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867 results for “repositories”
Investigation of metadata standard use by geoscience data repositories
This dataset supports a paper being written about metadata standard use by geoscience data repositories. The study is being done to better understand which metadata standards and keyword vocabularies are prominent within the geoscience data repository landscape. The findings should be useful for NCAR's evaluation of metadata standards within our own systems, as well as by external data repository staff. The guiding questions of the study are as follows: 1. What metadata standards are geoscience data repositories using? 2. What keyword / subject term vocabularies are they using? 3. What interoperability challenges are present in the use of metadata and keyword vocabulary standards within the geoscience repository community?
Repository: A global emission inventory of particulate phosphorus from fertilizer production and handling
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Data repository for Lin et al. (2022) "Thermospheric neutral density variation during the "SpaceX" storm: Implications from physics-based whole geospace modeling"
This dataset contains the necessary data and plotting tools supporting the paper titled "Thermospheric neutral density variation during the "SpaceX" storm: Implications from physics-based whole geospace modeling", by Lin et al., 2022. The data set contains thermospheric mass density simulated by MAGE, TIEGCM, DTM, and MSIS for the 1-6 February 2022 geomagnetic storm event.
Online repository for Paper "AgentFL: Scaling LLM-based Fault Localization to Project-Level Context"
<h3>Summary</h3> <p>This is the online repository for the arXiv paper "AgentFL: Scaling LLM-based Fault Localization to Project-Level Context".</p> <p>We also provide the results for the TSE'25 paper "SOAPFL: A Standard Operating Procedure for LLM-based Method-Level Fault Localization".</p> <h3>Environment</h3> <ul> <li><a href="https://github.com/rjust/defects4j/tree/v1.4.0">Defects4J-V1.4.0</a> (Note that the buggy items in V1.4.0 is identical with V1.2.0, we use V1.4.0 to avoid some problems in V1.2.0)</li> <li><a href="https://github.com/rjust/defects4j/tree/v2.0.0">Defects4J-V2.0.0</a></li> <li>Python version >= 3.8.5</li> </ul> <h3>Defects4J Mod</h3> <p>Before running AgentFL, please apply the files under the <code>AgentFL/Defects4J_mod</code> directory to modify your Defects4J V1.4.0/V2.0.0.</p> <h3>Run AgentFL</h3> <p>Set your own OpenAI API key in <code>AgentFL/camel/model_backend.py</code></p> <p>It's easy to run AgentFL for localizing a bug with the following command:</p> <p><code>python3 run.py --config <CONFIG_DIR> --version <D4J_VERSION> --project <PROJECT> --bugID <BUG_ID> --model <GPT_MODEL_NAME></code></p> <p>For example:</p> <p><code>python3 run.py --config Default --version 1.4.0 --project Closure --bugID 26 --model GPT_3_5_TURBO</code></p> <p>More configs can be seen under the directory <code>AgentFL/Config</code></p> <h3>Results</h3> <p>We release all of the results of AgentFL in the <code>AgentFL/Results</code> directory, including the evaluation results on Defects4J V1.4.0/V2.0.0 and the ablation study result.</p> <p>For each bug, we record all of the prompts, responses, and intermediate outputs.</p> <blockquote> <p>NEW: We have released the newest results for TSE'25 paper "SOAPFL: A Standard Operating Procedure for LLM-based Method-Level Fault Localization". The results can be found in the `<a href="https://zenodo.org/api/records/16938304/draft/files/SoapFL_results.zip/content" target="_blank" rel="noopener noreferrer">SoapFL_results.zip</a>` file!</p> </blockquote> <h3>Human Evaluation Results</h3> <p>The human evaluation results can be found in the file <code>AgentFL/EvaluationResult/DebugResult_d4j140_GPT35_human.xlsx</code></p> <h3>System Messages for Agents</h3> <ul> <li>Test Code Reviewer:</li> </ul> <blockquote> <p>You are a Test Code Reviewer. We share a common interest in collaborating to successfully locate the buggy code that cause the test suite to fail. You can examine the test code and the initialized classes to analyze the similar behavior of the failed tests within the test suite. To locate the bug, you must write a response that appropriately solves the requested instruction based on your expertise.</p> </blockquote> <ul> <li>Source Code Reviewer</li> </ul> <blockquote> <p>You are a Source Code Reviewer. we are both working at DebugDev. We share a common interest in collaborating to successfully locate the buggy code that cause the test suite to fail. Your main responsibilities is to generate a comment for each covered method base on the method call relationship. To locate the bug, you must write a response that appropriately solves the requested instruction based on your expertise.</p> </blockquote> <ul> <li>Software Test Engineer</li> </ul> <blockquote> <p>You are a Software Test Engineer. We share a common interest in collaborating to successfully locate the buggy code that cause the test suite to fail. You main responsibilities include examining the information of the failed tests to analyze the possible causes of the test failures, and determining the method that need to be fixed. To locate the bug, you must write a response that appropriately solves the requested instruction based on your expertise.</p> </blockquote> <ul> <li>Software Architect</li> </ul> <blockquote> <p>You are a Software Architect. We share a common interest in collaborating to successfully locate the buggy code that cause the test suite to fail. You are very familiar with the architecture of the software, the functions of each class and method in the software. You main responsibilities include examining the given information to locate the possible buggy classes and buggy methods. To locate the bug, you must write a response that appropriately solves the requested instruction based on your expertise.</p> </blockquote>
Data Repository for the study of Groundwater Depletion, Food Security, and Power Utility
<p>The present article is based on exploratory and qualitative research methodology for understanding irrigation system with reference to water-energy-food nexus. This article is based on both primary and secondary data. The primary data has been collected through semi-structured in-depth interviews by picking some of the samples from the field. The primary data includes data from the field which is compiled into different sections of the data management plan as follows.</p><ol><li>The socio-economic and demographic profile of the 2 Villages based on the <strong>PRA</strong> (Participatory Rural Appraisal).</li><li>The qualitative data from all types of farmers to understand the common issues and problems of the topic under research (owners as well as non-owners with all socio-economic categories) through <strong>FGD</strong> (Focused Group Discussion).</li><li>The qualitative data from <strong>Cases and Case Studies</strong> to get the deeper understanding of the research of farmers / incidence / processes from the villages to provide some unique insights to the research.</li><li>The qualitative data through <strong>Semi-Structured Interviews</strong> from other stakeholders to understand the issues and problems of the topic under research from their point of view.</li></ol>
Gauge data repository for "Comprehensive evaluation and comparison of ten precipitation products in terms of accuracy and stability over a typical mountain basin, Southwest China"
<h4>1. Title: </h4> <p>Gauge data repository for "Comprehensive evaluation and comparison of ten precipitation products in terms of accuracy and stability over a typical mountain basin, Southwest China".</p> <h4>2. Corresponding author: </h4> <p>Xingbi Lei (leixingbi@st.gxu.edu.cn, leixingbi@foxmail.com).</p> <h4>3. Institution: </h4> <p>Key Laboratory of Disaster Prevention and Structural Safety of the Ministry of Education, Nanning 530000, China;</p> <p>College of Architecture and Civil Engineering, Guangxi University, Nanning 530000, China; </p> <p>Guangxi Provincial Engineering Research Center of Water Security and Intelligent Control for Karst Region, Guangxi University, Nanning 530000, China; </p> <p>Guangxi Key Laboratory of Disaster Prevention and Engineering Safety, Guangxi University, Nanning 530000, China; </p> <p>State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 200029, China; </p> <p>Guangxi Water & Power Design Institute Co., Ltd., Nanning 530023, China. </p> <h4>4. Description:</h4> <p>This repository contains gauge data for the research article "Comprehensive evaluation and comparison of ten precipitation products in terms of accuracy and stability over a typical mountain basin, Southwest China", which is currently under review for the journal "Atmospheric Research".</p> <p>The gauge data used in this study is the daily measurements (2003/01/01-2018/12/31) of 12 stations in the Chengbi River Basin (Southwest China). The data source was the Chengbi River Reservoir Bureau. The data has been quality controlled by the Bureau in accordance with "MWR, PRC,2015. Specification for precipitation observations: SL 21-2015". Please see the journal article for more detailed information.</p> <h4>5. Citation:</h4> <p>When using this dataset, please cite "Mo, C. et al., 2024. Comprehensive evaluation and comparison of ten precipitation products in terms of accuracy and stability over a typical mountain basin, Southwest China. Atmospheric Research, 297: 107116.10.1016/j.atmosres.2023.107116". </p> <p> </p>
Code and Data Repository for "Identifying microbiota community patterns important for plant protection using machine learning in synthetic community experiments"
<p>The repository includes data and R code necessary to reproduce all figures and analyses presented in the study titled "Identifying microbiota community patterns important for plant protection using machine learning in synthetic community experiments".</p>
GitHub Public Repository Metadata
<p>Metadata (i.e. no code) of all public repositories with 5+ stars on GitHub</p><p> </p><p>This dataset is obtained from the Github API and contains only public repository-level metadata.<br>It may be useful for anyone interested in studying the Github ecosystem. It contains approximately 3.1 million entries.</p><p>The Github API Terms of Service apply.</p><blockquote><p>You may not use this dataset for spamming purposes, including for the purposes of selling GitHub users' personal information, such as to recruiters, headhunters, and job boards.</p></blockquote><p><br>The data format is a JSON array of entries, an example of which is given below.</p><p><i>Redacted for anonymity.</i></p>
Public Repository: Aquino (2023) Front Netw Physiol
<p>This GitLab project contains data and MATLAB code to reproduce the analyses and figures published in</p><p>Aquino, M. R. C., Liddy, J. J., Napoli, C. D., Fonseca, S. T., van Emmerik, R. E. A., Busa, M. A. (2023). Changes to balance dynamics following a high-intensity run are associated with future injury occurrence in recreational runners. Fron Netw Physiol, 3(#), #. https://doi.org/10.3389/fnetp.2023.1227861.</p><p>Note: The alphanumeric code appended to the end of the folder name is the commit hash.</p>
Replication Package for 'Lessons Learned from Mining the Hugging Face Repository'
<p>Replication Package attached to the 'Lessons Learned from Mining the Hugging Face Repository' article. Within the README and accompanying scripts, you will find detailed instructions to guide you through the analysis conducted in the article. Please, remember to cite the original paper if the replication package is used.</p>
An investigation of data repositories in materials chemistry
<p>Detailed lists of articles used for testing accessibility and raw data used for analysis of data repositories in materials chemistry literature.</p>
ROS2 Python code repository from Github
<p>The dataset comprises a curated assemblage of GitHub repositories housing ROS2 source code scripted in Python. Acquired in November 2023, this compilation is organized within a compressed file. Included alongside the repositories is a CSV file mapping each directory within the archive to its respective repository on GitHub, facilitating streamlined navigation and utilization.</p>
Data repository for "Scalable Quantum Monte Carlo with Direct-Product Trial Wave Functions"
<p>This Zenodo deposit provides a comprehensive collection of raw data, processed data, and scripts associated with the research paper available at <a href="https://arxiv.org/abs/2306.15186" target="_new">https://arxiv.org/abs/2306.15186</a>.</p> <p>Contents:</p> <p>- data.zip includes all the QMCpack inputs and outputs, which can be used in the blocking analysis to obtain the AFQMC energy. Processed Data "SI for DP-MSD-AFQMC.xlsx" contains the AFQMC results after the blocking analysis in XLSX format. "SI for DP-MSD-AFQMC.xlsx" also includes the energies from all other methods presented in the paper.</p> <p>- scripts.zip contains all the Python scripts used in running calculations for the paper. This includes scripts to generate LAS-AFQMC and CAS-AFQMC trials (under scripts/utils) in QMCpack format, as well as inputs for each compound.</p>
Repository: No Magmatic Driving Force for Europan Seafloor Volcanism
<p>Included are the data and the source par files for the manuscript "No Magmatic Driving Force for Seafloor Volcanism" by Green et al. (in press). Par files show input parameters for each model included in the main results. Also included are all generated .dat files, named to match their corresponding par file.</p>
Repository of smell comments
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OREN Knowledge Repository: A database of programs, best practices and general data for rural entrepreneurs
<p>The OREN project, <span>Multi-Stakeholder Platform for Rural Entrepreneurs (Project reference No: 2021-1-IT02-KA220-ADU-000033510), is funded by ERASMUS+ through the Italian National Agency Indire and upon completion, expected in 2024, OREN will provide entrepreneurs in the rural sector with a unique toolbox. The tools provided will include courses, guidelines and, most importantly, an integrated platform with business tools where it will be possible to control and coordinate a Business Plan. </span></p> <p><span>Part of the material that was developed for the project were data on best practice for rural entreprises. The current knowledge repository contains two files:</span></p> <p><span>1) OREN_Data_repository_PR1.xlsx: It contains 3 tabs</span></p> <ul> <li><span>IMPORTANT FACTORS: The tab contains important factors that emerged from a desk research on the partner countries of the OREN project. </span></li> <li><span>CASE STUDIES: The tab contains relevant case studies from the partner countries of the OREN project</span></li> <li><span>PROGRAMS: The tab contains relevant programs about rural entrepreneurship from the partner countries of the OREN project</span></li> </ul> <p><span>2) OREN_Data_repository_PR2.xlsx: It contains </span><span>data were grouped into two categories:</span></p> <ul> <li><span>General Data on the area of interest of the partner’s country</span></li> <li>Specific data on rural organizations in the area of interest.</li> </ul> <p> </p> <p> </p>
Heat demand profiles from the When2Heat project repository
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Repository for "Direct simulation and machine learning structure identification unravel soft martensitic transformation and twinning dynamics"
<p>These files contain data and generation codes for figures, a numerical code for the analysis of structure factors, and a numerical code for the simulation, in an article "Direct simulation and machine learning structure identification unravel soft martensitic transformation and twinning dynamics".</p>
X-COBOL: A Dataset of Open-Source COBOL repositories
<p>A dataset of 5195 open source COBOL files across 168 repositories</p>
Netzero2040: Reaching climate neutrality in Austria by 2040: engaging stakeholders for model-supported scenario development. Data repository
<p><span>This repository contains scenario results in pyam format, qualitative scenario narratives and drivers identified by stakeholders for the NetZero2040 project.</span></p> <p><span>NetZero2040 developed the first independent scenarios achieving climate-neutrality in Austria by 2040. We improve on previous analyses by employing a structured co-creation process involving stakeholders and modellers, simultaneously modelling the whole energy system and the electricity system in great detail, creating shared visions of a climate-neutral future. Results are openly available and have been broadly disseminated in the scientific community and to the public. Our scenarios are differentiated by assumptions on energy demand and imports of energy carriers. They show that a rapid electrification of transport and heating, in combination with a build out rate of renewable energies which is well above historical maxima in the Austrian power system, allow significant emission reductions until 2030, and that these measures are consistently required in all scenarios. However, after 2030 scenarios diverge and uncertainty about the most cost-efficient transformation measures prevail. </span></p>
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.