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84 results for “Common model”
Global scientific research commons under the Nagoya Protocol: Towards a collaborative economy model for the sharing of basic research assets
<p>This paper aims to get a better understanding of the motivational and transaction cost features of<br /> building global scientific research commons, with a view to contributing to the debate on the design of<br /> appropriate policy measures under the recently adopted Nagoya Protocol. For this purpose, the paper<br /> analyses the results of a world-wide survey of managers and users of microbial culture collections, which<br /> focused on the role of social and internalized motivations, organizational networks and external<br /> incentives in promoting the public availability of upstream research assets. Overall, the study confirms<br /> the hypotheses of the social production model of information and shareable goods, but it also shows the<br /> need to complete this model. For the sharing of materials, the underlying collaborative economy in<br /> excess capacity plays a key role in addition to the social production, while for data, competitive pressures<br /> amongst scientists tend to play a bigger role.</p>
Common Phone: A Multilingual Dataset for Robust Acoustic Modelling
<p><em>Release Date: 17.01.22</em></p> <p><strong>Welcome to Common Phone 1.0</strong></p> <p><strong>Legal Information</strong></p> <p><em>Common Phone</em> is a subset of the <em>Common Voice</em> corpus collected by <em>Mozilla Corporation</em>. By using <em>Common Phone</em>, you agree to the <a href="https://commonvoice.mozilla.org/en/terms">Common Voice Legal Terms</a>. <em>Common Phone</em> is maintained and distributed by speech researchers at the <a href="https://lme.tf.fau.de/">Pattern Recognition Lab</a> of Friedrich-Alexander-University Erlangen-Nuremberg (<a href="https://www.fau.de/">FAU</a>) under the <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0 license</a>.</p> <p>Like for <em>Common Voice</em>, you must not make any attempt to identify speakers that contributed to <em>Common Phone</em>.</p> <p><strong>About <em>Common Phone</em></strong></p> <p>This corpus aims to provide a basis for Machine Learning (ML) researchers and enthusiasts to train and test their models against a wide variety of speakers, hardware/software ecosystems and acoustic conditions to improve generalization and availability of ML in real-world speech applications.<br> The current version of <em>Common Phone</em> comprises 116,5 hours of speech samples, collected from 11.246 speakers in 6 languages:</p> <table align="center"> <thead> <tr> <th> <p><strong>Language</strong></p> </th> <th> <p><strong>Speakers</strong></p> </th> <th> <p><strong>Hours</strong></p> </th> </tr> </thead> <tbody> <tr> <td> </td> <td> <p><code>train</code> / <code>dev</code> / <code>test</code></p> </td> <td> <p><code>train</code> / <code>dev</code> / <code>test</code></p> </td> </tr> <tr> <td> <p>English</p> </td> <td> <p>4716 / 771 / 774</p> </td> <td> <p>14.1 / 2.3 / 2.3</p> </td> </tr> <tr> <td> <p>French</p> </td> <td> <p>796 / 138 / 135</p> </td> <td> <p>13.6 / 2.3 / 2.2</p> </td> </tr> <tr> <td> <p>German</p> </td> <td> <p>1176 / 202 / 206</p> </td> <td> <p>14.5 / 2.5 / 2.6</p> </td> </tr> <tr> <td> <p>Italian</p> </td> <td> <p>1031 / 176 / 178</p> </td> <td> <p>14.6 / 2.5 / 2.5</p> </td> </tr> <tr> <td> <p>Spanish</p> </td> <td> <p>508 / 88 / 91</p> </td> <td> <p>16.5 / 3.0 / 3.1</p> </td> </tr> <tr> <td> <p>Russian</p> </td> <td> <p>190 / 34 / 36</p> </td> <td> <p>12.7 / 2.6 / 2.8</p> </td> </tr> <tr> <td> <p><strong>Total</strong></p> </td> <td> <p>8417 / 1409 / 1420</p> </td> <td> <p>85.8 / 15.2 / 15.5</p> </td> </tr> </tbody> </table> <p> </p> <p>Presented <code>train</code>, <code>dev</code> and <code>test</code> splits are <strong>not identical</strong> to those shipped with <em>Common Voice</em>. Speaker separation among splits was realized by only using those speakers that had provided age and gender information. This information can only be provided as a registered user on the website. When logged in, the session ID of contributed recordings is always linked to your user, thus we could easily link recordings to individual speakers. Keep in mind this would not be possible for unregistered users, as their session ID changes if they decide to contribute more than once.<br> During speaker selection, we considered that some speakers had contributed to more than one of the six <em>Common Voice</em> datasets (one for each language). In <em>Common Phone</em>, a speaker will only appear in one language.<br> The dataset is structured as follows:</p> <ul> <li>Six top-level directories, one for each language.</li> <li>Each language folder contains: <ul> <li>[train|dev|test].csv files listing audio files, respective speaker ID and plain text transcript.</li> <li>meta.csv provides speaker information: age group, gender, language, accent (if available) and which of the three splits this speaker was assigned to. File names match corresponding audio file names except their extension.</li> <li>/grids/ contains phonetic transcription for every audio file in Praat TextGrid format.</li> <li>/mp3/ contains audio files in mp3, identical to those of <em>Common Voice</em>, e.g., sampling rates have been preserved and may vary for different files.</li> <li>/wav/ contains raw audio files in 16 bits/sample, 16 kHz single channel. They had been created from the original mp3 audios. We provide them for convenience, keep in mind that their source had undergone MP3-compression.</li> </ul> </li> </ul> <p><strong>Where does the phonetic annotation come from?</strong></p> <p>Phonetic annotation was computed via <a href="https://clarin.phonetik.uni-muenchen.de/BASWebServices/interface/Pipeline">BAS Web Services</a>. We used the regular Pipeline (G2P-MAUS) without ASR to create an alignment of text transcripts with audio signals. We chose International Phonetic Alphabet (IPA) output symbols as they work well even in a multi-lingual setup. <em>Common Phone</em> annotation comprises 101 phonetic symbols, including silence.</p> <p><strong>Why <em>Common Phone</em>?</strong></p> <ul> <li>Large number of speakers and varying acoustic conditions to improve robustness of ML models</li> <li>Time-aligned IPA phonetic transcription for every audio sample</li> <li>Gender-balanced and age-group-matched (equal number of female/male speakers in every age group)</li> <li>Support for six different languages to leverage multi-lingual approaches</li> <li>Original MP3 files plus standard WAVE files</li> </ul> <p><strong>Is there any publication available?</strong></p> <p><em>Yes, a paper describing Common Phone in detail is currently under revision for LREC </em><em>2022. You can access a pre-print version on arXiv entitled “<a href="https://arxiv.org/abs/2201.05912">Common Phone: A Multilingual Dataset for Robust Acoustic Modelling</a>”.</em></p>
Data for common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline
<p>This upload contains the HZV029 Plasma and HZV029 Two-Phase dataset for reviewers of the "Data for common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline" submission. </p> <p>Both datasets will be uploaded to metabolomics workbench and the upload completed before final publication of the manuscript. For the he HZV029 Plasma datasets only the final run is included for any sample (i.e., failed injections or other samples with data quality issues that were reran during acquisition were omitted).</p> <p>Also included in the upload is the source code for the MetDataModel and the pcpfm at the time of manuscript re-submission and the pcpfm itself. If you find this upload in the future, please check out the github repos for more updated versions:</p> <p>https://github.com/shuzhao-li-lab/PythonCentricPipelineForMetabolomics</p> <p>https://github.com/shuzhao-li-lab/metDataModel</p> <p>The github repo does not store the input the data for space reasons, they only have the notebooks. However, the .zip here has both the notebooks by themselves in the notebook subdirectory and a separate directory with the notebooks and the data used to generate all the figures and results in the manuscript.</p> <p><strong>Some information that is needed to rerun this analysis:</strong></p> <p>Sequence files are critical to the functioning of the pipeline. The sequence files for all analyses are provided under sequence_files.zip. These can be used to recapitulate the analysis by eitehr changing the filepath to each acquisition to where you put it on your sytem or by placing the sequence file in the same directory as the mzml or raw. In the latter case, the pipeline will search for filenames matching the sample names. The sequence files also store some sample metadata such as the type of sample a given acquisition is (unknown, pooled, qc, etc...)</p> <p>.raw to .mzML conversion works well on MacOS but may not work well on other systems. You will need to use the ability to specify your own conversion command or convert files outside of the pipeline. </p> <p>To replicate the results, you do need to have the annotation sources downloaded which can be done using the pipeline. MS2 annotation requires the files in the AcquireX directory which is MS2 acquisitions on pooled HZV029 plasma samples.</p> <p>For the comparison between MetaboAnalystR and the pcpfm, subsets of the datasets were used. These subsets and the sequence files are in Subsets_for_performance_testing.zip. The sequences are also in the sequence_files directory as well</p> <p>The notebooks reference data in the analysis folders. Copies of these files are located with the notebooks to ease reproduction of the exact results in the paper; however, to do so, you will need to change paths to this data in the notebook. This lets the notebooks be ran during a rerun without copying intermediates back and forth and it keeps the github repo clean.</p> <p><strong>Version History:</strong></p> <p>This version is after reviewer comments and is for resubmission.</p> <p> </p> <p><strong>Contributions:</strong></p> <p>Joshua M Mitchell implemented the pipeline and was first author on the manuscript. Shuzhao Li is the corresponding author on the manuscript. </p> <p>Maheshwor Thapa performed the experiments to collect the HZV029 data. Yuanye Chi helped with testing and documenting the pipeline. </p> <p>Jiangou (Jeff) Xia and Zhiqiang Pang provided the R portion of the analysis. </p>
Mapping Across OpenAIRE And Argos Data Models And The DMP Common Standard
<p>This is the outcome of the mapping activity across the data models of Argos and the OpenAIRE Research Graph and the RDA DMP Common Standard.</p>
Common guillemots in the Baltic Sea studied with video surveillance and object detection: raw data, annotations, model, and model outputs
<p>The data comes from common guillemots studied at Stora Karlsö, Sweden between 2019 and 2021. The common guillemots breed at an artificial cliff, and has been filmed continusly from above over three breeding seasons. Using the video material, a YOLOv5 model has been trained to detect adult birds, chicks and eggs. The dataset contains annotations (bounding boxes) used for training the model, the model itself, and outputs from the model (object detections).</p> <p>The data can be used and shared freely.</p>
NIVA Common Semantic Model
<p>NIVA has developed under Enterprise Architect a UML data model for some pieces of IACS data or processes, namely core geographic data, EO monitoring and Farm Registry. The NIVA model may be found under: EU Common Agricultural Model /Conceptual model. More detailed explanations about content of this model may be found in NIVA deliverable D3.2 Common Semantic Model, available on the NIVA web site : <a href="https://www.niva4cap.eu/deliverables/">Deliverables – Niva4cap</a> </p>
Retinal proteome profiling of inherited retinal degeneration across three different mouse models suggests common drug targets in retinitis pigmentosa
Open the record for dataset details and reuse information.
Data from: Subgenome-informed statistical modeling of transcriptomes in 25 common wheat accessions reveals cis- and trans- regulation architectures
Open the record for dataset details and reuse information.
Common guillemots in the Baltic Sea studied with video surveillance and object detection: raw data, annotations, model, and model outputs
Open the record for dataset details and reuse information.
Wide post-common envelope binaries from Gaia: orbit validation and formation models
<p>Inlists and run_star_extras used in the models of Yamaguchi et al. (2024, submitted to PASP, arXiv:2405.06020<span>) ("Wide post-common envelope binaries from Gaia: orbit validation and formation models"). See Rees et al. (2024) for details about TP-AGB routines.</span></p> <p>MESA r22.05.1, measdk 22.6.1</p>
CONCEPT-HF Common Data Model
<p><span>Common Data Model for Cohort Analysis of Patients in the CONCEPT-HF Project.</span></p> <p><span>This data model includes cohort definition information with the specification for selecting hospitalization episodes due to heart failure based on the primary diagnosis (i.e., ICD-9 or ICD-10), and the specification and definition of the minimum set of variables required to accomplish the study's objectives. </span></p> <p><span>A schematic figure is also included to depict the expected sources of the required information within the information systems of different levels of care, according to the sequence of the care process.</span></p> <p><strong><span>Aims of the CONCEPT-HF study</span></strong></p> <p><strong><span>General Objective</span></strong></p> <p><span>The CONCEPT-IC project aims to analyze the effectiveness of the healthcare process experienced by HF patients.</span></p> <p><span>Specific Objectives are to identify and characterize the healthcare trajectories experienced by HF patients within the healthcare system and compare care pathways experienced by HF patients with the theoretical trajectories derived from clinical guidelines, including process indicators, diagnosis, clinical follow-up, and pharmacological treatment recommendations.</span></p> <p><span>A third point of interest is to evaluate the effect of patients' and healthcare providers' adherence to clinical guidelines on health outcomes.</span></p> <p><span>Finally, we want to evaluate the quality improvement strategies based on HF Programs concerning process and outcome indicators, analyzing deviations from observed healthcare trajectories compared to theoretical ones and the differences in HF care and outcomes between three Spanish healthcare systems: Andalusian, Aragonese, and Basque.</span></p>
Semantic Triples from "A Collaborative, Realism-Based, Electronic Healthcare Graph: Public Data, Common Data Models, and Practical Instantiation"
<p>These RDF triples (<a href="https://zenodo.org/api/files/3d3308cb-8221-4a17-abc5-0ae32bb33f26/synthea_graph_exportable.nq.zip?versionId=7aff7c4a-bb0c-46ae-b006-7a80bcec0925">synthea_graph_exportable.nq.zip</a>) are the result of modeling electronic health records (<a href="https://zenodo.org/api/files/3d3308cb-8221-4a17-abc5-0ae32bb33f26/synthea_csv_output_turbo_cannonical.zip">synthea_csv_output_turbo_cannonical.zip), </a>that were synthesized with the Synthea software (https://github.com/synthetichealth/synthea). Anyone who loads them into a triplestore database is encouraged to provide feedback at https://github.com/PennTURBO/EhrGraphCollab/issues. The following abstract comes from a paper, describing the semantic instantiation process, and presented to the ICBO 2019 conference (https://drive.google.com/file/d/1eYXTBl75Wx3XPMmCIOZba-8Cv0DIhlRq/view).</p> <p>ABSTRACT: There is ample literature on the semantic modeling of biomedical data in general, but less has been published on realism-based, semantic instantiation of electronic health records (EHR). Reasons include difficult design decisions and issues of data governance. A collaborative approach can address design and technology utilization issues, but is especially constrained by limited access to the data at hand: protected health information.</p> <p>Effective collaboration can be facilitated by public EHR-like data sets, which would ideally include a large variety of datatypes mirroring actual EHRs and enough records to drive a performance assessment. An investment into reading public EHR-like data from a popular common data model (CDM) is preferable over reading each public data set’s native format.</p> <p>In addition to identifying suitable public EHR-like data sets and CDMs, this paper addresses instantiation via relational-to-RDF mapping. The completed instantiation is available for download, and a competency question demonstrates fidelity across all discussed formats.</p>
Dataset for Assessment of Commonly Used Equivalent Circuit Models for Corrosion Analysis and Frequency Reduction Analysis
<p>Datasets for Submission: "<strong>An Assessment of Commonly Used Equivalent Circuit Models for Corrosion Analysis: A Bayesian Approach to Electrochemical Impedance Spectroscopy</strong>"</p> <p>This dataset accompanies the above-mentioned submission and includes the following compressed files:</p> <p>Generated EIS Datasets for Three Corrosion Systems Mentioned in the submission and corresponding Nyquist Plots:</p> <ul> <li>Uniform corrosion system: <em><a href="../api/records/13351508/draft/files/Uniform_Generated%20data%20and%20Nyquist%20plots.rar/content" target="_blank" rel="noopener noreferrer">Uniform_Generated data and Nyquist plots.rar</a></em></li> <li>Passivating system: <em><a href="../api/records/13351508/draft/files/Passivating_Generated%20EIS%20data%20and%20Nyquist%20plots.rar/content" target="_blank" rel="noopener noreferrer">Passivating_Generated EIS data and Nyquist plots.rar</a></em></li> <li>Coating system: <em><a href="../api/records/13351508/draft/files/Coating_Generated%20EIS%20data%20and%20Nyquist%20plots.rar/content" target="_blank" rel="noopener noreferrer">Coating_Generated EIS data and Nyquist plots.rar</a></em></li> </ul> <p>Characteristic EIS Data Selected for Frequency Reduction Analysis:</p> <ul> <li>Uniform corrosion system: <em><a href="../api/records/13351508/draft/files/Uniform_charac_EIS.npy/content" target="_blank" rel="noopener noreferrer">Uniform_charac_EIS.npy</a></em></li> <li>Passivating system: <em><a href="../api/records/13351508/draft/files/Passivating_charac_EIS.npy/content" target="_blank" rel="noopener noreferrer">Passivating_charac_EIS.npy</a></em></li> <li>Coating system: <em><a href="../api/records/13351508/draft/files/Coating_charac_EIS.npy/content" target="_blank" rel="noopener noreferrer">Coating_charac_EIS.npy</a></em></li> </ul>
Different schemes that are most commonly used for the evaluation and benchmarking of climate models.
<p>Different schemes that are most commonly used for the evaluation and benchmarking of climate models. Most of the schemes can be applied to different realms (e.g. atmosphere, ocean, land...), and each scheme can include more than one diagnostic or metric. Scheme 1 only includes the portrait plot as metric which is very versatile in its application across different domains, analysed variables and number of included observations or time periods. Scheme 2 represents all diagnostics that are based on analyses of biases and variabilities. Scheme 3 includes all diagnostics that focus on spatial analyses, e.g. spatial correlations or physical connections between neighboring regions/realms. Scheme 4 includes any budget assessments. These diagnostics are commonly applied globally, but can also be applied regionally if boundary conditions and fluxes across boundaries are clearly defined. Scheme 5 represents all other statistical approaches for model evaluation, e.g. the analyses of distributions. Scheme 6 finally includes all diagnostics that aim for describing Earth System and its interconnections and changes as a whole, e.g. emergent constraints or equilibrium climate sensitivity (ECS).</p>
A White Matter Atlas and Common Connectivity Space Facilitate the Pig as a Translational Model in Neuroscience
<p>Dataset accompanying the preprint <em>"A White Matter Atlas and Common Connectivity Space Facilitate the Pig as a Translational Model in Neuroscience"</em>. Contained here are the files used for the cross-species browser spec files, as well as the raw and preprocessed pig diffusion-weighted data, and their raw anatomical files and surfaces. </p> <p>The preprint is available here: <a href="https://www.biorxiv.org/content/10.1101/2020.10.13.337436">https://www.biorxiv.org/content/10.1101/2020.10.13.337436</a></p> <p>And the associated code and lighter files can be found here: <a href="https://github.com/neurabenn/pig_connectivity_bp_preprint">https://github.com/neurabenn/pig_connectivity_bp_preprint</a></p> <p> </p>
Data from: Microhabitat selection in the common lizard: implications of biotic interactions, age, sex, local processes, and model transferability among populations
Modeling species' habitat requirements are crucial to assess impacts of global change, for conservation efforts and to test mechanisms driving species presence. While the influence of abiotic factors has been widely examined, the importance of biotic factors and biotic interactions, and the potential implications of local processes are not well understood. Testing their importance requires additional knowledge and analyses at local habitat scale. Here, we recorded the locations of species presence at the microhabitat scale and measured abiotic and biotic parameters in three different common lizard (Zootoca vivipara) populations using a standardized sampling protocol. Thereafter, space use models and cross-evaluations among populations were run to infer local processes and estimate the importance of biotic parameters, biotic interactions, sex, and age. Biotic parameters explained more variation than abiotic parameters, and intraspecific interactions significantly predicted the spatial distribution. Significant differences among populations in the relationship between abiotic parameters and lizard distribution, and the greater model transferability within populations than between populations are in line with effects predicted by local adaptation and/or phenotypic plasticity. These results underline the importance of including biotic parameters and biotic interactions in space use models at the population level. There were significant differences in space use between sexes, and between adults and yearlings, the latter showing no association with the measured parameters. Consequently, predictive habitat models at the population level taking into account different sexes and age classes are required to understand a specie's ecological requirements and to allow for precise conservation strategies. Our study therefore stresses that future predictive habitat models at the population level and their transferability should take these parameters into account.
Data from: Towards a common methodology for developing logistic tree mortality models based on ring-width data
Tree mortality is a key process shaping forest dynamics. Thus, there is a growing need for indicators of the likelihood of tree death. During the last decades, an increasing number of tree-ring based studies have aimed to derive growth–mortality functions, mostly using logistic models. The results of these studies, however, are difficult to compare and synthesize due to the diversity of approaches used for the sampling strategy (number and characteristics of alive and death observations), the type of explanatory growth variables included (level, trend, etc.), and the length of the time window (number of years preceding the alive/death observation) that maximized the discrimination ability of each growth variable. We assess the implications of key methodological decisions when developing tree-ring based growth–mortality relationships using logistic mixed-effects regression models. As examples, we use published tree-ring datasets from Abies alba (13 different sites), Nothofagus dombeyi (one site), and Quercus petraea (one site). Our approach is based on a constant sampling size and aims at (1) assessing the dependency of growth–mortality relationships on the statistical sampling scheme used, (2) determining the type of explanatory growth variables that should be considered, and (3) identifying the best length of the time window used to calculate them. The performance of tree-ring-based mortality models was reasonably high for all three species (area under the receiving operator characteristics curve, AUC > 0.7). Growth level variables were the most important predictors of mortality probability for two species (A. alba, N. dombeyi), while growth-trend variables need to be considered for Q. petraea. In addition, the length of the time window used to calculate each growth variable was highly uncertain and depended on the sampling scheme, as some growth–mortality relationships varied with tree age. The present study accounts for the main sampling-related biases to determine reliable species-specific growth–mortality relationships. Our results highlight the importance of using a sampling strategy that is consistent with the research question. Moving towards a common methodology for developing reliable growth–mortality relationships is an important step towards improving our understanding of tree mortality across species and its representation in dynamic vegetation models.
Data from: Examining temporal sample scale and model choice with spatial capture-recapture models in the common leopard Panthera pardus
Many large carnivores occupy a wide geographic distribution, and face threats from habitat loss and fragmentation, poaching, prey depletion, and human wildlife-conflicts. Conservation requires robust techniques for estimating population densities and trends, but the elusive nature and low densities of many large carnivores make them difficult to detect. Spatial capture-recapture (SCR) models provide a means for handling imperfect detectability, while linking population estimates to individual movement patterns to provide more accurate estimates than standard approaches. Within this framework, we investigate the effect of different sample interval lengths on density estimates, using simulations and a common leopard (Panthera pardus) model system. We apply Bayesian SCR methods to 89 simulated datasets and camera-trapping data from 22 leopards captured 82 times during winter 2010–2011 in Royal Manas National Park, Bhutan. We show that sample interval length from daily, weekly, monthly or quarterly periods did not appreciably affect median abundance or density, but did influence precision. We observed the largest gains in precision when moving from quarterly to shorter intervals. We therefore recommend daily sampling intervals for monitoring rare or elusive species where practicable, but note that monthly or quarterly sample periods can have similar informative value. We further develop a novel application of Bayes factors to select models where multiple ecological factors are integrated into density estimation. Our simulations demonstrate that these methods can help identify the "true" explanatory mechanisms underlying the data. Using this method, we found strong evidence for sex-specific movement distributions in leopards, suggesting that sexual patterns of space-use influence density. This model estimated a density of 10.0 leopards/100 km2 (95% credibility interval: 6.25–15.93), comparable to contemporary estimates in Asia. These SCR methods provide a guide to monitor and observe the effect of management interventions on leopards and other species of conservation interest.
Integrating laboratory experiments and biogeographic modelling approaches to understand sensitivity to ocean warming in rare and common marine annelids
<p><span>Among ectotherms, rare species are expected to have a narrower thermal niche breadth and reduced acclimation capacity and thus be more vulnerable to global warming than their common relatives. To assess these hypotheses, we experimentally quantified the thermal sensitivity of seven common, uncommon, and rare species of temperate marine annelids of the genus <em>Ophryotrocha</em> to assess those species' vulnerability to ocean warming. We measured the upper and lower limits of physiological thermal tolerance, survival, and reproductive performance of each species along a temperature gradient (18, 24, and 30 °C). We then combined this information to produce curves of each species' fundamental thermal niche by including trait plasticity. Each thermal curve was then</span> <span>expressed as a habitat suitability index (HSI) and projected for the Mediterranean Sea and temperate Atlantic Ocean under a present day (1970-2000), mid- (2050-2059) and late- (2090-2099) 21st Century scenario for two climate change scenarios (RCP2.6 and RCP8.5). Rare and uncommon species showed a reduced upper thermal tolerance compared to common species, and the niche breadth and acclimation capacity were comparable among groups. The simulations predicted an overall increase in the HSI for all species and identified potential hotspots of HSI decline for uncommon and rare species</span> <span>along the warm boundaries of their potential distribution, though they failed to project the higher sensitivity of these species into a greater vulnerability to ocean warming. In the discussion, we provide elements and caveats on the implications of our results for conservation efforts.</span></p>
The effects of exploratory behavior on physical activity in a common animal model of human disease, zebrafish (Danio rerio)
<p>Zebrafish (Danio rerio) are widely accepted as a multidisciplinary vertebrate model for neurobehavioral and clinical studies, and more recently have become established as a model for exercise physiology and behavior. Individual differences in activity level (e.g., exploration) have been characterized in zebrafish, however, how different levels of exploration correspond to differences in motivation to engage in swimming behavior has not yet been explored. We screened individual zebrafish in two tests of exploration: the open field and novel tank diving tests. The fish were then exposed to a tank in which they could choose to enter a compartment with a flow of water (as a means of testing voluntary motivation to exercise). After a 2-day habituation period, behavioral observations were conducted. We used correlative analyses to investigate the robustness of the different exploration tests. Due to the complexity of dependent behavioral variables, we used machine learning to determine the personality variables that were best at predicting swimming behavior. Our results show that contrary to our predictions, the correlation between novel tank diving test variables and open field test variables was relatively weak. Novel tank diving variables were more correlated with themselves than open field variables were to each other. Males exhibited stronger relationships between behavioral variables than did females. In terms of swimming behavior, fish that spent more time in the swimming zone spent more time actively swimming, however, swimming behavior was inconsistent across the time of the study. All relationships between swimming variables and exploration tests were relatively weak, though novel tank diving test variables had stronger correlations. Machine learning showed that three novel tank diving variables (entries top/bottom, movement rate, average top entry duration) and one open field variable (proportion of time spent frozen) were the best predictors of swimming behavior, demonstrating that the novel tank diving test is a powerful tool to investigate exploration. Increased knowledge about how individual differences in exploration may play a role in swimming behavior in zebrafish is fundamental to their utility as a model of exercise physiology and behavior.</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.