Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,034
datasets available to search
ShareScore release 0.9.0
Dataset results
1,034 results for “Empirical”
FIGURE 5. Neodialineura spp. N in Revision of the stiletto fly genus Neodialineura Mann (Diptera: Therevidae): an empirical example of cybertaxonomy
FIGURE 5. Neodialineura spp. N. litura sp. nov.: A, epandrium and tergite 8; B, gonocoxites, ventral; C, same, lateral; D, aedeagus, lateral; E, same, dorsal; N. saxatilis (White): F, gonocoxites and aedeagus with epandrium removed, dorsal; N. bifaria sp. nov.: G, aedeagus lateral; H, same, dorsal; N. striatithorax Mann: I, male head, lateral; N. trichidion sp. nov.: J, male head, lateral. Scale lines: 0.2 mm.
FIGURE 10 in Revision of the stiletto fly genus Neodialineura Mann (Diptera: Therevidae): an empirical example of cybertaxonomy
FIGURE 10. Neodialineura ataxia sp. nov.: Paratype female head, anterior [Morphbank]. Head width= 1.0 mm.
FIGURE 1 in Revision of the stiletto fly genus Neodialineura Mann (Diptera: Therevidae): an empirical example of cybertaxonomy
FIGURE 1. Neodialineura striatithorax Mann, female, Brisbane, Queensland [Morphbank]. Body length= 6.5 mm. (Photo: Anthony O'Toole, University of Queensland).
FIGURE 4 in Revision of the stiletto fly genus Neodialineura Mann (Diptera: Therevidae): an empirical example of cybertaxonomy
FIGURE 4. Neodialineura spinosa sp. nov., female, Yanchep, Western Australia [Morphbank]. Body length= 7.0 mm. (Photo: S.L. Winterton).
FIGURE 2 in Revision of the stiletto fly genus Neodialineura Mann (Diptera: Therevidae): an empirical example of cybertaxonomy
FIGURE 2. Neodialineura nitens (White), male, Warrumbungle National Park, New South Wales [Morphbank]. Body length= 5.0 mm. (Photo: S.L. Winterton).
FIGURE 11, 12 in Revision of the stiletto fly genus Neodialineura Mann (Diptera: Therevidae): an empirical example of cybertaxonomy
FIGURE 11, 12. Neodialineura atmis sp. nov.: Holotype female. 11, dorsal [Morphbank]; 12, lateral [Morphbank]. Body length= 8.0 mm.
FIGURE 8, 9 in Revision of the stiletto fly genus Neodialineura Mann (Diptera: Therevidae): an empirical example of cybertaxonomy
FIGURE 8, 9. Neodialineura ataxia sp. nov.: Paratype female. 8, dorsal [Morphbank]; 9, lateral [Morphbank]. Body length= 5.0 mm.
FIGURE 6, 7 in Revision of the stiletto fly genus Neodialineura Mann (Diptera: Therevidae): an empirical example of cybertaxonomy
FIGURE 6, 7. Neodialineura ataxia sp. nov.: Holotype male. 6, dorsal [Morphbank]; 7, lateral [Morphbank]. Body length= 3.8 mm.
All Spectra Associated with "An Empirical Template Library of Stellar Spectra for a Wide Range of Spectral Classes, Luminosity Classes, and Metallicities Using SDSS BOSS Spectra"
<p>Zip files of all the individual SDSS BOSS spectra. They are sorted by metallicity, luminosity class and spectral type, where each zip file contains all the original SDSS spectra to be co-added into each template. The fits files are in the following format: The primary HDU contains comments about the right ascension, declination and other useful object information. The first table extension contains the spectrum information (wavelength, flux, inverse variance, etc.) The second table extension contains other parameter information (object type, flags, and more). </p> <p> </p>
When do agents outperform centralized algorithms? - A systematic empirical evaluation in logistics - datasets and results v1.1.0
<p>This directory contains the data and results that were used and obtained during the realization of the following paper:</p> <p>When do agents outperform centralized algorithms? - A systematic empirical evaluation in logistics. Rinde R.S. van Lon and Tom Holvoet. Journal of Autonomous Agents and Multi-Agent Systems (2017).</p> <p>This is version v1.1.0. At the time of writing the paper is not yet accepted, some of the details (e.g. title, date of publication) are subject to change. The code that has been used can be found in this repository: https://github.com/rinde/vanLon17-JAAMAS-code </p> <p>This directory has the following structure:</p> <p>- makefile - script that can be used to run the analyses. The most important commands are:<br> - all - runs all analyses.<br> - paper-deps - runs only the analyses that were directly used in the paper.<br> - additional-analysis - runs several additional analyses.</p> <p>- readme.txt - this file.</p> <p>- results - contains all R-scripts used to analyse the data<br> - data/main - contains the results of the main result.<br> - data/mas-tuning - contains the results of the MAS tuning experiments.<br> - data/optaplanner-tuning-gendreau - contains the results of the OptaPlanner tuning experiments.<br> - data/sensitivity - contains the results of the simulator sensitivity experiments (using OptaPlanner algorithms).</p> <p>- scenarios<br> - gendreau2006 - contains the dataset from the Gendreau et al. [1] paper, used for the OptaPlanner tuning experiments.<br> - mas-tuning-dataset - contains the dataset that was used for tuning the MAS. This was generated using the code from https://github.com/rinde/vanLon17-JAAMAS-code<br> - vanLonHolvoet15-adapted-to-4-hours - contains the dataset that was used for the main experiment. This is an adapted version of the dataset from [2]. This version has a duration of 4 hours and requires a real-time simulator. It was generated using the code from https://github.com/rinde/vanLon17-JAAMAS-code </p> <p>[1] Michel Gendreau, Francois Guertin, Jean-Yves Potvin, and René Séguin. Neighborhood search heuristics for a dynamic vehicle dispatching problem with pick- ups and deliveries. Transportation Research Part C: Emerging Technologies, 14 (3):157–174, 2006. ISSN 0968090X. doi:10.1016/j.trc.2006.03.002.</p> <p>[2] Rinde R. S. van Lon and Tom Holvoet. Towards systematic evaluation of multi-agent systems in large scale and dynamic logistics. In Qingliang Chen, Paolo Torroni, Serena Villata, Jane Hsu, and Andrea Omicini, editors, PRIMA 2015: Principles and Practice of Multi-Agent Systems: 18th International Conference, Bertinoro, Italy, October 26-30, 2015, Proceedings, pages 248–264. Springer In- ternational Publishing, Cham, 2015. ISBN 978-3-319-25524-8. doi:10.1007/978- 3-319-25524-8 16.</p>
Dataset for the Paper: "How Do OSS Developers Reuse Architectural Solutions from Q&A Sites: An Empirical Study"
<p>This package contains the dataset of the paper "How Do OSS Developers Reuse Architectural Solutions from Q&A Sites: An Empirical Study":</p> <p><strong>(1) Commits and Issues.xlsx</strong></p> <p>This file includes 984 commits and issues for the mining study.</p> <p><strong>(2) Survey Questionnaire (Customized Invitation Email).docx</strong></p> <p>This file provides the customized email used to invite each survey participant.</p> <p><strong>(3) Survey Questionnaire.xlsx</strong></p> <p>This file includes 12 survey questions used to collect the responses from the participants.</p> <p><strong>(4) Survey Responses.</strong><strong>xlsx</strong></p> <p>This file includes 227 valid responses and the results of data extraction and analysis for the survey study.</p> <p><strong>(5) Data Extraction and Analysis.mx20</strong></p> <p>This file contains the results of data extraction and analysis for the mining study, which were analyzed by the MAXQDA tool. The file can be opened by MAXQDA 2020 or higher versions, which are available at https://www.maxqda.com/. You may also use the free 14 days trial version of MAXQDA 2020, which is available at https://www.maxqda.com/trial for download.</p>
Artifact of the paper "An Empirical Investigation on the Challenges in Scientific Workflow Systems Development"
<p><strong><span>Scientific Workflow Systems (SWSs)</span></strong><span> play a critical role in the contemporary scientific landscape, significantly enriching research endeavors by augmenting productivity and fostering collaboration</span><span>, SWSs</span><span> elevate the standard of scholarly inquiry, fortifying its pillars of reproducibility and ethical adherence. Essentially, they </span><span>serve as</span><span> the bedrock upon which efficient, transparent, and impactful research </span><span>is built</span><span>, propelling knowledge and innovation across diverse fields. SWSs accomplish mundane yet essential tasks intrinsic to scientific inquiry—ranging from data acquisition to analysis and reporting. By liberating researchers from the shackles of manual labor, SWSs enable them to channel their energies toward more intellectually demanding pursuits, thereby enhancing the pace and quality of research outcomes. Moreover, SWSs wield a formidable influence in standardizing workflows across research cohorts, instilling a sense of uniformity in experimental methodologies and data-handling practices. This standardization not only cultivates a culture of rigor and coherence but also fosters cross-disciplinary dialogue and collaboration.</span></p> <p>Integral to the operation of SWSs is their capacity to integrate diverse tools, software, and data sources, effectively functioning as centralized hubs for research management. This integration expedites the research process and facilitates seamless data exchange and interoperability—a pivotal asset in an era characterized by the deluge of data and the imperative of interdisciplinary collaboration. Furthermore, SWSs afford researchers and project managers real-time insights into the progress of research endeavors, empowering them to identify bottlenecks, allocate resources judiciously, and optimize workflow execution. This granular oversight enhances project transparency and accountability and serves as a catalyst for informed decision-making.</p> <p>Crucially, SWSs are engineered to accommodate the complexities inherent in scientific inquiry, adeptly handling vast volumes of data and supporting parallel processing to meet the evolving demands of research projects. This scalability underscores their adaptability to diverse research paradigms, ensuring their relevance across a spectrum of scientific disciplines. Facilitating collaboration across geographic and temporal divides, SWSs offer a suite of collaborative features—including version control, shared workspaces, and communication tools—that transcend the constraints of physical proximity. By fostering a culture of inclusivity and knowledge exchange, SWSs catalyze innovation and synergy among distributed research teams.</p> <p>Moreover, SWSs serve as custodians of reproducibility, meticulously documenting each facet of the research workflow—from data sources to analysis methods—thus safeguarding the integrity of scientific inquiry. This commitment to transparency and methodological rigor underpins the credibility of research findings, engendering trust within the scientific community and beyond. The customizable nature of SWSs empowers research teams to tailor their workflows to suit their unique needs and preferences, further amplifying their utility and versatility. In essence, SWSs emerge not merely as tools of convenience but as indispensable allies in the relentless pursuit of scientific excellence.</p> <p>Numerous developers actively participate in the advancement of SWSs through diverse roles, including designing system architectures to ensure flexibility and performance, developing algorithms for data processing and analysis, crafting user-friendly interfaces, handling backend logic, integrating with external tools, and ensuring quality, security, and compliance. They address challenges such as optimizing performance and scalability by leveraging parallel processing and distributed computing techniques. To tackle these diverse tasks, developers encounter numerous challenges, often turning to crowd-sourced platforms like Stack Overflow and GitHub to discuss and address them. Stack Overflow serves as a vital resource for developers to seek solutions, learn new technologies, validate best practices, and engage with the programming community. Similarly, GitHub facilitates collaborative development by allowing developers to report problems, propose enhancements, and contribute to open-source projects. Our research draws insights from Stack Overflow discussions, GitHub issues, and pull request reports related to SWSs, reflecting the dynamic and collaborative nature of software development in this domain.</p>
Supplementary files of Ament & Almeida (submitted) Is additive coding useful for morphological phylogenetic analyses? An empirical evaluation
Open the record for dataset details and reuse information.
Replication Package for the Paper: "How Do Users Revise Architectural Related Questions on Stack Overflow: An Empirical Study"
<p>This is the replication package for the paper: "How Do Users Revise Architectural Related Questions on Stack Overflow: An Empirical Study". In the following, we provide a brief description of the folders and files:</p> <p><strong>(1) raw data</strong></p> <p>The raw data folder contains the retrieved 36,417 posts and the SQL query used for retrieving ARPs from Stack Overflow through the query interface provided by Stack Exchange.</p> <p><strong>(2) filtered ARPs</strong></p> <p>The filtered ARPs folder contains 13,205 filtered candidates ARPs from the retrieved 36,417 posts and the results of data analysis for the first RQ (i.e., RQ1).</p> <p><strong>(3) randomly selected posts and labeling results</strong></p> <p>The randomly selected posts and labeling results folder contains 1,068 randomly selected posts and their labeling results (i.e., 21 ARPs, wherein 14.3%, 3 out of 21 ARPs, do not contain “architect*” terms and 85.7%, 18 out of 21 APRs, contain “architect*” terms).</p> <p><strong>(4) relevant ARPs for answering RQs</strong></p> <p>The relevant ARPs for answering RQs folder contains 4,114 ARPs with revision information for answering the last three RQs (i.e., RQ2, RQ3, and RQ4).</p> <p><strong>(5) interview responses</strong></p> <p>The interview responses folder contains 11 collected interview responses from software practitioners. These responses were gathered to evaluate the identified categories related to ARQ revisions.</p> <p><strong>(6) data extraction and analysis</strong></p> <p>The data extraction and analysis folder contains the MAXQDA file. Data Labeling & Encoding for RQs.mx20 is the results of data labeling and encoding for RQ2, RQ3, and RQ4, which were analyzed by the MAXQDA tool. This file can be opened by MAXQDA 2020 or higher versions, which are available at https://www.maxqda.com/ for download. You may also use the free 14 days trial version of MAXQDA 2020, which is available at https://www.maxqda.com/trial for download.</p>
Data and Code 1/3 "Public Policies and Global Forest Conservation: Empirical Evidence from National Borders"
<p>Data and Code "Public Policies and Global Forest Conservation: Empirical Evidence from National Borders", published in Global Environmental Change, authored by David Wuepper (1,2)*, Thomas Crowther (3), Thomas Lauber (3), Devin Routh (3,4), Solen Le Clec'h (5), Rachael D. Garrett (6,7), Jan Börner (8,9). (1 Land Economics Group, University of Bonn, Germany, 2 Agricultural Economics and Policy Group, ETH Zurich, Switzerland, 3 Global Ecosystem Ecology Group, ETH Zurich, Switzerland, 4 SIT Group, Uiversity of Zurich, Switzerland, 5 Environmental Systems Analysis Group, Wageningen University, Netherlands, 6 Conservation and Development, Cambridge University, UK, 7 Environmental Policy Lab, ETH Zürich, Switzerland, 8 Institute for Food and Resource Economics, University of Bonn, Germany, 9 Center for Development Research, University of Bonn, Germany. </p>
Data and Code 2/3 "Public Policies and Global Forest Conservation: Empirical Evidence from National Borders"
<p>Data and Code "Public Policies and Global Forest Conservation: Empirical Evidence from National Borders", published in Global Environmental Change, authored by David Wuepper (1,2)*, Thomas Crowther (3), Thomas Lauber (3), Devin Routh (3,4), Solen Le Clec'h (5), Rachael D. Garrett (6,7), Jan Börner (8,9). (1 Land Economics Group, University of Bonn, Germany, 2 Agricultural Economics and Policy Group, ETH Zurich, Switzerland, 3 Global Ecosystem Ecology Group, ETH Zurich, Switzerland, 4 SIT Group, Uiversity of Zurich, Switzerland, 5 Environmental Systems Analysis Group, Wageningen University, Netherlands, 6 Conservation and Development, Cambridge University, UK, 7 Environmental Policy Lab, ETH Zürich, Switzerland, 8 Institute for Food and Resource Economics, University of Bonn, Germany, 9 Center for Development Research, University of Bonn, Germany. </p>
Data and Code 3/3 "Public Policies and Global Forest Conservation: Empirical Evidence from National Borders"
<p>Data and Code "Public Policies and Global Forest Conservation: Empirical Evidence from National Borders", published in Global Environmental Change, authored by David Wuepper (1,2)*, Thomas Crowther (3), Thomas Lauber (3), Devin Routh (3,4), Solen Le Clec'h (5), Rachael D. Garrett (6,7), Jan Börner (8,9). (1 Land Economics Group, University of Bonn, Germany, 2 Agricultural Economics and Policy Group, ETH Zurich, Switzerland, 3 Global Ecosystem Ecology Group, ETH Zurich, Switzerland, 4 SIT Group, Uiversity of Zurich, Switzerland, 5 Environmental Systems Analysis Group, Wageningen University, Netherlands, 6 Conservation and Development, Cambridge University, UK, 7 Environmental Policy Lab, ETH Zürich, Switzerland, 8 Institute for Food and Resource Economics, University of Bonn, Germany, 9 Center for Development Research, University of Bonn, Germany. </p>
Anatomy of top 1% most highly-cited publications. An empirical comparison of two approaches. Dataset
<p>Supplementary material containing tables with the main pieces of data used in the publication entitled Anatomy of top 1% most highly-cited publications. An empirical comparison of two approaches. This pieces of data were downloaded from the November 2022 snapshot of OpenAlex.</p>
Artifact for An Extensive Empirical Study of Nondeterministic Behavior in Static Analysis Tools
<p>This repository contains data for 'An Extensive Empirical Study of Nondeterministic Behavior in Static Analysis Tools' and the source code of the tool NDDetector that is used for performing the experiments in RQ2.</p><p>There are two directories, data and tool:</p><p><data> contains the data for the conclusion made in the two research questions, RQ1 and RQ2. (rq1 is Research Question 1s data)</p><p>In rq1/ there are:</p><p>final_results.csv - Contains 43 distinct results from 4 repositories (SOOT, WALA, FlowDroid, DroidSafe) that fix or report nondeterminism.</p><p>summary.pdf - Reports the number of nondeterminism results by tool repository at each stage of the qualitative study.</p><p>categorization.pdf - Reports the number of nondeterminism results by root cause categories at each component of analysis codebase in which the nondeterminism takes place</p><p>raw_data.zip - Contains the raw commits and issues extracted from 9 repositories (SOOT, DOOP, WALA, FlowDroid, DroidSafe, AmanDroid, TAJS, Code2Flow, PyCG)</p><p>key_words_results.zip - Contains the results extracted by each keyword (concurrency, concurrent, consistent, determinism, deterministic, different, flakiness, flaky, parallel, thread) from the raw data.</p><p>In rq2/ there are:</p><p>ICSE2024_AGGREGATE_DATA.csv - Contains the result distributions of each combination of target program, configuration hash, and tool aswell as the calculated consistency score.</p><p>analyze_results.py - Script that makes this data.</p><p>node_freqs - Contains the frequency of each node in the nondeterministic results we observed,it also keeps track of whether this particular node is a callee or caller or source/sink.</p><p>edge_dists - Contains the actual edge distributions of all of our results that behaved nondeterministically. it contains, for each result (edge/flow) across repetitions, which repetitions did or did not contain this edge/flow and which did. This means if you are interested in the actual differences across results generated by tool edge_dists/ is the place to look.</p><p>figure_8 - The raw data and occurences per node sheet for generating Figure_8.</p><p><tool> contains the framework and its source code that we used for conducting the experiments as well as the scripts that are used to post-process the detected nondeterminstic behavior and generate the summarized results in Section 4.</p>
[Dataset] Evaluation of the Empirical Scaling Factor of Joule Heating Rates in TIE-GCM with EISCAT Measurements
<p>Dataset used for "Evaluation of the Empirical Scaling Factor of Joule Heating Rates in TIE-GCM with EISCAT Measurements"</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.