Datasets

To gather news articles from the web that discuss the Cochrane Review, we used Altmetric Explorer from Altmetric.com and retrieved articles on August 1, 2023. We selected all articles that were written in English, published in the United States, and had a publication date prior to March 10, 2023 (according to the “Mention Date” on Altmetric.com). This date is significant as it is when Cochrane issued a statement about the "misleading interpretation" of the Cochrane Review. The collection of news articles is presented in the Altmetric_data.csv file. The dataset contains the following data that we exported from Altmetric Explorer: - Publication date of the news article - Title of the news article - Source/publication venue of the news article - URL - Country We manually checked and added the following information: - Whether the article still exists - Whether the article is accessible - Whether the article is from the original source We assigned MAXQDA IDs to the news articles. News articles were assigned the same ID when they were (a) identical or (b) in the case of Article 207, closely paraphrased, paragraph by paragraph. Inaccessible items were assigned a MAXQDA ID based on their "Mention Title". For each article from Altmetric.com, we first tried to use the Web Collector for MAXQDA to download the article from the website and imported it into MAXQDA (version 22.7.0). If an article could not be retrieved using the Web Collector, we either downloaded the .html file or in the case of Article 128, retrieved it from the NewsBank database through the University of Illinois Library. We then manually extracted direct quotations from the articles using MAXQDA. We included surrounding words and sentences, and in one case, a news agency’s commentary, around direct quotations for context where needed. The quotations (with context) are the positions in our analysis. We also identified who was quoted. We excluded quotations when we could not identify who or what was being quoted. We annotated quotations with codes representing groups (government agencies, other organizations, and research publications) and individuals (authors of the Cochrane Review, government agency representatives, journalists, and other experts such as epidemiologists). The MAXQDA_data.csv file contains excerpts from the news articles that contain the direct quotations we identified. For each excerpt, we included the following information: - MAXQDA ID of the document from which the excerpt originates; - The collection date and source of the document; - The code with which the excerpt is annotated; - The code category; - The excerpt itself.

Diversity - PubMed dataset Contact: Apratim Mishra (March 22, 2024) This dataset presents article-level (pmid) and author-level (auid) diversity data for PubMed articles. The selection chosen includes articles retrieved from Authority 2018 [1], a total of 228 040 papers and 440 310 authors. The sample of papers is based on the top 40 journals in the dataset, limited to 2-10 authors published between 1990 – 2010, and stratified on paper count per year. Additionally, this dataset is limited to papers where the lead author is affiliated with one of the four countries: the US, the UK, Canada, and Australia. Files are encoded with ‘utf-8’. ################################################ File1: auids_plos.csv (Important columns defined, 7 in total) • AUID: a unique ID for each author • Ethnea: ethnicity prediction • Genni: gender prediction ################################################# File2: pmids_plos.csv (Important columns defined, 33 in total) • pmid: unique paper ID • year: Year of paper publication • no_authors: Author count • journal: Journal name • years: first year of publication for every author • age_bin: Binned age 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 NIH funding for any author • prior_cit_mean: Mean of all authors’ prior citation rate • Insti_impact_all: All authors’ respective institutions’ citation count • Insti_impact: Maximum of all institutions’ citation count • mesh_vals: Top MeSH values for every author for that paper • outer_mesh_vals: MeSH qualifiers for every author for 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

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

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

Hype - PubMed dataset Prepared by Apratim Mishra This dataset captures ‘Hype’ within biomedical abstracts sourced from PubMed. The selection chosen is ‘journal articles’ written in English, published between 1975 and 2019, totaling ~5.2 million. The classification relies on the presence of specific candidate ‘hype words’ and their abstract location. Therefore, each article might have multiple instances in the dataset due to the presence of multiple hype words in different abstract sentences. The candidate hype words are 36 in count: 'major', 'novel', 'central', 'critical', 'essential', 'strongly', 'unique', 'promising', 'markedly', 'excellent', 'crucial', 'robust', 'importantly', 'prominent', 'dramatically', 'favorable', 'vital', 'surprisingly', 'remarkably', 'remarkable', 'definitive', 'pivotal', 'innovative', 'supportive', 'encouraging', 'unprecedented', 'bright', 'enormous', 'exceptional', 'outstanding', 'noteworthy', 'creative', 'assuring', 'reassuring', 'spectacular', and 'hopeful'. File 1: hype_dataset.csv Primary dataset. It has the following columns: 1. PMID: represents unique article ID in PubMed 2. Hype_word: Candidate hype word, such as ‘novel.’ 3. Sentence: Sentence in abstract containing the hype word. 4. Abstract_length: Length of article abstract. 5. Hype_percentile: Abstract relative position of hype word. 6. Hype_value: Propensity of hype based on the hype word, the sentence, and the abstract location. 7. Introduction: The ‘I’ component of the hype word based on IMRaD 8. Methods: The ‘M’ component of the hype word based on IMRaD 9. Results: The ‘R’ component of the hype word based on IMRaD 10. Discussion: The ‘D’ component of the hype word based on IMRaD File 2: hype_removed_phrases.csv Secondary dataset with same columns as File 1. Hype in the primary dataset is based on excluding certain phrases that are rarely hype. The phrases that were removed are included in File 2 and modeled separately. Removed phrases: 1. Major: histocompatibility, component, protein, metabolite, complex, surgery 2. Novel: assay, mutation, antagonist, inhibitor, algorithm, technique, series, method, hybrid 3. Central: catheters, system, design, composite, catheter, pressure, thickness, compartment 4. Critical: compartment, micelle, temperature, incident, solution, ischemia, concentration 5. Essential: medium, features, properties, opportunities 6. Unique: model, amino 7. Robust: regression 8. Vital: capacity, signs, organs, status, structures, staining, rates, cells, information 9. Outstanding: questions, issues, question, challenge, problems, problem, remains 10. Remarkable: properties 11. Definite: radiotherapy, surgery 12. Bright: field

This dataset consists the 286 publications retrieved from Web of Science and Scopus on July 6, 2023 as citations for (Willoughby et al., 2014): Willoughby, Patrick H., Jansma, Matthew J., & Hoye, Thomas R. (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, which we exported into a .csv file and an .rtf file. Willoughby2014_286citing_publications.csv is a Zotero data export of the citing publications. Willoughby2014_286citing_publications.rtf is a bibliography of the citing publications, using a variation of American Psychological Association style (7th edition) with full names instead of initials.

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

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.

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 was developed to create a census of sufficiently documented molecular biology databases to answer several preliminary research questions. Articles published in the annual Nucleic Acids Research (NAR) “Database Issues” were used to identify a population of databases for study. Namely, the questions addressed herein include: 1) what is the historical rate of database proliferation versus rate of database attrition?, 2) to what extent do citations indicate persistence?, and 3) are databases under active maintenance and does evidence of maintenance likewise correlate to citation? An overarching goal of this study is to provide the ability to identify subsets of databases for further analysis, both as presented within this study and through subsequent use of this openly released dataset.

Bibliotelemetry data are provided in support of the evaluation of Internet of Things (IoT) middleware within library collections. IoT infrastructure within the physical library environment is the basis for an integrative, hybrid approach to digital resource recommenders. The IoT infrastructure provides mobile, dynamic wayfinding support for items in the collection, which includes features for location-based recommendations. A modular evaluation and analysis herein clarified the nature of users’ requests for recommendations based on their location, and describes subject areas of the library for which users request recommendations. The modular mobile design allowed for deep exploration of bibliographic identifiers as they appeared throughout the global module system, serving to provide context to the searching and browsing data that are the focus of this study.

Conceptual novelty analysis data based on PubMed Medical Subject Headings ---------------------------------------------------------------------- Created by Shubhanshu Mishra, and Vetle I. Torvik on April 16th, 2018 ## Introduction This is a dataset created as part of the publication titled: Mishra S, Torvik VI. Quantifying Conceptual Novelty in the Biomedical Literature. D-Lib magazine : the magazine of the Digital Library Forum. 2016;22(9-10):10.1045/september2016-mishra. It contains final data generated as part of our experiments based on MEDLINE 2015 baseline and MeSH tree from 2015. The dataset is distributed in the form of the following tab separated text files: * PubMed2015_NoveltyData.tsv - Novelty scores for each paper in PubMed. The file contains 22,349,417 rows and 6 columns, as follow: - PMID: PubMed ID - Year: year of publication - TimeNovelty: time novelty score of the paper based on individual concepts (see paper) - VolumeNovelty: volume novelty score of the paper based on individual concepts (see paper) - PairTimeNovelty: time novelty score of the paper based on pair of concepts (see paper) - PairVolumeNovelty: volume novelty score of the paper based on pair of concepts (see paper) * mesh_scores.tsv - Temporal profiles for each MeSH term for all years. The file contains 1,102,831 rows and 5 columns, as follow: - MeshTerm: Name of the MeSH term - Year: year - AbsVal: Total publications with that MeSH term in the given year - TimeNovelty: age (in years since first publication) of MeSH term in the given year - VolumeNovelty: : age (in number of papers since first publication) of MeSH term in the given year * meshpair_scores.txt.gz (36 GB uncompressed) - Temporal profiles for each MeSH term for all years - Mesh1: Name of the first MeSH term (alphabetically sorted) - Mesh2: Name of the second MeSH term (alphabetically sorted) - Year: year - AbsVal: Total publications with that MeSH pair in the given year - TimeNovelty: age (in years since first publication) of MeSH pair in the given year - VolumeNovelty: : age (in number of papers since first publication) of MeSH pair in the given year * README.txt file ## Dataset creation This dataset was constructed using multiple datasets described in the following locations: * MEDLINE 2015 baseline: <a href="https://www.nlm.nih.gov/bsd/licensee/2015_stats/baseline_doc.html">https://www.nlm.nih.gov/bsd/licensee/2015_stats/baseline_doc.html</a> * MeSH tree 2015: <a href="ftp://nlmpubs.nlm.nih.gov/online/mesh/2015/meshtrees/">ftp://nlmpubs.nlm.nih.gov/online/mesh/2015/meshtrees/</a> * Source code provided at: <a href="https://github.com/napsternxg/Novelty">https://github.com/napsternxg/Novelty</a> Note: 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 <a href="https://www.nlm.nih.gov/databases/download/pubmed_medline.html">here </a>for information to get PubMed/MEDLINE, and NLMs data Terms and Conditions: Additional data related updates can be found at: <a href="http://abel.ischool.illinois.edu">Torvik Research Group</a> ## Acknowledgments This work was made possible in part with funding to VIT from <a href="https://projectreporter.nih.gov/project_info_description.cfm?aid=8475017&icde=18058490">NIH grant P01AG039347 </a> and <a href="http://www.nsf.gov/awardsearch/showAward?AWD_ID=1348742">NSF grant 1348742 </a>. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ## License Conceptual novelty analysis data based on PubMed Medical Subject Headings by Shubhanshu Mishra, and Vetle Torvik is licensed under a Creative Commons Attribution 4.0 International License. Permissions beyond the scope of this license may be available at <a href="https://github.com/napsternxg/Novelty">https://github.com/napsternxg/Novelty</a>

The PDF describes the process and data used for the heuristic user evaluation described in the related article “<i>Evaluating an automatic data extraction tool based on the theory of diffusion of innovation</i>” by Linh Hoang, Frank Scannapieco, Linh Cao, Yingjun Guan, Yi-Yun Cheng, and Jodi Schneider (under submission).<br /> Frank Scannapieco assessed RobotReviewer data extraction performance on ten articles in 2018-02. Articles are included papers from an update review: Sabharwal A., G.-F.I., Stellrecht E., Scannapeico F.A. <i>Periodontal therapy to prevent the initiation and/or progression of common complex systemic diseases and conditions</i>. An update. Periodontol 2000. In Press. <br/> The form was created in consultation with Linh Hoang and Jodi Schneider. To do the assessment, Frank Scannapieco entered PDFs for these ten articles into RobotReviewer and then filled in ten evaluation forms, based on the ten Robot Reviewer automatic data extraction reports. Linh Hoang analyzed these ten evaluation forms and synthesized Frank Scannapieco’s comments to arrive at the evaluation results for the heuristic user evaluation.

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.

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.

Trained models for multi-task multi-dataset learning for text classification in tweets. Classification tasks include sentiment prediction, abusive content, sarcasm, and veridictality. Models were trained using: <a href="https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_classification.py">https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_classification.py</a> See <a href="https://github.com/socialmediaie/SocialMediaIE">https://github.com/socialmediaie/SocialMediaIE</a> and <a href="https://socialmediaie.github.io">https://socialmediaie.github.io</a> for details. If you are using this data, please also cite the related article: Shubhanshu Mishra. 2019. Multi-dataset-multi-task Neural Sequence Tagging for Information Extraction from Tweets. In Proceedings of the 30th ACM Conference on Hypertext and Social Media (HT '19). ACM, New York, NY, USA, 283-284. DOI: https://doi.org/10.1145/3342220.3344929

Trained models for multi-task multi-dataset learning for sequence tagging in tweets. Sequence tagging tasks include POS, NER, Chunking, and SuperSenseTagging. Models were trained using: <a href="https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_experiment.py">https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_experiment.py</a> See <a href="https://github.com/socialmediaie/SocialMediaIE">https://github.com/socialmediaie/SocialMediaIE</a> and <a href="https://socialmediaie.github.io">https://socialmediaie.github.io</a> for details. If you are using this data, please also cite the related article: Shubhanshu Mishra. 2019. Multi-dataset-multi-task Neural Sequence Tagging for Information Extraction from Tweets. In Proceedings of the 30th ACM Conference on Hypertext and Social Media (HT '19). ACM, New York, NY, USA, 283-284. DOI: https://doi.org/10.1145/3342220.3344929

This is part of the Cline Center’s ongoing Social, Political and Economic Event Database Project (SPEED) project. Each observation represents an event involving civil unrest, repression, or political violence in Sierra Leone, Liberia, and the Philippines (1979-2009). These data were produced in an effort to describe the relationship between exploitation of natural resources and civil conflict, and to identify policy interventions that might address resource-related grievances and mitigate civil strife. This work is the result of a collaboration between the US Army Corps of Engineers’ Construction Engineer Research Laboratory (ERDC-CERL), the Swedish Defence Research Agency (FOI) and the Cline Center for Advanced Social Research (CCASR). The project team selected case studies focused on nations with a long history of civil conflict, as well as lucrative natural resources. The Cline Center extracted these events from country-specific articles published in English by the British Broadcasting Corporation (BBC) Summary of World Broadcasts (SWB) from 1979-2008 and the CIA’s Foreign Broadcast Information Service (FBIS) 1999-2004. Articles were selected if they mentioned a country of interest, and were tagged as relevant by a Cline Center-built machine learning-based classification algorithm. Trained analysts extracted nearly 10,000 events from nearly 5,000 documents. The codebook—available in PDF form below—describes the data and production process in greater detail.

Files in this dataset represent an investigation into use of the Library mobile app Minrva during the months of May 2015 through December 2015. During this time interval 45,975 API hits were recorded by the Minrva web server. The dataset included herein is an analysis of the following: 1) a delineation of API hits to mobile app modules use in the Minrva app by month, 2) a general analysis of Minrva app downloads to module use, and 3) the annotated data file providing associations from API hits to specific modules used, organized by month (May 2015 – December 2015).

The relationship between physical activity and mental health, especially depression, is one of the most studied topics in the field of exercise science and kinesiology. Although there is strong consensus that regular physical activity improves mental health and reduces depressive symptoms, some debate the mechanisms involved in this relationship as well as the limitations and definitions used in such studies. Meta-analyses and systematic reviews continue to examine the strength of the association between physical activity and depressive symptoms for the purpose of improving exercise prescription as treatment or combined treatment for depression. This dataset covers 27 review articles (either systematic review, meta-analysis, or both) and 365 primary study articles addressing the relationship between physical activity and depressive symptoms. Primary study articles are manually extracted from the review articles. We used a custom-made workflow (Fu, Yuanxi. (2022). Scopus author info tool (1.0.1) [Python]. <a href="https://github.com/infoqualitylab/Scopus_author_info_collection">https://github.com/infoqualitylab/Scopus_author_info_collection</a> that uses the Scopus API and manual work to extract and disambiguate authorship information for the 392 reports. The author information file (author_list.csv) is the product of this workflow and can be used to compute the co-author network of the 392 articles. This dataset can be used to construct the inclusion network and the co-author network of the 27 review articles and 365 primary study articles. A primary study article is "included" in a review article if it is considered in the review article's evidence synthesis. Each included primary study article is cited in the review article, but not all references cited in a review article are included in the evidence synthesis or primary study articles. The inclusion network is a bipartite network with two types of nodes: one type represents review articles, and the other represents primary study articles. In an inclusion network, if a review article includes a primary study article, there is a directed edge from the review article node to the primary study article node. The attribute file (article_list.csv) includes attributes of the 392 articles, and the edge list file (inclusion_net_edges.csv) contains the edge list of the inclusion network. Collectively, this dataset reflects the evidence production and use patterns within the exercise science and kinesiology scientific community, investigating the relationship between physical activity and depressive symptoms. FILE FORMATS 1. article_list.csv - Unicode CSV 2. author_list.csv - Unicode CSV 3. Chinese_author_name_reference.csv - Unicode CSV 4. inclusion_net_edges.csv - Unicode CSV 5. review_article_details.csv - Unicode CSV 6. supplementary_reference_list.pdf - PDF 7. README.txt - text file 8. systematic_review_inclusion_criteria.csv - Unicode CSV <b>UPDATES IN THIS VERSION COMPARED TO V3</b> (Clarke, Caitlin; Lischwe Mueller, Natalie; Joshi, Manasi Ballal; Fu, Yuanxi; Schneider, Jodi (2023): The Inclusion Network of 27 Review Articles Published between 2013-2018 Investigating the Relationship Between Physical Activity and Depressive Symptoms. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4614455_V3) - We added a new file systematic_review_inclusion_criteria.csv.

We used the following keywords files to identify categories for journals and conferences not in Scopus, for our STI 2023 paper "Assessing the agreement in retraction indexing across 4 multidisciplinary sources: Crossref, Retraction Watch, Scopus, and Web of Science". The first four text files each contains keywords/content words in the form: 'keyword1', 'keyword2', 'keyword3', .... The file title indicates the name of the category: file1: healthscience_words.txt file2: lifescience_words.txt file3: physicalscience_words.txt file4: socialscience_words.txt The first four files were generated from a combination of software and manual review in an iterative process in which we: - Manually reviewed venue titles were not able to automatically categorize using the Scopus categorization or extending it as a resource. - Iteratively reviewed uncategorized venue titles to manually curate additional keywords as content words indicating a venue title could be classified in the category healthscience, lifescience, physicalscience, or socialscience. We used English content words and added words we could automatically translate to identify content words. NOTE: Terminology with multiple potential meanings or contain non-English words that did not yield useful automatic translations e.g., (e.g., Al-Masāq) were not selected as content words. The fifth text file is a list of stopwords in the form: 'stopword1', 'stopword2, 'stopword3', ... file5: stopwords.txt This file contains manually curated stopwords from venue titles to handle non-content words like 'conference' and 'journal,' etc. This dataset is a revision of the following dataset: Version 1: Lee, Jou; Schneider, Jodi: Keywords for manual field assignment for Assessing the agreement in retraction indexing across 4 multidisciplinary sources: Crossref, Retraction Watch, Scopus, and Web of Science. University of Illinois at Urbana-Champaign Data Bank. Changes from Version 1 to Version 2: - Added one author - Added a stopwords file that was used in our data preprocessing. - Thoroughly reviewed each of the 4 keywords lists. In particular, we added UTF-8 terminology, removed some non-content words and misclassified content words, and extensively reviewed non-English keywords.

Data for Post-retraction citation: A review of scholarly research on the spread of retracted science Schneider, Jodi; Das, Susmita; Léveillé, Jacqueline; Proescholdt, Randi Contact: Jodi Schneider jodi@illinois.edu & jschneider@pobox.com ********** OVERVIEW ********** This dataset provides further analysis for an ongoing literature review about post-retraction citation. This ongoing work extends a poster presented as: Jodi Schneider, Jacqueline Léveillé, Randi Proescholdt, Susmita Das, and The RISRS Team. Characterization of Publications on Post-Retraction Citation of Retracted Articles. Presented at the Ninth International Congress on Peer Review and Scientific Publication, September 8-10, 2022 hybrid in Chicago. https://hdl.handle.net/2142/114477 (now also in https://peerreviewcongress.org/abstract/characterization-of-publications-on-post-retraction-citation-of-retracted-articles/ ) Items as of the poster version are listed in the bibliography 92-PRC-items.pdf. Note that following the poster, we made several changes to the dataset (see changes-since-PRC-poster.txt). For both the poster dataset and the current dataset, 5 items have 2 categories (see 5-items-have-2-categories.txt). Articles were selected from the Empirical Retraction Lit bibliography (https://infoqualitylab.org/projects/risrs2020/bibliography/ and https://doi.org/10.5281/zenodo.5498474 ). The current dataset includes 92 items; 91 items were selected from the 386 total items in Empirical Retraction Lit bibliography version v.2.15.0 (July 2021); 1 item was added because it is the final form publication of a grouping of 2 items from the bibliography: Yang (2022) Do retraction practices work effectively? Evidence from citations of psychological retracted articles http://doi.org/10.1177/01655515221097623 Items were classified into 7 topics; 2 of the 7 topics have been analyzed to date. ********************** OVERVIEW OF ANALYSIS ********************** DATA ANALYZED: 2 of the 7 topics have been analyzed to date: field-based case studies (n = 20) author-focused case studies of 1 or several authors with many retracted publications (n = 15) FUTURE DATA TO BE ANALYZED, NOT YET COVERED: 5 of the 7 topics have not yet been analyzed as of this release: database-focused analyses (n = 33) paper-focused case studies of 1 to 125 selected papers (n = 15) studies of retracted publications cited in review literature (n = 8) geographic case studies (n = 4) studies selecting retracted publications by method (n = 2) ************** FILE LISTING ************** ------------------ BIBLIOGRAPHY ------------------ 92-PRC-items.pdf ------------------ TEXT FILES ------------------ README.txt 5-items-have-2-categories.txt changes-since-PRC-poster.txt ------------------ CODEBOOKS ------------------ Codebook for authors.docx Codebook for authors.pdf Codebook for field.docx Codebook for field.pdf Codebook for KEY.docx Codebook for KEY.pdf ------------------ SPREADSHEETS ------------------ field.csv field.xlsx multipleauthors.csv multipleauthors.xlsx multipleauthors-not-named.csv multipleauthors-not-named.xlsx singleauthors.csv singleauthors.xlsx *************************** DESCRIPTION OF FILE TYPES *************************** BIBLIOGRAPHY (92-PRC-items.pdf) presents the items, as of the poster version. This has minor differences from the current data set. Consult changes-since-PRC-poster.txt for details on the differences. TEXT FILES provide notes for additional context. These files end in .txt. CODEBOOKS describe the data we collected. The same data is provided in both Word (.docx) and PDF format. There is one general codebook that is referred to in the other codebooks: Codebook for KEY lists fields assigned (e.g., for a journal or conference). Note that this is distinct from the overall analysis in the Empirical Retraction Lit bibliography of fields analyzed; for that analysis see Proescholdt, Randi (2021): RISRS Retraction Review - Field Variation Data. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-2070560_V1 Other codebooks document specific information we entered on each column of a spreadsheet. SPREADSHEETS present the data collected. The same data is provided in both Excel (.xlsx) and CSV format. Each data row describes a publication or item (e.g., thesis, poster, preprint). For column header explainations, see the associated codebook. ***************************** DETAILS ON THE SPREADSHEETS ***************************** field-based case studies CODEBOOK: Codebook for field --REFERS TO: Codebook for KEY DATA SHEET: field REFERS TO: Codebook for KEY --NUMBER OF DATA ROWS: 20 NOTE: Each data row describes a publication/item. --NUMBER OF PUBLICATION GROUPINGS: 17 --GROUPED PUBLICATIONS: Rubbo (2019) - 2 items, Yang (2022) - 3 items author-focused case studies of 1 or several authors with many retracted publications CODEBOOK: Codebook for authors --REFERS TO: Codebook for KEY DATA SHEET 1: singleauthors (n = 9) --NUMBER OF DATA ROWS: 9 --NUMBER OF PUBLICATION GROUPINGS: 9 DATA SHEET 2: multipleauthors (n = 5 --NUMBER OF DATA ROWS: 5 --NUMBER OF PUBLICATION GROUPINGS: 5 DATA SHEET 3: multipleauthors-not-named (n = 1) --NUMBER OF DATA ROWS: 1 --NUMBER OF PUBLICATION GROUPINGS: 1 ********************************* CRediT <http://credit.niso.org> ********************************* Susmita Das: Conceptualization, Data curation, Investigation, Methodology Jaqueline Léveillé: Data curation, Investigation Randi Proescholdt: Conceptualization, Data curation, Investigation, Methodology Jodi Schneider: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Supervision

This dataset was developed as part of an online survey study that investigates how phatic expressions—comments that are social rather than informative in nature—influence the perceived helpfulness of online peer help-giving replies in an asynchronous college course discussion forum. During the study, undergraduate students (N = 320) rated and described the helpfulness of examples of replies to online requests for help, both with and without four types of phatic expressions: greeting/parting tokens, other-oriented comments, self-oriented comments, and neutral comments.

This is a dataset from a choice experiment survey on family forest landowner preferences for managing invasive species.