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1,855 results for “framework”
Undirected Node Attributed Social Network Graph of Twitter Users interested in plastic pollution - created in the framework of the PlasticTwist project
<p>This dataset has been created in the framework of the Plastic Twist project (<a href="https://ptwist.eu/">Ptwist</a>) and more specifically using the Ptwist crowdsourcing application (<a href="https://crowdsourcing.plastictwist.com/">crowdsourcing.plastictwist.com/</a>). We are sharing the edge list and specific node attributes (hashtags) of Twitter users posting about plastic pollution. The dataset can be used for community detection,clustering, node importance, influence maximization tasks, etc. Each user is represented by a unique integer which has nothing to do with the official Twitter user ID. The dataset contains three (3) files: </p> <ul> <li>ptwist.edgelist: A list containing all the 1,362,863 edges between the users. When loaded they create an undirected graph of 800K+ users.</li> <li>node_attributes.txt: This file contains information about the hashtags used by each user. (e.g. "652003": ["SingleUsePlastic"] -> user 6529003 has used the hashtag SingleUsePlastic) </li> <li>annotated_graph: A pickle file which, when loaded, returns a <a href="https://networkx.github.io/">NetworkX</a> node attributed undirected graph.</li> </ul> <p> </p> <p> </p>
DFT-optimized Computation-Ready Experimental Metal-Organic Framework (CoRE MOF) 2014
<p>There are two folders inside the zipped file:</p> <p>- 838 structures (without DDEC partial atomic charges)</p> <p>- 502 structures (with DDEC partial atomic charges)<br> </p> <p> </p>
A unifying framework for mean-field theories of asymmetric kinetic Ising systems [Dataset]
<p>Datasets for reproducing the results in the article Aguilera, M., Moosavi, S.A. & Shimazaki, H. A unifying framework for mean-field theories of asymmetric kinetic Ising systems. <em>Nature Communications</em> <strong>12, </strong>1197 (2021). https://doi.org/10.1038/s41467-021-20890-5. Results can be reproduced using the code repository of the article https://github.com/MiguelAguilera/kinetic-Plefka-expansions</p> <p>The main dataset contains simulations of an asymmetric, kinetic Sherrington-Kirkpatrick (SK) model around the equivalent of a ferromagnetic phase transition in the equilibrium SK model. External fields <span class="math-tex">\(H_i\)</span> are sampled from independent uniform distributions <span class="math-tex">\(\mathcal{U}(-\beta H_0, \beta H_0)\)</span> with <span class="math-tex">\(H_0=0.5\)</span>, whereas coupling terms <span class="math-tex">\(J_{ij}\)</span> are sampled from independent Gaussian distributions <span class="math-tex">\(\mathcal{N}(\beta \frac{J_0}{N},\beta^2 \frac{J_\sigma^2}{N})\)</span>, with <span class="math-tex">\(J_0=1, J_\sigma = 0.1\)</span> where <span class="math-tex">\(\beta\)</span> is a scaling parameter (i.e., an inverse temperature).</p> <p>To study the non-stationary transient dynamics of the model, we start from <span class="math-tex">\(\mathbf s_0 = \mathbf 1\)</span> (all elements set to 1 at <span class="math-tex">\(t=0\)</span>) and recursively update its state for <span class="math-tex">\(T=128\)</span> steps. We repeated this stochastic simulation for <span class="math-tex">\(10^6\)</span> trials for 21 values of <span class="math-tex">\(\beta\)</span> in the range <span class="math-tex">\([0.7\beta_c, 1.3\beta_c]\)</span>, except for the reconstruction of the phase transition where we used <span class="math-tex">\(R=10^5\)</span> and 201 values of <span class="math-tex">\(\beta\)</span> in the same range.<br> <br> Each file is stored in: 'data-H0-0.5-J0-1.0-Js-0.1-N-512-R-1000000-beta-[beta_ref].npz', where [beta_ref] contains the normalized value of <span class="math-tex">\(\beta/\beta_C\)</span> between 0.7 and 1.3.<br> <br> Furthermore, data in the folders 'forward.zip', 'inverse.zip' and 'reconstruction.zip' contain files to reproduce the results of the paper above. These files show the results of solving the forward Ising problem, the inverse Ising problem, and the reconstruction of the phase transition combining forward and inverse problems.</p>
A framework for step-wise explaining how to solve constraint satisfaction problems
<p>We explore the problem of step-wise explaining how to solve constraint satisfaction problems, with a use case on logic grid puzzles. More specifically, we study the problem of explaining the inference steps that one can take during propagation, in a way that is easy to interpret for a person. Thereby, we aim to give the constraint solver explainable agency, which can help in building trust in the solver by being able to understand and even learn from the explanations. The main challenge is that of finding a sequence of simple explanations, where each explanation should aim to be as cognitively easy as possible for a human to verify and understand. This contrasts with the arbitrary combination of facts and constraints that the solver may use when propagating. We propose the use of a cost function to quantify how simple an individual explanation of an inference step is, and identify the explanation-production problem of finding the best sequence of explanations of a CSP. Our approach is agnostic of the underlying constraint propagation mechanisms, and can provide explanations even for inference steps resulting from combinations of constraints. In case multiple constraints are involved, we also develop a mechanism that allows to break the most difficult steps up and thus gives the user the ability to zoom in on specific parts of the explanation. Our proposed algorithm iteratively constructs the explanation sequence by using an optimistic estimate of the cost function to guide the search for the best explanation at each step. Our experiments on logic grid puzzles show the feasibility of the approach in terms of the quality of the individual explanations and the resulting explanation sequences obtained.</p>
Data release for "OrchID: a Generalized Framework for Taxonomic Classification of Images Using Evolved Artificial Neural Networks"
<p><strong>Abstract</strong></p> <p>Taxonomic expertise for the identification of species is rare and costly. On-going advances in computer vision and machine learning have led to the development of numerous semi- and fully automated species identification systems. However, these systems are rarely agnostic to specific morphology, rarely can perform taxonomic “approximation” (by which we mean partial identification at least to higher taxonomic level if not to species), and frequently rely on costly scientific imaging technologies.</p> <p>We present a generic, hierarchical identification system for automated taxonomic approximation of organisms from images. We assessed the effectiveness of this system using photographs of slipper orchids (Cypripedioideae), for which we implemented image pre-processing, segmentation, and colour and shape feature extraction algorithms to obtain digital phenotypes for 116 species. The identification system trained on these digital phenotypes uses a nested hierarchy of artificial neural networks for pattern recognition and automated classification that mirrors the Linnean taxonomy, such that user-submitted photos can be assigned a genus, section, and species classification by traversing this hierarchy.</p> <p>Performance of the identification system varied depending on photo quality, number of species included for training, and desired taxonomic level for identification. High quality photos were scarce for some taxa and were under-represented in the training set, resulting in imbalanced network training. The image features used for training were sufficient to reliably identify photos to the correct genus but less so to the correct section and species.</p> <p>The outcomes of this project include a library of feature extraction algorithms called <em>ImgPheno</em>, a collection of scripts for neural network training called <em>NBClassify</em>, a library for evolutionary optimization of artificial neural network construction called <em>AI::FANN::Evolving</em> and a planned web application called <em>OrchID</em> for identification of user-submitted images. All project outcomes are open source and freely available.</p> <p><strong>About this release</strong></p> <p>This release corresponds belongs with our response to the reviewers of PLoS One. At this stage of the review cycle the manuscript is assessed as 'minor revision'. Consequently, we don't anticipate making more releases until publication.</p>
Model outputs from the study "A scalable framework for soil property mapping tested across a highly diverse tropical data-scarce region"
<p>Model outputs from the study "A scalable framework for soil property mapping tested across a highly diverse tropical data-scarce region". The study is published as open access and can be found at the following link: <a href="https://www.sciencedirect.com/science/article/pii/S2950289625000326">https://www.sciencedirect.com/science/article/pii/S2950289625000326</a></p> <p> </p> <p>The file "SWAT_USERSOIL.csv" was included to facilitate the assimilation of the soil mapping data into the Soil & Water Assessment Tool (SWAT, https://swat.tamu.edu/) for hydrological modeling. </p> <p> </p> <p>Regarding the raster files, please note:</p> <p>a) All values in these datasets have been multiplied by 10,000 to optimize file sizes.</p> <p>b) Files are named using the variable acronym, followed by the corresponding soil layer. For outputs derived from pedotransfer functions (PTFs), the PTF reference is appended after the variable acronym.</p> <p>c) Available data decrease with increasing soil layer number. This occurs because not all locations (grid cells) have the same soil depth or number of soil layers.</p> <p> </p> <p>If you have any questions about the dataset or its use, please don't hesitate to contact us.</p> <p> </p> <p> </p>
Datasets of synthetic task graphs for evaluating a reliability and latency multi-objective task allocation framework
<p>These datasets of synthetic task graphs were generated to evaluate the performance and scalability of a multi-objective task allocation approach for workflow applications of various structures and sizes in a system based on the edge-hub-cloud paradigm. The targeted architecture comprised an edge device (e.g., a single-board computer attached to an unmanned aerial vehicle (UAV)) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. The objectives were the maximization of the overall reliability and the minimization of the overall latency of the application, under memory, storage, energy, and task precedence constraints. We considered that a percentage of the tasks required fixed allocation on the edge or hub device. Each task had a different vulnerability factor (i.e., probability of failure) on each device.</p> <p>We generated nine task graphs of serial, parallel, and mixed (a combination of serial and parallel) structure with 10, 100, and 1000 nodes, utilizing the Task Graphs For Free (TGFF) random task graph generator [1]. Additional task parameters (e.g., execution time, power consumption, vulnerability factor, memory, storage, output data size) were included post-generation, using representative random values. More details are provided in README.txt.</p> <p>Note: These datasets are released under a Creative Commons Attribution license. If you utilize these datasets in your work, please cite us using the corresponding Zenodo DOI https://doi.org/10.5281/zenodo.10357101.</p> <p>References:</p> <p>[1] R. P. Dick, D. L. Rhodes and W. Wolf, "TGFF: Task graphs for free," Proceedings of the Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE'98), Seattle, WA, USA, 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245.</p>
Stiffness Moduli Modelling and Prediction in Four-Point Bending of Asphalt Mixtures: A Machine Learning-Based Framework within Weave-UNISONO 2021 project, NCN project No 2021/03/Y/ST8/00079, and GACR project GA22-04047K
<div><strong>Summary:</strong></div> <div>Two selected mixtures were thoroughly investigated in an experimental trial carried out by means of a four-point bending test (4PBT) apparatus. The mixtures were prepared using spilite aggregate, a conventional 50/70 penetration grade bitumen, and limestone filler. Their stiffness moduli (SM) were determined while samples were exposed to 11 loading frequencies (from 0.1 to 50 Hz) and 4 testing temperatures (from 0 to 30 °C). Observations were recorded and used to develop a machine learning (ML) model. The main scope was the prediction of the stiffness moduli based on the volumetric properties and testing conditions of the corresponding mixtures, which would provide the advantage of reducing the laboratory efforts required to determine them.</div> <div> </div> <div><strong>The dataset includes:</strong></div> <div>Characteristics of bituminous binder, CSV raw data</div> <div> <ul> <li>bituminous binder.csv</li> </ul> </div> <div>Grading curves of tested asphalt mixtures</div> <ul> <li>AML16 Grading curves.csv</li> <li>AMP22 Grading curves.csv</li> </ul> <div>Volumetric characterizations of AML16 and AMP22 mixtures</div> <ul> <li>AML16 Volumetric characterizations.csv</li> <li>AMP22 Volumetric characterizations.csv</li> </ul> <div>Outcomes of the 4PBT experimental trial carried out on AML16 and AMP22 mixtures</div> <ul> <li>AML16 Stiffness Modulus 4PB.csv</li> <li>AMP22 Stiffness Modulus 4PB.csv</li> </ul>
Figure: Occupational Fatality and Health Metrics within EU Consumption and Various Supply Chain Accounting Frameworks
<p><span><strong>Occupational Fatality and Health Metrics within EU Consumption and Supply Chain Contexts.</strong> Directly taken from (Koundouri et al., 2023) and reproduced with the authors' permission. </span><span>It displays in Figure A the</span><span> work-related fatal occupational injuries tied to goods finally consumed within the EU (Consumption Based Accounting -CBA- framework) and those within supply chains passing through the EU (Throughflow Based Accounting -TBA- framework) </span><span><span>(Beaufils et al., 2023) and</span></span><span> the results denote fatalities. It also displays in Figure B the D<span>isability-Adjusted Life Years</span><em><span> (</span></em></span><span>DALYs) associated with asbestos, asthmagen, and chromium-related occupational fatalities linked to European goods consumption (CBA) and traversing supply chains (TBA). Last, in Figure C, it provides</span><span> a comparative breakdown for each commodity from panels A and B, illustrating proportions by accounting framework (TBA vs. CBA).</span></p> <p><span>This figure put forward that despite potential barriers to target direct import intervention, optimized supply chain management can markedly reduce occupational fatalities related to global value chains passing through EU. As such, ILO frameworks such as Occupational Safety and Health Convention, 2006 (No. 187) </span><span><span>(International Labour Organization, 2006)</span></span><span>, and Occupational Safety and Health Convention, 1981 (No. 155) </span><span><span>(International Labour Organization, 1981)</span></span><span> offers a path to significant fatality reductions</span></p>
Two-Electron Redox Reactivity of Thorium Supported by Redox-Active Tripodal Frameworks
<p>This upload contains raw data (NMR, X-Ray, UV, Electrochemistry and Elemental Analysis) files for the article</p>
BIObec.WP2. T2.1. BBEC Framework Design.
<p>This dataset contains Deliverable 2.1 from the BIObec project and includes</p> <ol> <li>Desk Research to create an INVENTORY OF BEST PRACTICE to inform and enhance BBEC design</li> <li>Creation of a comprehensive CENTRE READINESS LEVEL FRAMEWORK SURVEY to inform the BBEC co-creation workshops and future centre design</li> </ol> <p>All 19 BIObec consortium partners submitted at least one example of best practice in education and/or industry and circa 30 best practice examples have been collected showcasing examples from a European and international perspective. Based on the inputs from the Inventory of Best Practice a survey has been created to profile the capabilities of the six BBEC locations and highlight opportunities for collaboration that will support BBEC design. This survey comprises 22 questions and was administered to all 19 partners with an accompanying guidance document - these activities provide the basis for T2.2 and T2.3 of WP2. </p> <p> </p>
Data supporting for: A High-Throughput Framework for Lattice Dynamics
<p><span>We develop an automated high-throughput workflow for calculating lattice dynamical properties </span><span>from first principles including those dictated by anharmonicity. The pipeline automatically calculates </span><span>interatomic force constants (IFC) up to 4th order from perturbed training supercells, and uses the </span><span>IFC to calculate lattice thermal conductivity, coefficient of thermal expansion, and vibrational free </span><span>energy and entropy. It performs phonon renormalization for dynamically unstable compounds to</span><br><span>obtain real effective phonon spectra at finite temperatures and calculates the associated free energy </span><span>corrections.</span> <span>The choice of methods and parameter selection process are done in a manner that </span><span>strikes a balance of computational efficiency and accuracy of results (as assessed through convergence </span><span>testing and comparison to experimental measurements). </span></p> <p><span>The following data includes:</span></p> <ul> <li><span>materials we calculated lattice thermal conductivities (kappa folder)</span></li> <li><span>materials we applied renormalization to get corrected free energies (renormalization folder)</span></li> </ul> <p><span>The target of this dataset is giving users a reference for our inputs and outputs when they want to run our workflow by their own.</span></p>
A scoping review of the ethics frameworks describing issues related to the use of extended reality
<p>This is the dataset used for the scoping review of D2.1, which required an overview and assessment of relevant ethics frameworks, codes of conduct, guidelines regarding extended reality technologies. The specific objective is to map, review, and assess existing frameworks in terms of relevant foundational principles, ethical issues addressed, proposals to address these ethical issues, and the commonalities/differences identified between the documents.</p>
List of Standards with relevance to a DPP-IT-Framework
<p>The ESPR specifies essential requirements for the DPP system in its Articles 9 and 10, and Annex III. Based on these high-level requirements, the EU Commission is mandating the European Standardisation Organisations (ESO’s) to propose harmonized standards for the DPP system based on appropriate existing and new standards corresponding to a number of areas of standardisation. It is requested by the Commission that these standards should be available by the end of 2025. To be in compliance with the data system requirements of the DPP, upcoming DPP solutions will need to comply with the identified standards.</p> <p>One of the objectives of CIRPASS is to contribute to this effort by sharing useful results with standardisation organisations to support their work. To this end, CIRPASS identified an initial list of existing standards relevant for the establishment and operation of a cross-sectoral DPP system.</p> <p>A list of more than 300 standards, organized according to area of standardisation, was elaborated and their relevance for the areas of standardization was graded. Note that the CIRPASS proposal for the DPP System is embedded in the existing infrastructure of our networked society. This means that the architecture itself makes the assumption of the existence of the Internet and the Web with its underlying Common Technical Specifications. Many of the standards listed in this dataset make the same assumption.</p>
Data for manuscript: A framework for integrating genomics, microbial traits, and ecosystem biogeochemistry
<p>Support manuscript: A framework for integrating genomics, microbial traits, and ecosystem biogeochemistry. </p> <p>Dataset includes 1) model and analysis, and 2) supplemental data. </p> <p>In the "model_analysis" file, we include the ecosys model source code, the modeling runs, and the modeling results. The detailed introduction is in README.md file. </p> <p><strong>Acknowledgments</strong></p> <p>We thank the EMERGE Biology Integration Institute Coordinators (members listed in Supplementary Information) for project guidance and management. This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070 (V.I.R., R.K.V., S.R.S., M.B.S., E.L.B., and the EMERGE Coordinators). Additional support for individual contributors included the following. Z.L. was additionally supported by Lawrence Livermore National Laboratory under the auspices of the U.S. Department of Energy under contract DE-AC52-07NA27344. W.J.R. was supported by the Belowground Biogeochemistry Scientific Focus Area and U.K. was supported by the Watershed Function Science Area, both funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under contract no. DE-AC02-05CH11231. G.L.M. was supported by the LLNL "Microbes Persist" Soil Microbiome Scientific Focus Area SCW1632 and an associated KBase award SCW1746. N.J.B. was supported by the US Department of Energy, Office of Science (BER), Early Career Research Program (#FP00005182). B.J.W. was supported by an Australian Research Council Future Fellowship (#FT210100521). J.T. was supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory. </p> <p>We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council’s grant 4.3-2021-00164. This research used resources of the National Energy Research Scientific Computing Center (NERSC) which is a U.S. Department of Energy Office of Science user facility. This research used the Lawrencium computational cluster resource provided by the IT Division at the Lawrence Berkeley National Laboratory (Supported by the Director, Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231). </p> <p><strong>Full list of the EMERGE Biology Integration Institute Coordinators and Affiliations</strong></p> <p>Eoin L. Brodie1,2, Sarah C. Bagby3, Jeffrey P. Chanton4, Jessica G. Ernakovich5, Regis Ferriere6,7, Suzanne B. Hodgkins8, William J. Riley1, Virginia I. Rich8,9, Scott R. Saleska6, Matthew B. Sullivan8,9,10, Ruth K. Varner11, Gene W. Tyson12, Malak M. Tfaily13, Ahmed A. Zayed8,9<br> 1Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory; Berkeley, CA 94720, USA.<br>2Department of Environmental Science, Policy and Management, University of California; Berkeley, CA 94720, USA.<br>3Department of Biology, Case Western Reserve University; Cleveland, OH, USA, 44106<br>4Earth Ocean and Atmospheric Sciences, Florida State University; Tallahassee, FL, USA<br>5Department of Natural Resources and the Environment, University of New Hampshire;<br>Durham, NH, USA 03824<br>6Department of Ecology and Evolutionary Biology, University of Arizona; Tucson, AZ,<br>85721, USA<br>7Institut de Biologie de l’ENS, Université Paris Sciences & Lettres; Paris, 75005, France<br>8Department of Microbiology, The Ohio State University; Columbus, OH, USA, 43210<br>9Center of Microbiome Science, The Ohio State University; Columbus, Ohio 43210, USA.<br>10Department of Civil, Environmental and Geodetic engineering, The Ohio State University; Columbus, Ohio 43210, USA.<br>11Department of Earth Sciences and Institute for the Study of Earth, Oceans and Space, University of New Hampshire; Durham, NH 03824, USA.<br>12Centre for Microbiome Research, School of Biomedical Sciences, Queensland University<br>of Technology (QUT), Translational Research Institute; Woolloongabba, QLD, Australia<br>13Department of Environmental Science, University of Arizona; Tucson, AZ, 85721, USA</p>
Efficient and accurate framework for genome-wide gene-environment interaction analysis in large-scale biobanks
<p>Gene-environment interaction (GxE) analysis elucidates the interplay between genetic predispositions and environmental influences, offering significant potential for precision medicine. With the increasing use of electronic health records (EHR) linked to genetic data in large-scale biobanks, genome-wide association studies (GWAS) have expanded to encompass complex traits with intricate structures, such as time-to-event and ordinal categorical traits. Although these complex traits convey more phenotypic information, most existing scalable genome-wide GxE analysis approaches only focus on quantitative or binary traits. In this work, we propose a scalable and accurate analysis framework, SPAGxE<sub>CCT</sub>, that is applicable to a wide variety of trait types. We extend SPAGxE to SPAGxE+, which can account for sample relatedness. In addition, we extend SPAGxE<sub>CCT</sub> to SPAGxEmix<sub>CCT</sub>, which accounts for population stratification and is applicable to include individuals from multiple ancestries or admixed populations. We applied SPAGxE<sub>CCT</sub>, SPAGxE+, and SPAGxEmix<sub>CCT</sub> to analyze time-to-event traits in UK Biobank. For the SPAGxE<sub>CCT</sub> analyses, 281,149 White British individuals were included. For the SPAGxE+ analyses, 337,367 WB individuals with sample relatedness were included. For the SPAGxEmix<sub>CCT</sub> analyses, 338,044 individuals from all ancestries were included. SPAGxE<sub>CCT</sub>, SPAGxE+, and SPAGxEmix<sub>CCT</sub> are computationally efficient to analyze large datasets with hundreds of thousands of individuals, can accurately control type I error rates while remaining powerful to identify novel GxE findings.</p>
SpatialMETA: A Novel Framework for Integrating Spatial Transcriptomics and Metabolomics Data
<p>Multimodal analysis of spatial transcriptomics (ST) and spatial metabolomics (SM) has rapidly advanced for characterizing tissue microenvironments. However, integrating ST and SM data remains challenging due to differing morphologies, resolutions, and batch effects. We developed SpatialMETA (Spatial Metabolomics and Transcriptomics Analysis), a novel method for integrating spatial multi-omics data, which aligns ST and SM to a unified resolution, enables both cross-modal and cross-sample integration to identify ST-SM associated spatial patterns, and provides extensive visualization and analysis capabilities. The datasets for SpatialMETA is avaiable. </p>
Example code and data for ubms: An R package for fitting hierarchical occupancy and N-mixture abundance models in a Bayesian framework
<p>This repository contains an R script (grouse_example.R) and data (grouse_data.csv) used to reproduce the grouse abundance analysis described in Kellner, K. F., et al. (2021) ubms: An R package for fitting hierarchical occupancy and N-mixture abundance models in a Bayesian framework. Methods in Ecology and Evolution. The R script requires installation of the ubms R package, which can be obtained from CRAN (https://cran.r-project.org/package=ubms).</p> <p>The repository also contains an additional example occupancy analysis (occupancy_example.R) using the crossbill dataset included with the unmarked R package.</p>
Mapping Building BioData.pt Indicators against the performance and impact assessment frameworks for research infrastructures of OECD, ESFRI and RI-PATHS project
<p>"Buiding BioData.pt" indicators observed in international frameworks for performance and impact assessment of research infrastructures, namely, OECD, ESFRI and RI-PATHS.</p>
Relevance of "Building BioData.pt" indicators identified in ESFRI (E), OECD (O) and RI-PATHS (R) impact assessment frameworks
<p>The relevance of the indicators maintained by the "Building BioData.pt" project was assessed against the objectives of different organizations/initiatives: 1) Strategic objectives of BioData.pt; 2) Objectives of the Portuguese Roadmap for Research Infrastructures; 3) Objectives of ELIXIR; 4) Objectives of EOSC; 5) Sustainable Development Goals of the United Nations.</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.