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846 results for “commit”
Training Parents by Acceptance and Commitment Therapy for Managing Childhood Asthma Care
ClinicalTrials.gov study NCT02405962. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: The transcription factor Pou3f1 promotes neural fate commitment via activation of neural lineage genes and inhibition of external signaling pathways
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Consideration of food and nutrition in blue economy voluntary commitments
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Data and code for publication: zu Ermgassen et al., "Using supply chain data to monitor zero deforestation commitments: an assessment of progress in the Brazilian soy sector"
<p>Data and code for the publication zu Ermgassen et al., "Using supply chain data to monitor zero deforestation commitments: an assessment of progress in the Brazilian soy sector".</p> <p>This includes one data file (zdc-monitoring.rdata) and three scripts, to be run in [R]:<br> 01_PREPARE_DATA.R - Loads the data (from a user-specified location) and then produces summary data which are called in subsequent scripts.<br> 02_PLOT_FIGURES.R - Reproduces all figures in the manuscript.<br> 03_KEY_STATISTICS.R - Reproduces all statistics quoted in the manuscript, which are based on the Trase data.</p> <p>More detailed descriptions of the data contents and scripts are given in the README.md file.</p>
A dataset of Bot Commits
<p>This dataset contains information about 13,762,430 commits by 461 bots, each of whom have created more than 1000 commits, that have committed code in Git.</p> <p><br> The data is stored in a gzipped csv file (";" as the separator) with the following format in each line:</p> <p>"author_id"; "commit-sha"; "time-of-the-commit"; "timezone"; "files-modified-by-the-commit"; "projects-the-commit-is-associated-with"; "commit-message". In the case of having multiple projects and/or files for a given commit, they are separated by ','.</p> <p><br> These bots were detected using the BIMAN bot detection approach using the World of Code(http://worldofcode.org/) dataset.</p> <p>For details of the approach, see the corresponding paper in MSR 2020. </p> <p><br> If you're using this data for your research, please don't forget to cite it!!!</p>
Refining estimates of the commitment to global sea level rise over the next 500 years
<p>Within Australia alone, more than A$226 billion of coastal infrastructure is vulnerable to the anticipated rise in sea level by the end of the century. The IPCC Fifth Assessment Report concludes that the likely increase in global mean sea level during the 21st century ranges from 26-55 centimetres (under the low-end RCP2.6 climate scenario) to 45-82 centimetres (under the high-end RCP8.5 climate scenario). However, these projections do not take into account the potential for collapse of the marine-based sectors of the Antarctic Ice Sheet.</p> <p>Recent evidence has indicated that the IPCC projections may be under-estimates, with sea level increases of up to 2.5 metres possible by the end of the 21st century. Modelling studies have also demonstrated the potential for the Antarctic Ice Sheet to undergo irreversible collapse during the coming centuries. The most extreme prediction is that, under the RCP8.5 scenario, Antarctica alone could contribute 15.65±2.00 metres to global sea level by the year 2500.</p> <p>Here, we combine climate modelling and ice sheet modelling to explore the evolution of the Antarctic Ice Sheet over the next 500 years under a range of climate scenarios. We run the models many times to account for gaps in our understanding of ice sheet dynamics, using our knowledge of past changes in the Antarctic Ice Sheet to identify the configurations that are plausible. This allows us to generate robust projections of the Antarctic contribution to global sea level from the present to the year 2500, complete with quantified confidence intervals.</p> <p>We conclude that the sea level contribution during the 21st century will be modest, consistent with the IPCC Fifth Assessment Report, but that melting of the Antarctic Ice Sheet will accelerate thereafter. We also conclude that previous studies have underestimated the range of uncertainty in projections of future global sea level rise.</p>
Data from: Identification of top-priority areas to achieve EU Biodiversity Strategy for 2030 key commitments
<p>Data from: Identification of top-priority areas to achieve EU Biodiversity Strategy for 2030 key commitments. The dataset includes all files to run prioritization analyses included in Iulia V. Miu, Laurentiu Rozylowicz, Viorel D. Popescu, Paulina Anastasiu (2020) Identification of areas of very high biodiversity value to achieve the EU Biodiversity Strategy for 2030 key commitments. </p> <p>To run analyses, unzip the files in your computer (drive C) and use Zonation v4 developed by CBIG - CONSERVATION BIOLOGY INFORMATICS GROUP, Department of Biosciences of the University of Helsinki (<a href="https://www.helsinki.fi/en/researchgroups/digital-geography-lab/software-developed-in-cbig#section-52992">https://www.helsinki.fi/en/researchgroups/digital-geography-lab/software-developed-in-cbig#section-52992</a>). Files are available for nationwide prioritization of terrestrial Natural 2000 in Romania, taxa specific prioritization of terrestrial Natural 2000 in Romania (amphibians, mammals, plants, reptiles, invertebrates, and fish), and at biogeographical level prioritization of terrestrial Natural 2000 in Romania (Alpine, Continental, Pannonian, Steppic & Black Sea).</p>
Bot-NonBot-Commit-Msg
<p>This dataset contains the commit messages of the 13,150 bots and 13,150 human developers, as part of the data used in https://dl.acm.org/doi/10.1145/3379597.3387478. For privacy concerns, the developer identities have been replaced with their corresponding SHA1 values. The format of the data is:</p> <p><commit SHA>; <SHA1 value for developer identity>;commit message; <whether the developer is a bot or non-bot>; all git repositories the commit is a part of (separated by ;)</p> <p>git repo format: userName_repoName</p>
Irrevocable Commitments dataset
<p>Fyrqvist-Rantapuska-Torstila Irrevocable Commitments dataset</p> <p>This data contains data on irrevocable commitments in U.K. tender offers from 1985 to 2016. For data from 1/1/2001 to 10/18/2016, we independently hand-collect the data from FE InvestEgate (<a href="http://www.investegate.co.uk/">http://www.investegate.co.uk/</a>). FE InvestEgate data originates from the Regulatory News Service of London Stock Exchange. For data from 1/1/1985 to 12/31/2000, we collect the data with the help of Thomson Reuters. A team from Thomson Reuters hand-collected the data from prospectuses and other sources through an iterative process with one of the authors highlighting potential errors at each round. The final data was verified by performing random cross-checks with FE InvestEgate. For a subset of 35 deals the data is collected using London Stock Exchange, news, and company websites.</p>
100 NPM Repositories used for Anomalicious Commit Detection Experiment
<p>100 NPM Repositories used to evaluate positive rates of the Anomalicious Commit Detector</p>
Cross-Language Vulnerability Dataset with File Changes and Commit Messages
<p>Cross-Language Vulnerability Dataset with File Changes and Commit Messages</p>
COWORKER SUPPORT AS MODERATOR ON THE RELATIONSHIP BETWEEN HRM PRACTICES ANDORGANIZATIONAL COMMITMENT: A PROPOSED FRAMEWORK.
<p>Several factors have been proposed to have influence over organizational commitment; among some <br>of those prominent factors are human resource management practices. However, among the <br>prominent studies on the said relationship; the reported findings are inconsistent. Therefore, it is<br>proposed to incorporate a moderating variable to further explain this relationship. The current study <br>proposed coworker support as moderator on the relationship between HRM practices and<br>organizational commitment.</p>
Replication Package of the Paper: "Using Large Language Models for Commit Message Generation: A Preliminary Study"
<p>This replication package contains the evaluation data and script files used in the paper "Using Large Language Models for Commit Message Generation: A Preliminary Study". We provide below a brief description of each folder:</p><ul><li><strong>experiment_data/human_evaluation</strong>: Human evaluation results from two participants.</li><li><strong>experiment_data/msg</strong>: Generated commit messages of each method (baselines & LLMs) and human-written commit messages.</li><li><strong>script</strong>: Evaluation metric script, parallel inference script (for using OpenAI API).</li></ul>
Block-oriented description of a fictional CHP generation plant for unit commitment testing
<p>Block-oriented description of a fictional combined heat&power generation plant for unit commitment testing.</p> <p>Example input data is available at: https://doi.org/10.5281/zenodo.10875471</p>
Dataset for testing unit commitment models of CHCP plants
<p>Zipped set of 365 JSON files, one for each day of year 2022, containing fictional but realistic data for a combined heat, power and cooling generation plant:</p> <ul> <li>heat_demand: heat demand in kWh</li> <li>cooling_demand: cooling demand in kWh</li> <li>electricity_purchase: price for purchasing electricity in €/MWh</li> <li>electricity_selling: price for selling electricity in €/MWh</li> <li>gas_purchase: price for natural gas in €/MWh</li> </ul> <p> </p>
Single-cell Roadmap dataset "Cardiac differentiation roadmap for analysis of plasticity and balanced lineage commitment" (Snabel et al.)
<p>View the temporal single-cell transcriptomics data (UMAP, PCA, Heatmaps and Violin plots) using the Shiny App interface of iSEE (<a href="https://doi.org/10.12688/f1000research.14966.1">doi:10.12688/f1000research.14966.1</a>) for easy visualization of the single-cell data described in "Single-cell roadmap of cardiac differentiation identifies roles for ZNF711 and retinoic acid in balanced epicardial and cardiomyocyte lineage commitment" (Snabel et al., bioRXiv).</p> <p>For instructions on how to use this data, please visit https://github.com/Rebecza/scRoadmap_CardiacDiffs/.</p>
The commit history of the dependent libraries.
<p>Each file represents a software library and is named accordingly. These files store a commit history, where each commit signifies modifications between when a dependent library involve this library as dependency and when it subsequently removed the dependency.</p> <p>Note: The commits history is not later than 2019.</p>
BOLD release 5 April 2024, curated with pipeline commit d7f034f1e14f15daed708bb3e0e8ddf1b50e0249
Open the record for dataset details and reuse information.
Workplace commitments of snowsport instructors
<p>This data set (n=200) investigates the antecedents and outcomes of the workplace commitments of snowsport instructors.</p> <p>Constructs:</p> <ul> <li>Organizational commitment</li> <li>Destination commitment</li> <li>Coworker commitment</li> <li>Job commitment</li> <li>Perceived organizational support</li> <li>Camaraderie</li> <li>Meaningful work</li> <li>Place attachment</li> <li>Intention to return (organization/destination)</li> <li>Word-of-mouth (organization/destination)</li> <li>Intention to recommend (organization/destination)</li> </ul>
BOLD release 5 April 2024, curated with pipeline commit aeda07ed466cf326bfc2cd6f7897f9f413480ac0
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.