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209
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ShareScore release 0.9.0
Dataset results
209 results for “anonymization”
Data from: Stronger transferability but lower variability in transcriptomic- than in anonymous microsatellites: evidence from Hylid frogs.
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Data from: Comparative population genetic analysis of bocaccio rockfish Sebastes paucispinis using anonymous and gene-associated simple sequence repeat loci
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Anonymized Researcher Interview Data - from the Raising the Profile of the NCSU Libraries Research Support Strategies & Engagement project
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Data from: Anonymous faecal sampling and NIRS studies of diet quality: problem or opportunity?
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Datasets for Tweets from Anonymous Physicians about COVID-19 in the U.S.
<p>This dataset was created for a project that assessed Twitter data from physicians posted anonymously by administrators of a specific Twitter user page to better understand physician perspectives and sentiments about COVID-19 in the United States. </p> <p>Tweet identifiers are contained in the 'tweet_identifiers.csv file'</p> <p>Other files contain sentiment analysis data; one file used vaderSentiment in Python 3, and the other file used NRC in R (see sources below for further information and use of these packages.</p> <ol> <li>Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.</li> <li>NRC Emotion Lexicon, Saif M. Mohammad and Peter D. Turney, NRC Technical Report, December 2013, Ottawa, Canada.</li> <li>Jockers ML (2015). <em>Syuzhet: Extract Sentiment and Plot Arcs from Text</em>. <a href="https://github.com/mjockers/syuzhet">https://github.com/mjockers/syuzhet</a>.</li> </ol> <p>Code used specifically for this project may be found at: https://github.com/sullkath/tweet_analysis</p> <p>Link to paper publication: </p> <p>Pre-print in bioRxiv available at: </p>
Spatially anonymized data from: Novel step selection analyses on energy landscapes reveal how linear features alter migrations of soaring birds
<p>This dataset consists of spatially anonymized movement data as well as environmental covariate data to estimate energy landscape step selection selections for migratory golden eagles that summer in Alaska.</p> <ol> <li>Human modification of landscapes includes extensive addition of linear features, such as roads and transmission lines. These can alter animal movement and space use and affect the intensity of interactions among species, including predation and competition. Effects of linear features on animal movement have seen relatively little research in avian systems, despite ample evidence of their effects in mammalian systems and that some types of linear features, including both roads and transmission lines, are substantial sources of mortality.</li> <li>Here, we used satellite telemetry combined with step selection functions designed to explicitly incorporate the energy landscape (el‐SSFs) to investigate the effects of linear features and habitat on movements and space use of a large soaring bird, the golden eagle <em>Aquila chrysaetos</em>, during migration. Our sample consisted of 32 adult eagles tracked for 45 spring and 39 fall migrations from 2014 to 2017.</li> <li>Fitted el‐SSFs indicated eagles had a strong general preference for south‐facing slopes, where thermal uplift develops predictably, and that these areas are likely important aspects of migratory pathways. el‐SSFs also provided evidence that roads and railroads affected movement during both spring and fall migrations, but eagles selected areas near roads to a greater degree in spring compared to fall and at higher latitudes compared to lower latitudes. During spring, time spent near linear features often occurred during slower‐paced or stopover movements, perhaps in part to access carrion produced by vehicle collisions.</li> <li>Regardless of the behavioural mechanism of selection, use of these features could expose eagles and other soaring species to elevated risk via collision with vehicles and/or transmission lines. Linear features have previously been documented to affect the ecology of terrestrial species (e.g. large mammals) by modifying individuals' movement patterns; our work shows that these effects on movement extend to avian taxa.</li> </ol>
Spatially anonymized data for: Multistate Ornstein-Uhlenbeck approach for practical estimation of movement and resource selection around central places
<p>1. Home range dynamics and movement are central to a species' ecology and strongly mediate both intra- and interspecific interactions. Numerous methods have been introduced to describe animal home ranges, but most lack predictive ability and cannot capture effects of dynamic environmental patterns, such as the impacts of air and water flow on movement.</p> <p>2. Here, we develop a practical, multi-stage approach for statistical inference into the behavioral mechanisms underlying how habitat and dynamic energy landscapes---in this case how airflow increases or decreases the energetic efficiency of flight---shape animal home ranges based around central places. We validated the new approach using simulations, then applied it to a sample of 12 adult golden eagles (Aquila chrysaetos) tracked with satellite telemetry. </p> <p>3. The application to golden eagles revealed effects of habitat variables that align with predicted behavioral ecology. Further, we found that males and females partition their home ranges dynamically based on uplift. Specifically, changes in wind and sun angle drove differential space use between sexes, especially later in the breeding season when energetic demands of growing nestlings require both parents to forage more widely. </p> <p>4. This method is easily implemented using widely available programming languages and is based on a hierarchical multistate Ornstein-Uhlenbeck space use process that incorporates habitat and energy landscapes. The underlying mathematical properties of the model allow straightforward computation of predicted utilization distributions, permitting estimation of home range size and visualization of space use patterns under varying conditions.</p>
Dataset of Trucks' Anonymized Recorded Driving and Operation
<p>During a period of 12 months, 54 class N3 trucks from four fleets of German fleet operators were equipped with high resolution GPS data loggers. A total of 1.26 million km of driving data has been recorded and constitutes one of the most comprehensive open datasets to date for high-resolution data of heavy commercial vehicles. This dataset provides metadata of recorded tracks as well as high-resolution time series data of the vehicle speed. Its applications include simulation of electrification for heavy commercial vehicles, modeling logistics processes or driving cycle construction.</p>
Data and Scripts for Anonymous et al. "How to optimally allocate sampling effort in experimental ecology"
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Increased Attention Decreases the Convincingness of Belief-Confirming Evidence (Anonymous Version)
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Comprehensive analysis of type 2 diabetes-associated gene polymorphisms in a cohort of 99 anonymized metastatic colorectal cancer patients, including their frequency.
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Dataset for tDCS & tabaco (anonymized)
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Proptest AI Anonymous Dataset
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Test of anonymous repo
<p>This repository presents sample publicly available anonymous source code and data</p>
Anonymous
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Anonymized Brain MRI Scans
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Figure 15 from: Vargas HA, Solis MA, Vargas-Ortiz M (2022) The South American moth Rheumaptera mochica (Dognin, 1904) (Lepidoptera, Geometridae, Larentiinae) rediscovered after more than a century of anonymity. ZooKeys 1085: 129-143. https://doi.org/10.3897/zookeys.1085.76868
Figure 15 Rheumaptera affirmata (Guenée, [1858]), Brazil, syntype (dorsal, ventral) and labels. Photos kindly provided by Gunnar Brehm. Scale bar: 10 mm.
Figure 14 from: Vargas HA, Solis MA, Vargas-Ortiz M (2022) The South American moth Rheumaptera mochica (Dognin, 1904) (Lepidoptera, Geometridae, Larentiinae) rediscovered after more than a century of anonymity. ZooKeys 1085: 129-143. https://doi.org/10.3897/zookeys.1085.76868
Figure 14 Rheumaptera mochica (Dognin, 1904), geographic distribution. Star indicates type locality (Arequipa, Peru), circles indicate new distribution records in northern Chile.
Figure 12 from: Vargas HA, Solis MA, Vargas-Ortiz M (2022) The South American moth Rheumaptera mochica (Dognin, 1904) (Lepidoptera, Geometridae, Larentiinae) rediscovered after more than a century of anonymity. ZooKeys 1085: 129-143. https://doi.org/10.3897/zookeys.1085.76868
Figure 12 Rheumaptera mochica (Dognin, 1904) and congeners, maximum likelihood tree of DNA barcodes. Numbers indicate bootstrap values (1000 replicates).
Figures 6-11 from: Vargas HA, Solis MA, Vargas-Ortiz M (2022) The South American moth Rheumaptera mochica (Dognin, 1904) (Lepidoptera, Geometridae, Larentiinae) rediscovered after more than a century of anonymity. ZooKeys 1085: 129-143. https://doi.org/10.3897/zookeys.1085.76868
Figures 6-11 Rheumaptera mochica (Dognin, 1904), genitalia 6 male genitalia, ventral view, phallus removed 7 basal process of sacculus projection (rectangle in Fig. 6) 8 phallus 9 cornuti 10 female genitalia in ventral view 11 signa (rectangle in Fig. 10). Scale bar: 1 mm.
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.