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.
53
datasets available to search
ShareScore release 0.9.0
Dataset results
53 results for “social coding”
Male-like ornamentation in female hummingbirds results from social harassment rather than sexual selection DATA_CODE
<p>Contains all relevant data and code for analyses and figures used in "Male-like ornamentation in female hummingbirds results from social harassment rather than sexual selection" by Jay J. Falk, Michael S. Webster, and Dustin R. Rubenstein. Current Biology, 2021. </p>
Data and code for: Generation and applications of simulated datasets to integrate social network and demographic analyses
<p class="MsoNormal"><span>Social networks are tied to population dynamics; interactions are driven by population density and demographic structure, while social relationships can be key determinants of survival and reproductive success. However, difficulties integrating models used in demography and network analysis have limited research at this interface. We introduce the R package genNetDem for simulating integrated network-demographic datasets. It can be used to create longitudinal social networks and/or capture-recapture datasets with known properties. It incorporates the ability to generate populations and their social networks, generate grouping events using these networks, simulate social network effects on individual survival, and flexibly sample these longitudinal datasets of social associations. By generating co-capture data with known statistical relationships it provides functionality for methodological research. We demonstrate its use with case studies testing how imputation and sampling design influence the success of adding network traits to conventional Cormack-Jolly-Seber (CJS) models. We show that incorporating social network effects in CJS models generates qualitatively accurate results, but with downward-biased parameter estimates when network position influences survival. Biases are greater when fewer interactions are sampled or fewer individuals are observed in each interaction. While our results indicate the potential of incorporating social effects within demographic models, they show that imputing missing network measures alone is insufficient to accurately estimate social effects on survival, pointing to the importance of incorporating network imputation approaches. genNetDem provides a flexible tool to aid these methodological advancements and help researchers test other sampling considerations in social network studies.</span></p>
data and code for "Depression and social anxiety in relation to problematic TikTok use severity: The mediating role of boredom proneness and distress intolerance "
<p>data and code for the paper titled "Depression and social anxiety in relation to problematic TikTok use severity: The mediating role of boredom proneness and distress intolerance", to be published on Computers in Human Behavior.</p> <p> </p> <p> </p>
Code and data for "Postreproductive female killer whales reduce socially inflicted injuries in their male offspring"
<p>R code and data necessary to reproduce all analyses from the manuscript "Postreproductive female killer whales reduce socially inflicted injuries in their male offspring". Included is demographic and social data on 103 southern resident killer whales, the R code to fit Bayesian regression models and code to produce the plots in the manuscript.</p>
Data and code for: Social phenotype-dependent selection of social environment in wild great and blue tits: An experimental study
Open the record for dataset details and reuse information.
Data and code for: Generation and applications of simulated datasets to integrate social network and demographic analyses
Open the record for dataset details and reuse information.
Data and code from: Climate and parasite pressure jointly shape traits mediating the coevolution between an ant social parasite and its host
Open the record for dataset details and reuse information.
Data and Code for: Food distribution, but not market forces, predict behavioral social tolerance in rhesus macaques
Open the record for dataset details and reuse information.
Code and data from: Familiarity, dominance, sex and season shape common waxbill social networks.
Open the record for dataset details and reuse information.
Code for: A metapopulation model of social group dynamics and disease applied to Yellowstone wolves
Open the record for dataset details and reuse information.
Data and code from: Social plasticity and individuality shape variation in contest behaviour
Open the record for dataset details and reuse information.
poojaruhal/RP-commenting-practices-social-media: RP-commenting-practices-social-media: RP_TOSEM_2020 v.1.0.1 Second release of of the replication Package for the paper "What do Developers Discuss about Code Comment Conventions on Social Media"
<p>RP-commenting-practices-social-media</p> <p>Replication Package for the paper "What do Developers Discuss about Code Comment Conventions on Social Media?"</p> <p>Structure</p> <pre><code>Paper-presenation.pdf Makar_tool/ Data/ stackoverfow_questions_with_answers_by_tags.csv stackoverfow_tags_metrics.csv apache_mailing_list.csv mailing_lists_ASF_@dev_@users_1.csv mailing_lists_ASF_@dev_@users_2.csv quora.csv sample_stackoverfow_questions_with_answers_by_tags.csv Schemas/ apache_mailing_lists.json quora.json stackoverfow_questions_answers_by_tag.json stackoverfow_tag_count.json stackoverfow_tag_metrics.json RQ1/ LDA_input/ stackoverfow_raw_dataset.csv LDA_output/ Mallet/ output_csv/ docs-in-topics.csv topic-words.csv topics-in-docs.csv topics-metadata.csv output_html/ all_topics.html Docs/ Topics/ RQ2/ datasource_rawdata/ mailing_lists_selection_criteria.csv quora.csv stackoverflow.csv manual_analysis_output/ stackoverflow_quora_taxonomy.xlsx </code></pre> <p>Contents of the Replication Package</p> <p><strong>Paper-presenation.pdf</strong> presents the highlights of the work in a presenation.</p> <p><strong>Makar_tool/</strong> contains the data processed using the tool for the study</p> <ul> <li> <p><strong>Data/</strong></p> <ul> <li><code>stackoverfow_questions_with_answers_by_tags.csv</code> - all stackoverflow questions used in the study as stored in Makar <ul> <li><code>stackoverfow_tags_metrics.csv</code> - all data containing the calculations done for stackoverflow tag selection</li> <li><code>apache_mailing_list.csv</code> - statistically significant sample of <code>mailing_lists_ASF_@dev_@users_1.csv</code> and <code>mailing_lists_ASF_@dev_@users_2.csv</code> used in the study</li> <li><code>mailing_lists_ASF_@dev_@users_1.csv</code> - mailing list data used in the study as stored in Makar (part 1)</li> <li><code>mailing_lists_ASF_@dev_@users_2.csv</code> - mailing list data used in the study as stored in Makar (part 2)</li> <li><code>quora.csv</code> - all quora questions used in the study as stored in Makar</li> <li><code>sample_stackoverfow_questions_with_answers_by_tags</code> - statistically significant sample of <code>stackoverfow_questions_with_answers_by_tags.csv</code> used in the study</li> </ul> </li> </ul> </li> <li> <p><strong>Schemas/</strong></p> <ul> <li><code>apache_mailing_lists.json</code> - data schema used in Makar to store mailing list data</li> <li><code>quora.json</code> - data schema used in Makar to store quora data</li> <li><code>stackoverfow_questions_answers_by_tag.json</code> - data schema used in Makar to store stackoverflow questions data</li> <li><code>stackoverfow_tag_count.json</code> - data schema used in Makar to lookup number of questions per tag available in stackoverflow</li> <li><code>stackoverfow_tag_metrics.json</code> - data schema used in Makar to stackoverflow tag metrics data</li> </ul> </li> <li> <p><strong>RQ1/</strong> - contains the data used to answer RQ1</p> <ul> <li><strong>LDA_input/</strong> - input data used for LDA analysis <ul> <li><code>stackoverfow_raw_dataset.csv</code> - stackoverflow questions used to perform LDA analysis</li> </ul> </li> <li><strong>LDA_output/</strong> <ul> <li><strong>Mallet/</strong> - contains the LDA output generated by MALLET tool <ul> <li><strong>output_csv/</strong> <ul> <li><code>docs-in-topics.csv</code> - documents per topic</li> <li><code>topic-words.csv</code> - most relevant topic words</li> <li><code>topics-in-docs.csv</code> - topic probability per document</li> <li><code>topics-metadata.csv</code> - metadata per document and topic probability <ul> <li><strong>output_html/</strong> - Browsable results of mallet output</li> </ul> </li> <li><code>all_topics.html</code></li> <li><code>Docs/</code></li> <li><code>Topics/</code></li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> <li> <p><strong>RQ2/</strong> - contains the data used to answer RQ2</p> <ul> <li><strong>datasource_rawdata/</strong> - contains the raw data for each source <ul> <li><code>mailing_lists_selection_criteria.csv</code> - criteria used to select mailing_lists.</li> <li><code>quora.csv</code> - contains the processed dataset (like removing HTML tags). To know more about the preprocessing steps, please refer to the reproducibility section in the paper. The data is preprocessed using Makar tool.</li> <li><code>stackoverflow.csv</code> - contains the processed stackoverflow dataset. To know more about the preprocessing steps, please refer to the reproducibility section in the paper. The data is preprocessed using Makar tool.</li> </ul> </li> <li><strong>manual_analysis_output/</strong> <ul> <li><code>stackoverflow_quora_taxonomy.xlsx</code> - contains the classified dataset of stackoverflow and quora and description of taxonomy. <ul> <li><code>Taxonomy</code> - contains the description of the first dimension and second dimension categories. Second dimension categories are further divided into levels, separated by <code>|</code> symbol.</li> <li><code>stackoverflow-posts</code> - the questions are labelled relevant or irrelevant and categorized into the first dimension and second dimension categories. <ul> <li><code>quota-posts</code> - the questions are labelled relevant or irrelevant and categorized into the first dimension and second dimension categories.</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul>
Code and data associated with 'The web architecture, dynamics, and silk investment in the social spider, Stegodyphus sarasinorum'
<p>Location of the main files and folders relevant to the paper:</p> <p>All R based analyses were done with a combination of a Windows 8 laptop and a Linux Mint 19.2 Cinnamon.<br> The nparLD analyses require R version 2.15.3, and the other analyses were done with R version<br> 3.4.4. Multiple versions of R can be installed and run on the same system using the Rbuild and Renv packages. Please see the parts of the Rmd files describing the nparLD analyses to see how this was done.</p> <p>## Silk investment and topology related analyses are in the folder 'web-construction FINAL round- raw data/analysis-FINALround/analysis-results/feb_2016_Rmdfiles/</p> <p>R Markdown notebook files and associated HTML outputs used to replicate figures and results in paper:</p> <p>### Pore-size and coordination number analysis</p> <p>- beleyur_et_al_topology_analyses.Rmd</p> <p>### Silk investment and per-capita analyses</p> <p>- beleyur_et_al_silk_analyses.Rmd</p> <p>### Weight-loss analysis</p> <p>- weight_loss_analysis.Rmd</p> <p>### nparLD analysis scripts</p> <p>- poresize_CN_nparLD.R<br> - silk_nparLD.R</p> <p>## Coordination number and pore size calculations</p> <p>Code and data used to quantify the coordination number and pore size in 'AllResults.zip' - a combination of raw images, .mat data files and .m files that can be analysed using the MATLAB platform.</p> <p>All data and code in 'AllResults.zip'</p> <p> </p> <p>## Raw data location:</p> <p>'web-construction FINAL round- raw data/pics-till 11th March/pics by date and batch/'</p> <p>All raw JPG images are placed according to their batch (A-J), and group size. Please note that not all webs can be finally analysed as having the labelled group size because despite all efforts spiders somehow managed to move between colonies in some cases! Please refer to the actual colonies used in the manuscript, these webs have definitely been built by the specified number of spiders.</p> <p>## Hand-cropped images of only web with retreat</p> <p>'web-construction FINAL round- raw data/pics-till 11th March/cropped_photos_in_one_place/'</p> <p>These are TIF files made after manual cropping of the web pictures. Please see the note above explaining how to choose the webs with valid group sizes.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Hearing sensitivity and amplitude coding in bats are differentially shaped by echolocation calls and social calls
Differences in auditory perception between species are influenced by phylogenetic origin and the perceptual challenges imposed by the natural environment, e.g. detecting prey- or predator-generated sounds and communication signals. Bats are well suited for comparative studies on auditory perception since they predominantly rely on echolocation to perceive the world, while their social calls and most environmental sounds have low frequencies. We tested if hearing sensitivity and stimulus level coding in bats differ between high and low frequency ranges by measuring auditory brainstem responses (ABRs) of 86 bats belonging to 11 species. In most species, auditory sensitivity was equally good at both high and low frequency ranges, while amplitude was more finely coded for higher frequency ranges. Additionally, we conducted a phylogenetic comparative analysis by combining our ABR data with published data on 27 species. Species-specific peaks in hearing sensitivity correlated with peak frequencies of echolocation calls and pup isolation calls, suggesting that changes in hearing sensitivity evolved in response to frequency changes of echolocation and social calls. Overall, our study provides the most comprehensive comparative assessment of bat hearing capacities to date and highlights the evolutionary pressures acting on their sensory perception.
Data and Code for Inequality is rising where social network segregation interacts with urban topology
<p>This folder contains data and code to reproduce results of the paper "Inequality is rising where social network segregation interacts with urban topology". arXiv version: https://arxiv.org/abs/1909.11414.</p>
Data and code for "Sociality modulates nutritional carrying capacity of an endangered species"
<p><span>Data and code to accompany the peer reviewed paper "Sociality modulates nutritional carrying capacity of an endangered species" (pending acceptance). Data was derived from publicly available GIS layers and data collected by the California Department of Fish and Wildlife as part of endangered species recovery and monitoring efforts. Questions about the data set and code should be directed to <a href="mailto:asksnbs@wildlife.ca.gov"><span>asksnbs@wildlife.ca.gov</span></a><span><span> </span>and </span><span><span><a href="mailto:seth.t.rankins@gmail.com">seth.t.rankins@gmail.com</a>.</span></span></span></p>
A Framework for Improving Social Inclusion using Network Analysis and IoT-based Contact Tracing, Dataset and Source Code
Open the record for dataset details and reuse information.
Information diffusion assumptions can distort our understanding of social network dynamics (code and data)
<p>This repository contains the data (<code>cascade_reconstruction.tar.gz</code>) and code (<code>code_repository.tar.gz</code>) for a paper titled "Information diffusion assumptions can distort our understanding of social network dynamics" by <a href="https://www.matthewdeverna.com/">Matthew R. DeVerna</a>, <a href="https://pierri.faculty.polimi.it/">Francesco Pierri</a>, <a href="https://rachithaiyappa.github.io/">Rachith Aiyappa</a>, <a href="https://diogofpacheco.github.io/">Diogo Pachecho</a>, <a href="https://jbryden.co.uk/home/">John Bryden</a>, and <a href="https://cnets.indiana.edu/fil">Filippo Menczer</a> .</p> <p>Please see the README.md file for important details! If you would like to report issues with the code, you can do so through an associated GitHub repository that houses the project's code, which can be found <a href="https://github.com/osome-iu/cascade_reconstruction">here</a>.</p>
Hearing sensitivity and amplitude coding in bats are differentially shaped by echolocation calls and social calls
Open the record for dataset details and reuse information.
Data and code for Fraser and Nettle, 'Hunger affects social decisions in a Public Goods Game but not an Ultimatum Game'
<p><strong>Data and code for Fraser and Nettle 'Hunger affects social decisions in a multi-round Public Goods Game but not a single-shot Ultimatum Game' </strong></p> <p>The attached R script reproduces all analyses in the paper. There are three .csv data files used by the script: the dataset for experiment 1, the main dataset for experiment 2, and the latency data for experiment 2.</p> <p>This version: April 2020.</p> <p>Since the version of October 2019, we switched the order of presentation of the experiments, so the one previously referred to as experiment 2 is now experiment 1 and vice versa. Other new analyses and sections have also been added.</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.