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zenodo48/100

CETAF-DiSSCo/COVID19-TAF biodiversity-related knowledge hub working group: indexed biotic interactions and review summary

<p>This data publication originated as part of developing a biodiversity-related knowledge hub on COVID-19 via COVID19-TAF - Communities Taking Action (https://cetaf.org/covid19-taf-communities-taking-action), a community-rooted initiative raised jointly by the Consortium of European Taxonomic Facilitaties (CETAF, https://cetaf.org) and Distributed Systems of Scientific Collections (DiSSCo, https://www.dissco.eu/).</p> <p>This archive contains the biodiversity datasets of interest identified in period 14 April-6 October 2020 through COVID19-TAF activities and subsequently indexed by Global Biotic Interactions (GloBI, https://globalbioticinteractions.org).&nbsp; GloBI provides open access to finding species interaction data (e.g., predator-prey, pollinator-plant, virus-host, parasite-host) by combining existing open datasets using open source software.</p> <p>These identified datasets (see references and reviews below) add to a growing collection of open species interaction datasets already indexed by GloBI. So, this data publication only includes a small subset of indexed datasets and include only datasets that were added as a direct consequence of COVID19-TAF activities of the biodiversity-related knowledge hub working group.</p> <p>If you have questions or comments about this publication, please open an issue at https://github.com/ParasiteTracker/tpt-reporting or contact the authors by email.</p> <p>Funding:<br> The creation of this archive was made possible in part by reporting software developed as part of the National Science Foundation award &quot;Collaborative Research: Digitization TCN: Digitizing collections to trace parasite-host associations and predict the spread of vector-borne disease,&quot; Award numbers DBI:1901932 and DBI:1901926 . Also, this material is based upon work supported by the National Science Foundation under Grant No. DGE-1545433 .</p> <p>References:<br> Jorrit H. Poelen, James D. Simons and Chris J. Mungall. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2014.08.005.</p> <p>GloBI Data Review Report</p> <p>Datasets under review:<br> &nbsp;- Geiselman, Cullen K. &amp; Sarah Younger. 2020. Bat Eco-Interactions Database. www.batbase.org accessed via https://github.com/globalbioticinteractions/batbase/archive/9c65cfeee1a054f9db8cd8bf6892017fd1b3c840.zip on 2020-10-04T22:53:45.576Z<br> &nbsp;- Geiselman, Cullen K. and Tuli I. Defex. 2015. Bat Eco-Interactions Database. www.batplant.org accessed via https://github.com/globalbioticinteractions/batplant/archive/a2e1b57052244d5251d17e96ea61f58bea88975e.zip on 2020-10-04T22:54:28.727Z<br> &nbsp;- Daniel Becker, Gregory F Albery, Anna R Sjodin, Timothee Poisot, Tad Dallas, Evan A. Eskew, Maxwell J. Farrell, Sarah Guth, Barbara A Han, Nancy B Simmons, Colin J Carlson. 2020. Predicting wildlife hosts of betacoronaviruses for SARS-CoV-2 sampling prioritization. bioRxiv 2020.05.22.111344; doi: https://doi.org/10.1101/2020.05.22.111344 accessed via https://github.com/globalbioticinteractions/becker2020/archive/47c6ad28e1c5058f3c13ca69a59fdf21229e8d7f.zip on 2020-10-04T22:54:46.723Z<br> &nbsp;- Chen L, Liu B, Yang J, Jin Q, 2014. DBatVir: the database of bat-associated viruses. Database (Oxford). 2014:bau021. doi:10.1093/database/bau021 accessed via https://github.com/globalbioticinteractions/dbatvir/archive/a906d76e362484d3ca1edbe9683f672838ab70b0.zip on 2020-10-04T22:56:13.913Z<br> &nbsp;- Chen L, Liu B, Wu Z, Jin Q, Yang J, 2017. DRodVir: A resource for exploring the virome diversity in rodents. J Genet Genomics. 44(5):259-264. accessed via https://github.com/globalbioticinteractions/drodvir/archive/0346c0e8d4d66c6400e9965bd6a6aeed24cd7586.zip on 2020-10-04T23:06:04.368Z<br> &nbsp;- Agosti, Donat. 2020. Transcription of Linn&eacute;, C. von, 1758. Systema naturae per regna tria naturae secundum classes, ordines, genera, species, cum characteribus, differentiis, synonymis, locis. Available at: http://dx.doi.org/10.5962/bhl.title.542 . accessed via https://github.com/globalbioticinteractions/linnaeus1758/archive/a818060080fa04a88dac6df1ae5b897304ae8877.zip on 2020-10-05T00:46:04.852Z<br> &nbsp;- Mollentze, Nardus, &amp; Streicker, Daniel G. (2019). Viral zoonotic risk is homogenous among taxonomic orders of mammalian and avian reservoir hosts (Version 1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3516613 accessed via https://github.com/globalbioticinteractions/mollentze2019/archive/ad12dc74d03c3d992618f16c37cafb7f7ffd9d01.zip on 2020-10-05T00:50:55.878Z<br> &nbsp;- Eneida L. Hatcher, Sergey A. Zhdanov, Yiming Bao, Olga Blinkova, Eric P. Nawrocki, Yuri Ostapchuck, Alejandro A. Sch&auml;ffer, J. Rodney Brister, Virus Variation Resource &ndash; improved response to emergent viral outbreaks, Nucleic Acids Research, Volume 45, Issue D1, January 2017, Pages D482&ndash;D490, https://doi.org/10.1093/nar/gkw1065 . accessed via https://github.com/globalbioticinteractions/ncbi-virus/archive/531a8d743d7adcf1153a19087e5d3c5b76750e3e.zip on 2020-10-05T00:53:53.646Z<br> &nbsp;- Olival, K. J., Hosseini, P. R., Zambrana-Torrelio, C., Ross, N., Bogich, T. L., &amp; Daszak, P. (2017). Host and viral traits predict zoonotic spillover from mammals. Nature, 546(7660), 646&ndash;650. doi:10.1038/nature22975 accessed via https://github.com/globalbioticinteractions/olival2017/archive/f61070a5339d0e6c6e76d7eb4e2102decb52317d.zip on 2020-10-05T00:56:43.356Z<br> &nbsp;- Pensoft Darwin Core Archives with associateTaxa columns accessed via https://github.com/globalbioticinteractions/pensoft-dwca/archive/ee8831a2a391203f4fa8c05a0ddd927202b234bf.zip on 2020-10-05T00:56:51.868Z<br> &nbsp;- Pensoft Darwin Core Archives available via Integrated Publication Toolkit accessed via https://github.com/globalbioticinteractions/pensoft-ipt/archive/4ad4b47978324681289e36f8c2b247b1bcc97b1a.zip on 2020-10-05T00:58:01.912Z<br> &nbsp;- De Rojas M, Do&ntilde;a J, Dimov I (2020) A comprehensive survey of Rhinonyssid mites (Mesostigmata: Rhinonyssidae) in Northwest Russia: New mite-host associations and prevalence data. Biodiversity Data Journal 8: e49535. https://doi.org/10.3897/BDJ.8.e49535 accessed via https://github.com/globalbioticinteractions/pensoft-table/archive/3488e0397ca4e083d5eca6949951e426a75713e3.zip on 2020-10-05T00:58:03.647Z<br> &nbsp;- Marcus Guidoti, Tatiana Ruschel, Donat Agosti. 2020. Corona virus related biotic associations manually extracted from literature. Plazi. accessed via https://github.com/globalbioticinteractions/plazi-covid19/archive/326578b0d9f974760dcd2e962d86636a6487a6c0.zip on 2020-10-05T00:58:08.025Z<br> &nbsp;- Shaw, LP, Wang, AD, Dylus, D, et al. The phylogenetic range of bacterial and viral pathogens of vertebrates. Mol Ecol. 2020; 29: 3361&ndash; 3379. https://doi.org/10.1111/mec.15463 accessed via https://github.com/globalbioticinteractions/shaw2020/archive/bb9ab857b7fdbb4e931752d01b43d37b3ada77cf.zip on 2020-10-05T01:05:23.554Z<br> &nbsp;- OpenBiodiv. 2020. Annotated biotic interaction tables from Pensoft publications. accessed via https://github.com/pensoft/pensoft-interaction-tables/archive/bb7d1dc9f2eba220a61502e06e6114053fd30788.zip on 2020-10-05T03:03:23.372Z<br> &nbsp;- Quentin J. Groom. 2020. Bat interation data manually extracted from literature. accessed via https://github.com/qgroom/batinterations/archive/70108945f9014aa0ac1db920191867f7e151c793.zip on 2020-10-05T03:04:11.533Z</p> <p>Generated on:<br> 2020-10-06</p> <p>by:<br> GloBI&#39;s Elton 0.10.2<br> (see https://github.com/globalbioticinteractions/elton).</p> <p>&nbsp;</p> <p>Note that all files ending with .tsv are files formatted<br> as UTF8 encoded tab-separated values files.</p> <p>https://www.iana.org/assignments/media-types/text/tab-separated-values</p> <p><br> Included in this review archive are:</p> <p>README:<br> &nbsp; This file.</p> <p>review_summary.tsv:<br> &nbsp; Summary across all reviewed collections of total number of distinct review comments.</p> <p>review_summary_by_collection.tsv:<br> &nbsp; Summary by reviewed collection of total number of distinct review comments.</p> <p>indexed_interactions_by_collection.tsv:<br> &nbsp; Summary of number of indexed interaction records by institutionCode and collectionCode.</p> <p>review_comments.tsv.gz:<br> &nbsp; All review comments by collection.</p> <p>indexed_interactions_full.tsv.gz:<br> &nbsp; All indexed interactions for all reviewed collections.</p> <p>indexed_interactions_simple.tsv.gz:<br> &nbsp; All indexed interactions for all reviewed collections selecting only sourceInstitutionCode, sourceCollectionCode, sourceCatalogNumber, sourceTaxonName, interactionTypeName and targetTaxonName.</p> <p>datasets_under_review.tsv:<br> &nbsp; Details on the datasets under review.</p> <p>elton.jar:<br> &nbsp; Program used to update datasets and generate the review reports and associated indexed interactions.</p> <p><br> datasets.zip:<br> &nbsp; source datasets collected by elton in process of executing the generate_report.sh script.</p> <p>generate_report.sh:<br> &nbsp; program used to generate the report</p> <p>generate_report.log:<br> &nbsp; log file generated as part of running the generate_report.sh script</p>

opencc-by-4.0May 2020View details →
dryad40/100

Evolution of conditional cooperation in collective-risk social dilemma with repeated group interactions

<p>The question of how cooperation evolves and is sustained over time has been a long-standing and unresolved issue in the fields of evolutionary biology and social sciences. Previous theoretical and experimental research based on the collective-risk social dilemma game has revealed the risk that the failure of collective goals will affect the evolution of cooperation. Considering that in the real world individuals usually adjust their decisions based on environmental factors such as risk intensity and cooperation level, it is still not well understood how such conditional behaviors affect the evolution of cooperation in repeated group interactions scenario from a theoretical perspective. Here, we construct an evolutionary game model with repeated interactions, in which defectors decide whether to cooperate in subsequent rounds of the game based on whether the risk exceeds their tolerance threshold and whether the number of cooperators exceeds the collective goal in the early rounds of the game. We find that the introduction of conditional cooperation strategy can effectively promote the emergence of cooperation, especially when the risk is low. In addition, the risk threshold significantly affects the evolutionary outcomes. Furthermore, our results confirm that a high risk can promote the emergence of cooperation. Importantly, when the risk exceeds the tolerance threshold, timely adjustment of strategies by conditional cooperators is beneficial for maintaining high-level cooperation.</p>

opencc-zeroMar 2024View details →
zenodo40/100

Interactions between land use, taxonomic group and aspects and levels of diversity in a Brazilian savanna: implications for the use of bioindicators

<p>The study was carried out&nbsp;in&nbsp;the&nbsp;Tri&acirc;ngulo&nbsp;Mineiro region of Minas Gerais state, covering the municipalities of&nbsp;Uberl&acirc;ndia, Monte&nbsp;Alegre,&nbsp;and Nova Ponte,&nbsp;in&nbsp;south-eastern&nbsp;Brazil.&nbsp;We&nbsp;conducted the study&nbsp;in&nbsp;five habitat types,&nbsp;comprising&nbsp;two&nbsp;natural&nbsp;habitats&nbsp;(savanna&nbsp;and&nbsp;semideciduous forest),&nbsp;and three anthropogenic land-uses:&nbsp;cattle pastures (planted with&nbsp;introduced&nbsp;Urochloa&nbsp;grasses),&nbsp;soy fields (where sampling took place when plants were at the vegetative phase)&nbsp;and&nbsp;&nbsp;plantations&nbsp;of&nbsp;Eucalyptus&nbsp;trees&nbsp;(&ge;&nbsp;6&nbsp;yrs&nbsp;old).&nbsp;Ants&nbsp;and beetles&nbsp;were sampled&nbsp;at&nbsp;the same&nbsp;40 sites (8&nbsp;replicates&nbsp;per&nbsp;land use), and birds&nbsp;at&nbsp;30 sites (6&nbsp;replicates&nbsp;per land use), only some of which were the same as for ants and beetles.&nbsp;</p> <p>Ants&nbsp;that forage on ground&nbsp;and&nbsp;dung beetles&nbsp;were sampled&nbsp;using&nbsp;pitfall traps. Sampling took&nbsp;place in&nbsp;November&nbsp;and December&nbsp;(early wet season)&nbsp;2017.&nbsp;In each site, eight traps were&nbsp;installed with traps located at the corners of a 100&times;100&nbsp;m square, and at the mid-points of&nbsp;the sides of the square,&nbsp;keeping a&nbsp;minimum distance of 50&nbsp;m between&nbsp;any two&nbsp;traps.&nbsp;All traps were at least 75 m distant from the edge of the respective land&nbsp;use.&nbsp;Traps were plastic containers (19 cm&nbsp;diam, 11 cm height) filled with 150 ml of a saline solution and detergent. Each trap had a wire hoop suspended over it to accommodate a small (4 cm&nbsp;diam, 4 cm height) plastic container for holding a dung bait. We used a 20 cm&nbsp;diameter plastic cover supported by three sticks to protect traps from rain. Traps were baited with&nbsp;~40 g of a mixture of pig dung and human&nbsp;faeces&nbsp;(4:1 proportion)&nbsp;and left in the field for 48-hrs.</p> <p>Birds were surveyed&nbsp;using&nbsp;20-min point counts in the rainy season (November 2017 to March 2018). At each site,&nbsp;five sampling points were established, 200 m distant from each other. All surveys started at sunrise (about 6 a.m.), and all species seen or heard from each point were recorded. Each sampling site was&nbsp;re-surveyed&nbsp;in the&nbsp;following&nbsp;dry season (April to&nbsp;October to 2018); however, for logistic reasons we were unable to re-survey the plantation sites.&nbsp;</p> <p>Ant&nbsp;and dung beetle&nbsp;species were identified to&nbsp;species or morphospecies&nbsp;by comparison with named species in the Zoological Collection at the Federal University of&nbsp;Uberl&acirc;ndia&nbsp;(UFU)&nbsp;or&nbsp;with&nbsp;specialist assistance from Fernando Vaz de Mello, respectively.&nbsp;Vouchers of&nbsp;all&nbsp;species have been deposited&nbsp;at&nbsp;UFU&acute;s Zoological Collection.&nbsp;Birds were identified directly in the field and species&nbsp;names follow the checklist produced by the Brazilian Ornithological Records Committee.</p> <p>We classified species&nbsp;functionally&nbsp;based on&nbsp;primary diet, foraging&nbsp;location&nbsp;and/or&nbsp;behaviour, and body size,&nbsp;as&nbsp;these&nbsp;traits&nbsp;are known to be sensitive&nbsp;to habitat modifications&nbsp;and of importance for the ecosystem services provided by ants, birds, and dung beetles.</p> <p>Ant species were classified according to their diet as&nbsp;predators,&nbsp;fungivores,&nbsp;nectarivores&nbsp;or omnivores,&nbsp;and according to their main foraging location&nbsp;as arboreal, epigeal&nbsp;(aboveground)&nbsp;or hypogeal&nbsp;(in soil and litter), based on&nbsp;information provided by Brown (2000) and Silvestre&nbsp;et al.&nbsp;(2003). Species were&nbsp;further&nbsp;classified&nbsp;into four body size categories based on&nbsp;our&nbsp;measurements of body length&nbsp;(Weber&acute;s length;&nbsp;Brown,&nbsp;1953)&nbsp;of&nbsp;1-5&nbsp;ant workers&nbsp;per species:&nbsp;1 (&lt; 0.75&nbsp;mm),&nbsp;2 (0.75-1.74&nbsp;mm),&nbsp;3 (1.75-3&nbsp;mm),&nbsp;and 4 (&gt; 3 mm).</p> <p>Dung beetles were classified as coprophagous, necrophagous, frugivore, generalist or predator, according to the type of food resource each species is most&nbsp;often&nbsp;attracted to. This classification was based on over 30 years of field experience&nbsp;throughout Brazil&nbsp;by one of the authors&nbsp;of this study&nbsp;(FVM),&nbsp;who used multiple types of baits&nbsp;(e.g., carcasses, fruits, faeces)&nbsp;to attract and collect dung beetles, and/or on literature information.&nbsp;Although information about the &ldquo;attractiveness&rdquo; of different types of baits&nbsp;to&nbsp;dung beetles (used here as a proxy for primary diet)&nbsp;was&nbsp;not obtained&nbsp;directly&nbsp;in the sites of the present study, it is importat to note that we are not aware of&nbsp;any evidence&nbsp;of geographic or habitat&nbsp;variation in bait preference&nbsp;among tropical species of dung beetles.&nbsp;Dung beetles&nbsp;were also&nbsp;classified&nbsp;according&nbsp;to&nbsp;their&nbsp;foraging&nbsp;behaviour as:&nbsp;telecoprid&nbsp;(species that make a dung ball and roll it away for burial),&nbsp;paracoprid&nbsp;(species that store dung in tunnels dug immediately below the dung&nbsp;source),&nbsp;or&nbsp;endocoprid&nbsp;(species living within or immediately below the dung, without moving it).&nbsp;For this,&nbsp;we used&nbsp;the database of the&nbsp;Zoological Collection&nbsp;of&nbsp;the Federal University&nbsp;of&nbsp;Mato Grosso (UFMT).&nbsp;Whenever sample sizes&nbsp;allowed, 30&nbsp;individuals from each species&nbsp;were weighed for determination of body mass&nbsp;(following Almeida et al.,&nbsp;2011), and species were&nbsp;classified&nbsp;according to the following ordinal scale: 1&nbsp;(&lt;&nbsp;10&nbsp;mg);&nbsp;2&nbsp;(10-99&nbsp;mg);&nbsp;3&nbsp;(100-300&nbsp;mg);&nbsp;and&nbsp;4&nbsp;(&gt;300&nbsp;mg).</p> <p>Each bird&nbsp;species&nbsp;was&nbsp;classified according to&nbsp;its&nbsp;primary diet as&nbsp;frugivores granivore, insectivore, nectarivore,&nbsp;carnivore,&nbsp;detritivore,&nbsp;or omnivore,&nbsp;and according to the main&nbsp;foraging&nbsp;location&nbsp;as&nbsp;ground, understory/shrubby vegetation, or tree canopy, based on&nbsp;the&nbsp;Wilman&nbsp;et al.&nbsp;(2014) database and&nbsp;our&nbsp;own&nbsp;field experience.&nbsp;Using these&nbsp;same sources,&nbsp;we obtained information&nbsp;on&nbsp;mean body weights of each&nbsp;species and&nbsp;assigned&nbsp;them&nbsp;to one of&nbsp;five&nbsp;size categories: 1-&nbsp;(&lt;15 g);&nbsp;2&nbsp;(15-39 g);&nbsp;3&nbsp;(40-199&nbsp;g);&nbsp;4&nbsp;(200-599 g);&nbsp;and 5&nbsp;(&gt;&nbsp;600 g).</p>

opencc-by-4.0Aug 2022View details →
dryad40/100

Data from: Status-dependent metabolic effects of social interactions in a group-living fish

<p>Social interactions can sometimes be a source of stress, but social companions can also ameliorate and buffer against stress. Stress and metabolism are closely linked, but the degree to which social companions modulate metabolic responses during stressful situations—and whether such effects differ depending on social rank—is poorly understood. To investigate this question, we studied Neolamprologus pulcher, a group-living cichlid fish endemic to Lake Tanganyika, and measured the metabolic responses of dominant and subordinate individuals when they were either visible or concealed from one another. When individuals could see each other, subordinates had lower maximum metabolic rates and tended to take longer to recover following an exhaustive chase compared to dominants. In contrast, metabolic responses of dominants and subordinates did not differ when individuals could not see one another. These findings suggest that the presence of a dominant individual has negative metabolic consequences for subordinates, even in stable social groups with strong prosocial relationships.</p>

opencc-zeroJun 2024View details →
zenodo40/100

Dataset: iClick Interactive Asia Group Limited (ICLK) Stock Performance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

opencc-zeroJun 2024View details →
zenodo40/100

Dataset: Interactive Brokers Group, Inc. (IBKR) Stock Performance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

opencc-zeroJun 2024View details →
zenodo40/100

ACM MMSys ODS The Unobtrusive Group Interaction (UGI) Corpus

<p>Studying group dynamics requires fine-grained spatial and temporal understanding of human behavior. Social psychologists studying human interaction patterns in face-to-face group meetings often find themselves struggling with huge volumes of data that require many hours of tedious manual coding. There are only a few publicly available multi-modal datasets of face-to-face group meetings that enable the development of automated methods to study verbal and non-verbal human behavior. &nbsp;In this paper, we present a new, publicly available multi-modal dataset for group dynamics study that differs from previous datasets in its use of ceiling-mounted, unobtrusive depth sensors. &nbsp;These can be used for fine-grained analysis of head and body pose and gestures, without any concerns about participants&#39; privacy or inhibited behavior. The dataset is complemented by synchronized and time-stamped meeting transcripts that allow analysis of spoken content. The dataset comprises 22 group meetings in which participants perform a standard collaborative group task designed to measure leadership and productivity. &nbsp;Participants&#39; post-task questionnaires, including demographic information, are also provided as part of the dataset. We show the utility of the dataset in analyzing perceived leadership, contribution, and performance, by presenting results of multi-modal analysis using our sensor-fusion algorithms designed to automatically understand audio-visual interactions.</p>

opencc-by-4.0Apr 2019View details →
zenodo40/100

The Social Group Interaction Dataset

<p>The Social Group Interaction Dataset is a dataset consisting of recordings of 6 groups of 5 people completing a series of tasks which provoke group formation. The sorts of interactions encouraged by task completion are as follows: discussion, collaborative task, robot interaction and debate. Recordings include Vicon and facial go-pro recordings. Big 5 personality results and basic demographic information are also included.</p>

opencc-by-4.0Mar 2023View details →
dryad40/100

Data from: Status-dependent metabolic effects of social interactions in a group-living fish

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publicJun 2024View details →
dryad40/100

Evolution of conditional cooperation in collective-risk social dilemma with repeated group interactions

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publicMar 2024View details →
dryad40/100

Data and code from: Species interactions amplify functional group responses to elevated CO2 and N enrichment in a 24-year grassland experiment

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publicAug 2024View details →
dryad36/100

Data from: Behavioral hypervolumes of predator groups and predator-predator interactions shape prey survival rates and selection on prey behavior

Predator-prey interactions often vary on the basis of the traits of the individual predators and prey involved. Here we examine whether the multidimensional behavioral diversity of predator groups shapes prey mortality rates and selection on prey behavior. We ran individual sea stars (Pisaster ochraceus) through three behavioral assays to characterize individuals' behavioral phenotype along three axes. We then created groups that varied in the volume of behavioral space that they occupied. We further manipulated the ability of predators to interact with one another physically via the addition of barriers. Prey snails (Chlorostome funebralis) were also run through an assay to evaluate their predator avoidance behavior before their use in mesocosm experiments. We then subjected pools of prey to predator groups and recorded the number of prey consumed and their behavioral phenotypes. We found that predator-predator interactions changed survival selection on prey traits: when predators were prevented from interacting, more fearful snails had higher survival rates, whereas prey fearfulness had no effect on survival when predators were free to interact. We also found that groups of predators that occupied a larger volume in behavioral trait space consumed 35% more prey snails than homogeneous predator groups. Finally, we found that behavioral hypervolumes were better predictors of prey survival rates than single behavioral traits or other multivariate statistics (i.e., principal component analysis). Taken together, predator-predator interactions and multidimensional behavioral diversity determine prey survival rates and selection on prey traits in this system.

opencc-zeroDec 2015View details →
zenodo36/100

X-ray structure ensemble refinement of the second bromodomain of Pleckstrin homology domain interacting protein (PHIP) (space group P21212)

<p>X-ray structure ensemble refinement of the second bromodomain of Pleckstrin homology domain interacting protein (PHIP) (space group P21212). Raw diffraction images are available on Zenodo: 10.5281/zenodo.4086066. A single conformer model was deposited in the Protein Data Bank under accession code <a href="https://www.ebi.ac.uk/pdbe/entry/pdb/7AV8">7AV8</a>. Refinement was carried out with <a href="https://www.phenix-online.org/documentation/reference/ensemble_refinement.html">phenix.ensemble_refinement</a> and the repository contains all input and output files:</p> <p>Input:</p> <ul> <li>mx8421v63_xPHIPAx1521_free.mtz</li> <li>refine9.pdb</li> </ul> <p>Output:</p> <ul> <li>PHIPA-P21212_ensemble_refinement_ensemble.geo</li> <li>PHIPA-P21212_ensemble_refinement_ensemble.pdb</li> <li>PHIPA-P21212_ensemble_refinement_ensemble.mtz</li> <li>PHIPA-P21212_ensemble_refinement_ensemble.log</li> </ul>

opencc-by-4.0Oct 2020View details →
dryad36/100

Herding mechanisms to maintain the cohesion of a harem group: two interaction phases during herding

<p>In animal groups, individual interactions achieve coordinated movements to maintain cohesion. In horse harem groups, herding<br> is a behavior in which males chase females from behind; it is considered to assist with group cohesiveness. However,<br> the mechanisms by which the individuals move to maintain group cohesion are unknown. We applied novel non-invasive<br> methods of drone filming and video tracking to observe horse movements in the field with high temporal and spatial resolution.<br> We tracked all group members and drew trajectories. We analyzed the movements of females and found two phases<br> of interactions based on their timing of movement initiation. The females that moved first were those nearest to the herding<br> male, while the movement initiation of the later females was determined by the distance from the nearest moving female, not<br> by the distance from the herding male. These interactions are unique among animal group movements and might represent a<br> herding mechanism responsible for maintaining group cohesion. This might be due to long-term stable relationships within<br> a harem group and strong social bonds between females. This study showed that the combination of drone filming and video<br> tracking is a useful method for analyzing the movements of animals simultaneously in high resolution.</p>

opencc-zeroNov 2019View details →
dryad36/100

Individual survival is dictated by group personality in a marsh ecosystem predator-prey interaction

<p>Predator–prey interactions structure ecological communities, and personality can significantly mediate these interactions. Personality expression is often contingent on social context, suggesting that group personality may be key in determining the outcomes of predator encounters. Here we test the influence of individual personality and group composition on survival of the marsh periwinkle, <em>Littoraria irrorata</em>, when exposed to its main predator, the blue crab, <em>Callinectes</em> <em>sapidus</em>, both integral players in mediating the productivity and health of salt marsh ecosystems. Personality varied along a bold-shy continuum and was unrelated to shell length, though neither trait influenced survival when individual snails were exposed to a predator. However, when snails were partitioned into groups differing in personality composition, snails in groups with bold individuals had higher survival than those in groups with none. While group type did not influence the survival of bold snails, shy snails performed significantly better in mixed groups: the presence of a few bold individuals emerged as especially effective in decreasing mortality of shy snails. The effect of personality on predator escape is thus conditional on group personality composition, indicating that social context can directly impact the survival and success of its individual members, with broad implications for community dynamics. </p>

opencc-zeroOct 2023View details →
dryad36/100

Data from: Behavioral hypervolumes of predator groups and predator-predator interactions shape prey survival rates and selection on prey behavior

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publicAug 2020View details →
dryad36/100

Individual survival is dictated by group personality in a marsh ecosystem predator-prey interaction

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publicOct 2023View details →
dryad36/100

Fear effects and group size interact to shape herbivory on coral reefs

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publicJun 2021View details →
dryad36/100

Herding mechanisms to maintain the cohesion of a harem group: two interaction phases during herding

Open the record for dataset details and reuse information.

publicNov 2019View details →
zenodo32/100

Raw diffraction images of the second bromodomain of Pleckstrin homology domain interacting protein (PHIP) (space group C2)

<p>Raw diffraction images of the second bromodomain of Pleckstrin homology domain interacting protein (PHIP) (space group C2). The final structure is deposited in the Protein Data Bank under accession code <a href="https://www.ebi.ac.uk/pdbe/entry/pdb/7AV9">7AV9</a>.</p> <p>&nbsp;</p> <p>Additional information:</p> <p>dataset: PHIPA-x1724<br> beamline: Diamond Light Source I04-1<br> visit:&nbsp; nt11175-63<br> date: 16-04-2015<br> &Omega; Start: 144.0&deg;<br> &Omega; Osc: 0.12&deg;<br> &Omega; Overlap: 0&deg;<br> No. Images: 1500<br> Resolution: 1.50&Aring;<br> Wavelength: 0.9173&Aring;<br> Exposure: 0.040s<br> Transmission: 100.00%<br> Beamsize: 60x50&mu;m</p>

opencc-by-4.0Oct 2020View details →

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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.

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neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

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behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record

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