Find research datasets worth reusing

opencc-by-4.0Sep 2023View details →

NPJ Climate Action AR6 scenarios database submission histograms by model family and project

Based on submissions to the IPCC AR6 Scenarios Database, this datasets uses the metadata to construct histograms of the submitted scenarios by model family and project, noting the total submissions, vetted scenarios, and climate assessed scenarios. A total of 2304 scenarios were submitted to the global emissions database, of these, 618 did not passing vetting for sufficiently consistency with historical energy and emissions data, and a further 484 did not have sufficient data to perform a climate assessment, leaving a total of 1202 used in the primary assessment of scenarios.  The database based on the scenario metadata. The ‘model family’ was determined by removing version numbers from the full model name. The ‘project family’ was obtained using the ‘Scenario family’ variable in metadata, supplemented by manually checking against cited literature. The classification of vetted scenarios was based on the variable ‘Historical vetting’ and the climate assessment on the ‘Climate Category’. This version is based on version 1.0 of the AR6 scenarios database.

opencc-by-4.0Sep 2023View details →

Data from: Prospection of potential actions during visual working memory starts early, is flexible, and predicts behavior.

Raw data (EEG and behavior) reported in the manuscript "Prospection of potential actions in visual working memory starts early, is flexible,  and predicts behavior" by Rose Nasrawi, Sage E.P. Boettcher, and Freek van Ede

opencc-by-4.0Dec 2022View details →

Data for Progress on Climate Action: a Multilingual Machine Learning Analysis of the Global Stocktake

Data to go with our submission to Climatic Change titled "Progress on Climate Action: a Multilingual Machine Learning Analysis of the Global Stocktake". Dataset contains the embeddings (.zip with pickles) as well as the associated document items (idem), the most-closely associated keywords and paragraphs per topic in the final model (.xlsx), the reduced 2d embeddings with all selected paragraphs (.csv utf-8 encoded), as well as an overview with the meta-data per source (.csv utf-8 encoded).

opencc-by-4.0Oct 2023View details →

The Dynamics of Collective Action Corpus

This respository includes two datasets, a Document-Term Matrix and associated metadata, for 17,493 New York Times articles covering protest events, both saved as single R objects. These datasets are based on the original <a href="https://web.stanford.edu/group/collectiveaction/cgi-bin/drupal/">Dynamics of Collective Action (DoCA) dataset</a>  (Wang and Soule 2012; Earl, Soule, and McCarthy). The original DoCA datset contains variables for protest events referenced in roughly 19,676 New York Times articles reporting on collective action events occurring in the US between 1960 and 1995. Data were collected as part of the Dynamics of Collective Action Project at Stanford University. Research assistants read every page of all daily issues of the New York Times to find descriptions of 23,624 distinct protest events. The text for the news articles were not included in the original DoCA data. We attempted to recollect the raw text in a semi-supervised fashion by matching article titles to create the Dynamics of Collective Action Corpus. In addition to hand-checking random samples and hand-collecting some articles (specifically, in the case of false positives), we also used some automated matching processes to ensure the recollected article titles matched their respective titles in the DoCA dataset. The final number of recollected and matched articles is 17,493. We then subset the original DoCA dataset to include only rows that match a recollected article. The "20231006_dca_metadata_subset.Rdata" contains all of the metadata variables from the original DoCA dataset (see <a href="https://web.stanford.edu/group/collectiveaction/cgi-bin/drupal/node/17">Codebook</a>), with the addition of "pdf_file" (used to link to original article pdfs) and "pub_title" (which is the title of the recollected article and may differ from the "title" variable in the original dataset), for a total of 106 variables and 21,126 rows (noting that a row is a distinct protest events and one article may cover more than one protest event). Once collected, we prepared these texts using typical preprocessing procedures (and some less typical procedures, which were necessary given that these were OCRed texts). We followed these steps in this order: We removed headers and footers that were consistent across all digitized stories and any web links or HTML; added a single space before an uppercase letter when it was flush against a lowercase letter to its right (e.g., turning "JohnKennedy'' into "John Kennedy''); removed excess whitespace; converted all characters to the broadest range of Latin characters and then transliterated to "Basic Latin'' ASCII characters; replaced curly quotes with their ASCII counterparts; replaced contractions (e.g., turned "it's'' into "it is''); removed punctuation; removed capitalization; removed numbers; fixed word kerning; applied a final extra round of whitespace removal. We then tokenized them by following the rule that each word is a character string surrounded by a single space. At this step, each document is then a list of tokens. We count each unique token to create a document-term matrix (DTM), where each row is an article, each column is a unique token (occurring at least once in the corpus as a whole), and each cell is the number of times each token occurred in each article. Finally, we removed words (i.e., columns in the DTM) that occurred less than four times in the corpus as a whole or were only a single character in length (likely orphaned characters from the OCRing process). The final DTM has 66,552 unique words, 10,134,304 total tokens and 17,493 documents. The "20231006_dca_dtm.Rdata" is a sparse matrix class object from the <a href="https://cran.r-project.org/web/packages/Matrix/index.html">Matrix R package</a>. In R, use the load() function to load the objects `dca_dtm` and `dca_meta`. To associate the `dca_meta` to the `dca_dtm` , match the "pdf_file" variable in`dca_meta` to the rownames of `dca_dtm`.

opencc-by-4.0Aug 2024View details →

Now or Never? Global Review of Reactive and Near Real-Time Conservation Actions For Cetaceans

Table S1: Information on resulting publications (n = 64) reviewed in this study.

Study of DS-1062a in Advanced or Metastatic Non-small Cell Lung Cancer With Actionable Genomic Alterations (TROPION-Lung05)

ClinicalTrials.gov study NCT04484142. IPD Sharing: YES. Countries: 10. Publications: 1.

Million Veteran Program Return of Actionable Results

ClinicalTrials.gov study NCT04178122. IPD Sharing: YES. Countries: 1. Publications: 2.

Respiratory Exacerbation Plans for Action and Care Transitions for Children With Severe CP

ClinicalTrials.gov study NCT05292365. IPD Sharing: YES. Countries: 1. Publications: 2.

Study of DS-1062a Versus Docetaxel in Previously Treated Advanced or Metastatic Non-small Cell Lung Cancer With or Without Actionable Genomic Alterations (TROPION-LUNG01)

ClinicalTrials.gov study NCT04656652. IPD Sharing: YES. Countries: 26. Publications: 2.