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Dataset results
747 results for “Open Data”
Jemadia suekentonmiller Grishin,2014 [open data]
A quick and simple 3D model made with open access data from the Natural History Museum London's Data Portal: https://data.nhm.ac.uk/object/0713709e-fb9a-4dde-b072-ad9df1598464/1565740800000 UV layout in Photoshop, modelling and animation in Blender 2.79. Check out the amazing 3D models from the Museum itself here: https://sketchfab.com/NHM_Imaging Source: Objaverse 1.0 / Sketchfab
Sociotechnical Dynamics in Open Source Smart Contract Repositories: An Exploratory Data Analysis of Curated High Market Value Projects
<p>This is the replication package for the paper “Sociotechnical Dynamics in Open Source Smart Contract Repositories: An Exploratory Data Analysis of Curated High Market Value Projects”.</p> <p>In project_curation_selection, there is the curation process of the 100 selected projects including the identification of GitHub repositories and classification of evolution scenarios. </p> <p>In distribution_commits_issues_contributors_market_value_before_after_deploy, data collection from GitHub projects includes the distribution of total commits, contributors, and issues before and after deployment of each investigated project. </p> <p>In analysis_commit_messages, there is qualitative analysis of commit message content from all investigated projects. </p> <p>In the analysis_contributors section, the data focuses on analyzing the profiles of each GitHub contributor involved in the investigated projects.</p> <p>In analysis_market_value_by_project, data refers to the market value and volume of each investigated project. </p> <p>In codes, there are scripts used to obtain the analyzed data.</p> <p> </p>
Dataset of the study "Open Access and Data Sharing in Cancer Stem Cells research"
<p>Dataset of the study "Open Access and Data Sharing in Cancer Stem Cells research"</p>
ATLAS OmniFold 24-Dimensional Z+jets Open Data
<div>These datasets contain the unbinned, twenty-four-dimensional ATLAS Z+jets differential cross-section measurement presented in <a href="https://cds.cern.ch/record/2899105" target="_blank" rel="noopener">CERN-EP-2024-132</a>. The measurements are presented as Pandas DataFrames in HDF5 format, and they are accompanied by MC predictions formatted as Numpy arrays. Measurements are provided both for "pseudo-data", i.e. a validation MC sample with truth- and reco-level quantities that has been reweighted to match data, as well as real data. </div> <div> </div> <div><strong>Important: </strong>Before using this data, please consult the <a href="https://gitlab.cern.ch/atlas-physics/public/sm-z-jets-omnifold-2024" target="_blank" rel="noopener">documentation & example notebooks</a>. </div> <div> </div> <div>The signal process is inclusive Z→μμ production with a fiducial region defined in the boosted regime: p_T^μμ > 200 GeV. </div> <div> </div> <div>In total, 24 Z+jets kinematic observables are measured:</div> <ul> <li>p_T, η, and ϕ of each of the two muons (6 observables)</li> <li>The p_T and rapidity of the dimuon system: p_T^μμ, y^μμ (2 observables)</li> <li>The 4-momenta (p_T, y, ϕ, m) of the two leading charged particle jets (8 observables)</li> <li>The number of (charged) constituents and n-subjettiness quantities τ_1, τ_2, and τ_3 for each of the same two jets (8 observables)</li> </ul> <div>The dimuon system p_T and y can be obtained from the muon kinematics, but they are included for convenience. The observables are labeled by 1 and 2 for leading and subleading in p_T, respectively. </div>
North Campus Open Space (NCOS) Monthly Bird Survey Data (2017-2023)
<p>Bird surveys are completed once a month by Cheadle Center staff and associates. Each survey starts 45 minutes after sunrise and lasts about 2 hours. The survey has at least 4 experienced birders- 2 that survey the eastern side and 2 that survey the western route (exact routes can be found on ArcGIS online). Surveyors use a combination of binoculars, scopes, and listening for calls to identify species. A point is placed on the map to the best accuracy possible using the ArcGIS app field maps or collector. The survey is also set up to record the surveyors' location every 30 seconds as a way to track the survey route. When there are large groups of birds, 10 or more, an approximate number is decided between the two surveyors. If a bird is seen multiple times along the route, it is labeled a repeat. The two groups met up after the survey and discussed what was seen. Any birds that are expected to be a repeat were changed to note that they were a repeat within the dataset. Environmental data such as cloud cover, water level, and temperature are recorded at the beginning and end of the survey.</p> <p>This data is an aggregation that contains the number of each species spotted on site each month. If you are interested in the birds' exact location, you can download individual monthly data from ArcGIS Online.</p>
Fault-Tolerant Computing with Single Qudit Encoding in a Molecular Spin. Open data set
<div> <p>Data supporting the original figures 2, 3 and 4 (ESI) of the related manuscript.</p> </div>
Dephasing-tolerant quantum sensing of transverse magnetic fields with spin qudits. Open data set
<p>Data supporting Figs. 1, 2, 3, 4 of the related manuscript.</p>
Double inverse nanotapers for efficient light coupling to integrated photonic devices - Open Data
<p>Available data for the manuscript "Double inverse nanotapers for efficient light coupling to integrated photonic devices"</p>
pyKNEEr: An image analysis workflow for open and reproducible research on femoral knee cartilage - Validation data
<p>Image data used in the paper introducing pyKNEEr. Explanations about these data are in the <a href="https://github.com/sbonaretti/pyKNEEr/tree/master/publication">GitHub</a> repository</p> <p>Changes in version 0.2.0: </p> <p>- Added inHouse images </p> <p>- Segmented images casted to int16 for smaller file size</p>
Demystifying Open Science and Research Data Management: A practical workshop for researchers
<p>Have you ever wondered why is everyone discussing about Open Science and Research Data Management (RDM) these days? Good Research Data Management (RDM) is crucial for reproducible and robust scientific research. Consequently, more and more funding bodies, governments, research institutions and other agencies have emphasised the value and importance of good data management and introduced policies on data management and sharing. However, it is easier said than done.<br> Like most researchers you might have more questions than answers about the topic. You are not alone! Come join us on the 24th at an interactive workshop where you can understand the why and how of open science and research data management in practical terms.</p> <p> </p> <p>This zenodo entry is a recording of the workshop described above. </p>
Open Data for SLR Artificial Intelligence and Electric Vehicles
Open the record for dataset details and reuse information.
Weak Exchange Interactions in Multispin Systems: EPR Studies of Metalloporphyrins Decorated with {Cr7Ni} Rings. Open data set
<p>Data supporting the original figures 2, 3, 4b, 5, and 6 of the related publication.</p>
Toronto Emergency Response - Open Data
<p>This upload contains data and documentation for the Python analysis undertaken in Google Colab as part of Episode 1 of the webinar series, conducted by Sambodhi's Center for Health Systems Research and Implementation (CHSRI). You can find the link to the Google Colab notebook <a href="https://colab.research.google.com/drive/1dFBokIJytUe1qWJePoTof1u2rxkIhaHQ?usp=sharing" target="_blank" rel="noopener">here.</a></p> <p>All the data uploaded here is open data published by the Toronto Police Public Safety Data Portal and the Ontario Ministry of Health. </p>
Open Data ICSEEIS Paper 1_Haryadi Sarjono
Open the record for dataset details and reuse information.
Many-Body Models for Chirality-Induced Spin Selectivity in Electron Transfer. Open data set
<p>Data supporting the original figures 1, 2, 3 and 4 of the related publication.</p>
Datasets and scripts related to the paper: "*Can Generative AI Help us in Open Coding of Software Engineering Data?*"
<p>This replication package contains datasets and scripts related to the paper: "<em>Can Generative AI Help us in Open Coding of Software Engineering Data?</em>"</p> <p>The replication package is organized into two directories:</p> <ul> <li> <p><code>manual_analysis</code>: This directory contains all sheets used to perform the manual analysis for RQ1, RQ2, and RQ3.</p> </li> <li> <p><code>stats</code>: This directory contains all datasets, scripts, and results metrics used for the quantitative analyses of RQ1 and RQ2.</p> </li> </ul> <p>In the following, we describe the content of each directory:</p> <h2>manual_analysis</h2> <ul> <li> <p><code>manual_analysis_rq1</code>: This directory contains all sheets used to perform manual analysis for RQ1 (independent and incremental coding).</p> <ul> <li> <p>The sub-directory <code>incremental_coding</code> contains .csv files for all datasets (<code>DL_Faults_COMMIT_incremental.csv</code>, <code>DL_Faults_ISSUE_incremental.csv</code>, <code>DL_Fault_SO_incremental.csv</code>, <code>DRL_Challenges_incremental.csv</code> and <code>Functional_incremental.csv</code>). All these .csv files contain the following columns:</p> <ul> <li><em>Link</em>: The link to the instances</li> <li><em>Prompt</em>: Prompt used as input to GPT-4-Turbo</li> <li><em>ID</em>: Instance ID</li> <li><em>FinalTag</em>: Tag assigned by the human in the original paper</li> <li><em>Chatgpt_output_memory</em>: Output of GPT-4-Turbo with incremental coding</li> <li><em>Chatgpt_output_memory_clean</em>: (only for the DL Faults datasets) output of GPT-4-Turbo considering only the label assigned, excluding the text</li> <li><em>Author1</em>: Label assigned by the first author</li> <li><em>Author2</em>: Label assigned by the second author</li> <li><em>FinalOutput</em>: Label assigned after the resolution of the conflicts</li> </ul> </li> <li> <p>The sub-directory <code>independent_coding</code> contains .csv files for all datasets (<code>DL_Faults_COMMIT_independent.csv</code>, <code>DL_Faults_ISSUE_ independent.csv</code>, <code>DL_Fault_SO_ independent.csv</code>, <code>DRL_Challenges_ independent.csv</code> and <code>Functional_ independent.csv</code>), containing the following columns:</p> <ul> <li><em>Link</em>: The link to the instances</li> <li><em>Prompt</em>: Prompt used as input to GPT-4-Turbo</li> <li><em>ID</em>: Specific ID for the instance</li> <li><em>FinalTag</em>: Tag assigned by the human in the original paper</li> <li><em>Chatgpt_output</em>: Output of GPT-4-Turbo with independent coding</li> <li><em>Chatgpt_output_clean</em>: (only for DL Faults datasets) output of GPT-4-Turbo considering only the label assigned, excluding the text</li> <li><em>Author1</em>: Label assigned by the first author</li> <li><em>Author2</em>: Label assigned by the second author</li> <li><em>FinalOutput</em>: Label assigned after the resolution of the conflicts.</li> </ul> </li> <li> <p>Also, the sub-directory contains sheets with inconsistencies after resolving conflicts. The directory <code>inconsistency_incremental_coding</code> contains .csv files with the following columns:</p> <ul> <li><em>Dataset</em>: The dataset considered</li> <li><em>Human</em>: The label assigned by the human in the original paper</li> <li><em>Machine</em>: The label assigned by GPT-4-Turbo</li> <li><em>Classification</em>: The final label assigned by the authors after resolving the conflicts. Multiple classifications for a single instance are separated by a comma “,”</li> <li><em>Final</em>: final label assigned after the resolution of the incompatibilities</li> </ul> </li> <li> <p>Similarly, the sub-directory <code>inconsistency_independent_coding</code> contains a .csv file with the same columns as before, but this is for the case of independent coding.</p> </li> </ul> </li> <li> <p><code>manual_analysis_rq2</code>: This directory contains .csv files for all datasets (<code>DL_Faults_redundant_tag.csv</code>, <code>DRL_Challenges_redundant_tag.csv</code>, <code>Functional_redundant_tag.csv</code>) to perform manual analysis for RQ2.</p> <ul> <li> <p>The <code>DL_Faults_redundant_tag.csv</code> file contains the following columns:</p> <ul> <li><em>Tags Redundant</em>: tags identified as redundant by GPT-4-Turbo</li> <li><em>Matched</em>: inspection by the authors to see if the tags are redundant matching or not</li> <li><em>FinalTag</em>: final tag assigned by the authors after the resolution of the conflict</li> </ul> </li> <li> <p>The <code>Functional_redundant_tag.csv</code> file contains the same columns as before</p> </li> <li> <p>The <code>DRL_Challenges_redundant_tag.csv</code> file is organized as follows:</p> <ul> <li><em>Tags Suggested</em>: The final tag suggested by GPT-4-Turbo</li> <li><em>Tags Redundant</em>: tags identified as redundant by GPT-4-Turbo</li> <li><em>Matched</em>: inspection by the authors to see if the tags redundant matching or not with the tags suggested</li> <li><em>FinalTag</em>: final tag assigned by the authors after the resolution of the conflict</li> </ul> </li> <li> <p>The sub-directory <code>code_consolidation_mapping_overview</code> contains .csv files (<code>DL_Faults_rq2_overview.csv</code>, <code>DRL_Challenges_rq2_overview.csv</code>, <code>Functional_rq2_overview.csv</code>) organized as follows:</p> <ul> <li><em>Initial_Tags</em>: list of the unique initial tags assigned by GPT-4-Turbo for each dataset</li> <li><em>Mapped_tags</em>: list of tags mapped by GPT-4-Turbo</li> <li><em>Unmatched_tags</em>: list of unmatched tags by GPT-4-Turbo</li> <li><em>Aggregating_tags</em>: list of consolidated tags</li> <li><em>Final_tags</em>: list of final tags after the consolidation task</li> </ul> </li> </ul> </li> <li> <p><code>prompt_for_each_rq</code>: This directory contains: - (i) the history of prompts used in each dataset (<code>prompts_history.txt</code>) -(ii) all final prompt used for the analysis of each dataset, prompt used for incremental coding, prompt used in rq2 to consolidate redundant codes, prompt used in rq3 to create taxonomy (<code>generic_prompt.txt</code>) -(iii) all .csv files in which there are indicate, for each dataset, the link and the prompt used (<code>prompt_DL_Faults_COMMIT.csv</code>, <code>prompt_DL_Faults_ISSUE.csv</code>, <code>prompt_DL_Faults_SO.csv</code>, <code>prompt_DRL_Challenges.csv</code>). For the Functional Dataset .csv file contains, instead, Question, Answer and Prompt used (<code>prompt_Functional.csv</code>)</p> </li> <li> <p><code>rq3</code>: This directory contains the taxonomies obtained from GPT-4-Turbo for the DL Faults and for the DRL Challenges (<code>taxonomy_DL_Faults.txt</code>,<code>taxonomy_DRL_Challenges.txt</code>)</p> </li> </ul> <h2>stats</h2> <ul> <li> <p><code>RQ1</code>: contains script and datasets used to perform metrics for RQ1. The analysis calculates all possible combinations between Matched, More Abstract, More Specific, and Unmatched.</p> <ul> <li><code>RQ1_Stats.ipynb</code> is a Python Jupyter nooteook to compute the RQ1 metrics. To use it, as explained in the notebook, it is necessary to change the values of variables contained in the first code block.</li> <li><code>independent-prompting</code>: Contains the datasets related to the independent prompting. Each line contains the following fields: <ul> <li><em>Link</em>: Link to the artifact being tagged</li> <li><em>Prompt</em>: Prompt sent to GPT-4-Turbo</li> <li><em>FinalTag</em>: Artifact coding from the replicated study</li> <li><em>chatgpt_output_text</em>: GPT-4-Turbo output</li> <li><em>chatgpt_output</em>: Codes parsed from the GPT-4-Turbo output</li> <li><em>Author1</em>: Annotator 1 evaluation of the coding</li> <li><em>Author2</em>: Annotator 2 evaluation of the coding</li> <li><em>FinalOutput</em>: Consolidated evaluation</li> </ul> </li> <li><code>incremental-prompting</code>: Contains the datasets related to the incremental prompting (same format as independent prompting)</li> <li><code>results</code>: contains files for the RQ1 quantitative results. The files are named <code>RQ1\_<<Dataset>>\_<<Prompt method>>\_<<ExcludingNegative>>\_<<MetricAggregation>>.csv</code>, where <em>Dataset</em> is the dataset name, <em>Prompt method</em> indicates whether results are for independent or incremental prompting, <em>Excluding Negatives</em> (for datasets where this applies) whether results have been obtained by excluding negative instances, and <em>MetricAggregation</em> (where it applies) how metrics have been aggregated (macro or weighted average). The files report columns indicating the <em>Dataset</em>, the <em>Matching type</em>, the <em>Accuracy</em>, <em>Precision</em>, <em>Recall</em>, <em>F1 Score</em>, and <em>Cohen's Kappa</em>.</li> </ul> </li> <li> <p><code>RQ2</code>: contains the script used to perform metrics for RQ2, the datasets it uses, and its output.</p> <ul> <li><code>RQ2_SetStats.ipynb</code> is the Python Jupyter notebook to perform the analyses. The scripts takes as input the following types of files, contained in the directory contains the script used to perform the metrics for RQ2. The script takes in input:</li> <li>RQ1 Data Files (<code>RQ1_DLFaults_Issues.csv</code>, <code>RQ1_DLFaults_Commits.csv</code>, and <code>RQ1_DLFaults_SO.csv</code>, joined in a single .csv <code>RQ1_DLFaults.csv</code>). These are the same files used in RQ1.</li> <li>Mapping Files (<code>RQ2_Mappings_DRL.csv</code>, <code>RQ2_Mappings_Functional.csv</code>, <code>RQ2_Mappings_DLFaults.csv</code>). These contain the mappings between human tags (<em>HumanTags</em>), GPT-4-Turbo tags (<em>Final Tags</em>), with indicated the type of matching (<em>MatchType</em>).</li> <li>Additional codes creating during the consolidation (<code>RQ2_newCodes_DRL.csv</code>, <code>RQ2_newCodes_Functional.csv</code>, <code>RQ2_newCodes_DLFaults.csv</code>), annotated with the matching: <em>new code</em>,<em>old code</em>,<em>human code</em>,<em>match type</em></li> <li>Set files (<code>RQ2_Sets_DRL.csv</code>, <code>RQ2_Sets_Functional.csv</code>, <code>RQ2_Sets_DLFaults.csv</code>). Each file contains the following columns: <ul> <li><em>HumanTags</em>: List of tags from the original dataset</li> <li><em>InitialTags</em>: Set of tags from RQ1,</li> <li><em>ConsolidatedTags</em>: Tags that have been consolidated,</li> <li><em>FinalTags</em>: Final set of tags (results of RQ2, used in RQ3)</li> <li><em>NewTags</em>: New tags created during consolidation</li> </ul> </li> <li><code>RQ2_Set_Metrics.csv</code>: Reports the RQ2 output metrics (Precision, Recall, F1-Score, Jaccard).</li> </ul> </li> </ul>
Data from: Feral cats are better killers in open habitats, revealed by animal-borne video
One of the key gaps in understanding the impacts of predation by small mammalian predators on prey is how habitat structure affects the hunting success of small predators, such as feral cats. These effects are poorly understood due to the difficulty of observing actual hunting behaviours. We attached collar-mounted video cameras to feral cats living in a tropical savanna environment in northern Australia, and measured variation in hunting success among different microhabitats (open areas, dense grass and complex rocks). From 89 hours of footage, we recorded 101 hunting events, of which 32 were successful. Of these kills, 28% were not eaten. Hunting success was highly dependent on microhabitat structure surrounding prey, increasing from 17% in habitats with dense grass or complex rocks to 70% in open areas. This research shows that habitat structure has a profound influence on the impacts of small predators on their prey. This has broad implications for management of vegetation and disturbance processes (like fire and grazing) in areas where feral cats threaten native fauna. Maintaining complex vegetation cover can reduce predation rates of small prey species from feral cat predation.
Open Datasets - available file for Design of Experimental (RSM) model, ANOVA table, SEM, VSM and Zeta potecial data for the nanocrystal clusters based on iron oxide.
<p>The open-access data from the statistical model for the design of experiment optimization and as well as the experimental data are available for the community. The data are based on an obtained results published in the article: https://www.mdpi.com/2079-4991/11/2/360</p>
Data from: Active sound production of scarab beetle larvae opens up new possibilities for species-specific pest monitoring in soils
Root-feeding Scarabaeidae larvae can pose a serious threat to agricultural and forest ecosystems, but many details of larval ecology are still unknown. We developed an acoustic data analysis method based on active sound production by larvae (i.e. stridulations) for gaining new insights into larval ecology. In a laboratory study, third instar larvae of the Common Cockchafer (Melolontha melolontha) (n = 38) and the Forest Cockchafer (M. hippocastani) (n = 15) kept in soil-filled containers were acoustically monitored for 5 min each, resulting in the first known stridulation recordings for each species. Subsequent continuous monitoring of three M. hippocastani larvae over several hours showed that a single larva could stridulate more than 70 times per hour, and stridulation rates increased drastically with increasing larval abundance. The new fractal dimension-based data analysis method automatically detected audio sections with stridulations and provided a semi-quantitative estimate of stridulation activity. It is the first data analysis method specifically targeting Scarabaeidae larvae stridulations in soils, enabling for the first time non-invasive species-specific pest monitoring.
FIGURE 2. Oreocharis yunnanensis. A. Habit. B. Flower. C. Opened corolla showing stamens and staminode. D. Calyx and pistil with disc. E. Calyx. F. Bracts. G in Additional notes on Oreocharis yunnanensis, a species of Gesneriaceae from southern Yunnan, China, including morphological and molecular data
FIGURE 2. Oreocharis yunnanensis. A. Habit. B. Flower. C. Opened corolla showing stamens and staminode. D. Calyx and pistil with disc. E. Calyx. F. Bracts. G. Cross section of ovary. Illustration by Yun-Xi Zhu based on the holotype Yun-Hong Tan 6925.
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.