data • Removing events with irreconcilable event dates • Removing events with insufficient sourcing (each event needs at least two sources) • Removing events that were inaccurately coded as coup events • Removing variables that fell below the threshold of inter-coder reliability required by the project • Removing the spreadsheet ‘CoupInventory.xls’ because of inadequate attribution and citations in the event summaries • Extending the period covered from 1945-2005 to 1945-2019 • Adding events from Powell and Thyne’s Coup Data (Powell and Thyne, 2011) <br> <b>Items in this Dataset</b> 1. <i>Cline Center Coup d'État Codebook v.2.1.3 Codebook.pdf</i> - This 15-page document describes the Cline Center Coup d’État Project dataset. The first section of this codebook provides a summary of the different versions of the data. The second section provides a succinct definition of a coup d’état used by the Coup d'État Project and an overview of the categories used to differentiate the wide array of events that meet the project's definition. It also defines coup outcomes. The third section describes the methodology used to produce the data. <i>Revised February 2024</i> 2. <i>Coup Data v2.1.3.csv</i> - This CSV (Comma Separated Values) file contains all of the coup event data from the Cline Center Coup d’État Project. It contains 29 variables and 1000 observations. <i>Revised February 2024</i> 3. <i>Source Document v2.1.3.pdf</i> - This 325-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 that particular event. <i>Revised February 2024</i> 4. <i>README.md</i> - This file contains useful information for the user about the dataset. It is a text file written in markdown language. <i>Revised February 2024</i> <br> <b> Citation Guidelines</b> 1. To cite the codebook (or any other documentation associated with the Cline Center Coup d’État Project Dataset) please use the following citation: Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Scott Althaus. 2024. “Cline Center Coup d’État Project Dataset Codebook”. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.1.3. February 27. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V7 2. To cite data from the Cline Center Coup d’État Project Dataset please use the following citation (filling in the correct date of access): Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Emilio Soto. 2024. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.1.3. February 27. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V7

These datasets represent first-time collaborations between first and last authors (with mutually exclusive publication histories) on papers with 2 to 5 authors in years [1988,2009] in PubMed. Each record of each dataset captures aspects of the similarity, nearness, and complementarity between two authors about the paper marking the formation of their collaboration.

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.

File Name: Inclusion_Criteria_Annotation.csv Data Preparation: Xiaoru Dong Date of Preparation: 2018-12-14 Data Contributions: Jingyi Xie, Xiaoru Dong, Linh Hoang Data Source: Cochrane systematic reviews published up to January 3, 2018 by 52 different Cochrane groups in 8 Cochrane group networks. Associated Manuscript authors: Xiaoru Dong, Jingyi Xie, Linh Hoang, and Jodi Schneider. Associated Manuscript, Working title: Machine classification of inclusion criteria from Cochrane systematic reviews. Description: The file contains lists of inclusion criteria of Cochrane Systematic Reviews and the manual annotation results. 5420 inclusion criteria were annotated, out of 7158 inclusion criteria available. Annotations are either "Only RCTs" or "Others". There are 2 columns in the file: - "Inclusion Criteria": Content of inclusion criteria of Cochrane Systematic Reviews. - "Only RCTs": Manual Annotation results. In which, "x" means the inclusion criteria is classified as "Only RCTs". Blank means that the inclusion criteria is classified as "Others". Notes: 1. "RCT" stands for Randomized Controlled Trial, which, in definition, is "a work that reports on a clinical trial that involves at least one test treatment and one control treatment, concurrent enrollment and follow-up of the test- and control-treated groups, and in which the treatments to be administered are selected by a random process, such as the use of a random-numbers table." [Randomized Controlled Trial publication type definition from https://www.nlm.nih.gov/mesh/pubtypes.html]. 2. In order to reproduce the relevant data to this, please get the code of the project published on GitHub at: https://github.com/XiaoruDong/InclusionCriteria and run the code following the instruction provided.

Dataset to be for SocialMediaIE tutorial

This data set contains survey results from a 2021 survey of University of Illinois University Library employees conducted as part of the Becoming A Trans Inclusive Library Project to evaluate the awareness of University of Illinois faculty, staff, and student employees regarding transgender identities, and to assess the professional development needs of library employees to better serve trans and gender non-conforming patrons. The survey instrument is available in the IDEALS repository: http://hdl.handle.net/2142/110080.