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93 results for “dashboard”
Aurora SDG Research Dashboard and Classifier - Instructions Videos
<p>Instruction videos about the Aurora SDG Research Dashboard, SDG Classifier, Badges and API.</p><p><a href="https://zenodo.org/doi/10.5281/zenodo.10040524">Also read the User Guides</a>.</p>
Weekly CoMix contact matrices for UKHSA COVID-19 dashboard and ONS COVID-19 infection survey age-groups
<p>Weekly contact matrices calculated from data collected as part of the UK arm of the CoMix survey. All contact matrices were calculated over two survey rounds (SR) of data to account for alternating panels (the indicated SR and the previous SR). Full details of composition can be found in Munday et. al. [1]. Contact matrices are provided for age-groups consistent with publicly available case data from the UKHSA COVID-19 dashboard <strong> (0-9, 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70+) </strong>and publicly available aggregates of infection and antibody prevalence from the ONS COVID-19 infection survey <strong>(2-10, 11-15, 16-24, 25-34, 35-49, 49-69 and 70+)</strong>. The data is provided in qs files as 1000 bootstrapped samples of each contact matrix for weekly 'survey rounds' between 19 and 94 (see directory "survey_round_dates.csv"). The files that begin with UKHSA contain the contact matrices for the age stratification of the UKHSA COVID-19 dashboard case data. The files that begin with ONS contain the contact matrices for the age stratification of the ONS COVID-19 infection survey. </p> <p>Ethics: The study and method of informed consent were approved by the ethics committee of the London School of Hygiene & Tropical Medicine (LSHTM; reference number 21795).</p> <p>1. Munday, J.D., Jarvis, C.I., Gimma, A. <em>et al.</em> Estimating the impact of reopening schools on the reproduction number of SARS-CoV-2 in England, using weekly contact survey data. <em>BMC Med</em> <strong>19</strong>, 233 (2021). https://doi.org/10.1186/s12916-021-02107-0</p>
Interpreting future climate conditions in Brazilian cities – Dashboard and EPW files
<h3>(English)</h3> <h1>1. Introduction</h1> <p>This project aims to address the impacts of climate change on the built environment by developing a set of future Brazilian EPW (Energy Plus Weather Format) files and a dashboard to interpret and evaluate the data. The future climate files were obtained using the Future Weather Generator (FWG) [1] with climate projections for Brazilian cities, integrating these projections into a code pipeline for automation. In this part of the project, thermal comfort indices, such as the Universal Thermal Climate Index (UTCI) and the Discomfort Index (DI), were also evaluated to understand future thermal comfort conditions. The methodology followed the structure available in the <a title="Future-EPW-analysis" href="https://github.com/igorcmvaz/future-EPW-analysis" target="_blank" rel="noopener">future-EPW-analysis</a> repository:</p> <ol> <li>Climate-One-Building (COB) web-scrapping for all available Brazilian EPW files (we recommend doing this carefully so as not to damage the COB infrastructure);</li> <li>Automatic organisation of all EPW files in a folder, extracting them from the ZIP format;</li> <li>Simulation of future climate files using FutureWeatherGenerator [1] in a line of code with default parameters (shown in Table 1);</li> <li>Organisation of all available EPWs (original and simulated) in a single database;</li> <li>Calculation of thermal comfort indices using pythermalcomfort [2].</li> </ol> <p>The main objective is to provide researchers, policymakers and professionals with a comprehensive tool for assessing and mitigating the impacts of climate change in different Brazilian cities, offering accurate data for thermal comfort and energy efficiency modelling. The methodology involves generating future EPW files, validating them against existing literature and visualising the results through a user-friendly dashboard. The study highlights the importance of adaptive and climate-resilient strategies in urban planning and building design. Expected climate changes in Brazil include increased dry bulb temperature and variations in relative humidity, radiation and wind speed in the different bioclimatic zones.</p> <p>The dashboard has been designed to simplify the visualisation of future climate data, focusing on the main climate variables, thermal comfort indices and data visualisation. It allows users to filter by city and automatically calculate all the indices, providing detailed analyses and comparisons of different scenarios. By offering a free, open-access, multi-platform, extensible, customisable and easy-to-maintain tool, the project aims to facilitate continuous updates, new features and corrections. This tool supports decision-making in public policy and urban planning, promoting a more sustainable and resilient built environment in the face of climate change.</p> <p> </p> <h1>2. Further details on the methodology</h1> <p>Details on how the indices were selected and how the study was conducted may be found in Vaz et al. [3]. The GitHub repository in <a title="Future-EPW-analysis" href="https://github.com/igorcmvaz/future-EPW-analysis" target="_blank" rel="noopener">future-EPW-analysis</a> [4] also includes details on the step-by-step procedures.</p> <h3>Table 1 - Parameters used in the FWG simulation:</h3> <table> <tbody> <tr> <td> <p><strong>Parameter</strong></p> </td> <td> <p><strong>Data used in the simulations</strong></p> </td> </tr> <tr> <td> <p>Base files</p> </td> <td> <p>578 cities from COB</p> </td> </tr> <tr> <td> <p>CMIP-6 models</p> </td> <td> <p>BCC-CSM2-MR, CAS-ESM2.0, CMCC-ESM2, CNRM-CM6.1-HR, CNRM-ESM2.1, EC-Earth3, EC-Earth3-Veg, MIROC-ES2H, MIROC6, MRI-ESM2.0, UKESM1.0-LL</p> </td> </tr> <tr> <td> <p>Grid</p> </td> <td> <p>Bilinear interpolation of the four nearest points</p> </td> </tr> <tr> <td> <p>Month transition smoothness</p> </td> <td> <p>72 hours</p> </td> </tr> <tr> <td> <p>Apply variable limits</p> </td> <td> <p>True</p> </td> </tr> <tr> <td> <p>Scenarios</p> </td> <td> <p>A total of nine scenarios: One baseline for 2021 and eight future files (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 for 2050 and 2080)</p> </td> </tr> <tr> <td> <p>Solar hour correction</p> </td> <td> <p>Made by day</p> </td> </tr> <tr> <td> <p>Diffuse irradiation model</p> </td> <td> <p>Engerer, 2015</p> </td> </tr> </tbody> </table> <p> </p> <h1>3. References</h1> <p>[1] E. Rodrigues, M.S. Fernandes, D. Carvalho, Future weather generator for building performance research: An open-source morphing tool and an application, Building and Environment 233 (2023) 110104. https://doi.org/10.1016/j.buildenv.2023.110104.</p> <p>[2] F. Tartarini, S. Schiavon, pythermalcomfort: A Python package for thermal comfort research, SoftwareX 12 (2020) 100578. https://doi.org/10.1016/j.softx.2020.100578.</p> <p>[3] Vaz, I.C.M.; Ghisi, E.; Thives, L.P.; Vieira, A.S.; Rupp, R.F.; da Rosa, A.S.; Flores, R.A.; Bastos, M.B.; Marinoski, D.L.; Silva, A.S.; Weeber, M.; Invidiata, A. (2024). Dashboard for interpreting future climate files used in the simulation of buildings – an outdoor thermal comfort approach. Under submission.</p> <p>[4] Future EPW Analysis - A pipeline of processes aimed at providing future EPW files based on existing models from the literature. Available at: https://github.com/igorcmvaz/future-EPW-analysis.</p> <p> </p> <h1>Current version of the dashboard: 1.0.0.</h1> <h1>Available at <a title="Dashboard comfort - 1.0.0." href="https://app.powerbi.com/view?r=eyJrIjoiNWI0ZTk5YjMtZjA5Ny00ZjE3LTk2ZDUtNDA1OThhNWQ3NWYxIiwidCI6ImZhNzk1MzFjLThjZTUtNGJkMy05N2VlLTI0NWU2ZWUyNjZiOCJ9" target="_blank" rel="noopener">Dashboard Comfort.</a></h1> <p>Suggestions for improvements can be made directly in the GitHub repository at <a title="Future-EPW-analysis" href="https://github.com/igorcmvaz/future-EPW-analysis" target="_blank" rel="noopener">future-EPW-analysis</a> or sent to igorcmvaz@gmail.com.</p> <p> </p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p> </p> <p> </p> <h3>(Português-BR)</h3> <h1>1. Introdução</h1> <p>Este projeto tem como objetivo abordar os impactos das mudanças climáticas no ambiente construído, desenvolvendo um conjunto de futuros arquivos EPW (Energy Plus Weather Format) brasileiros e um <em>dashboard</em> para interpretar e avaliar os dados. Os arquivos climáticos futuros foram obtidos com o Future Weather Generator (FWG) [1] com projeções climáticas para cidades brasileiras, integrando essas projeções a um pipeline de código para automação. Nessa parte do projeto, os índices de conforto térmico, como o Universal Thermal Climate Index (UTCI) e o Discomfort Index (DI), também foram avaliados para entender as condições futuras de conforto térmico. A metodologia seguiu a estrutura que está disponível no repositório <a title="Future-EPW-analysis" href="https://github.com/igorcmvaz/future-EPW-analysis" target="_blank" rel="noopener">future-EPW-analysis</a>:</p> <ol> <li>Web-scrapping do Climate-One-Building (COB) para todos os arquivos EPW brasileiros disponíveis (recomendamos fazer isso com cuidado para não prejudicar a infraestrutura do COB);</li> <li>Organização automática de todos os arquivos EPW em uma pasta, extraindo-os do formato ZIP;</li> <li>Simulação dos arquivos climáticos futuros por meio do FutureWeatherGenerator [1] em linha de código com parâmetros padrão (mostrados na Tabela 1);</li> <li>Organização de todos os EPW disponíveis (originais e simulados) em um único banco de dados;</li> <li>Cálculo dos índices de conforto térmico com o pythermalcomfort [2].</li> </ol> <p>O objetivo principal é fornecer a pesquisadores, formuladores de políticas e profissionais uma ferramenta abrangente para avaliar e mitigar os impactos das mudanças climáticas em diferentes cidades brasileiras, oferecendo dados precisos para modelagem de conforto térmico e eficiência energética. A metodologia envolve a geração de futuros arquivos EPW, validando-os com a literatura existente e visualizando os resultados por meio de um <em>dashboard</em> de fácil utilização. O estudo destaca a importância de estratégias adaptativas e resistentes ao clima no planejamento urbano e no projeto de edificações. As mudanças climáticas esperadas no Brasil incluem o aumento da temperatura de bulbo seco e variações na umidade relativa, radiação e velocidade do vento nas diferentes zonas bioclimáticas.</p> <p>O <em>dashboard</em> foi projetado para simplificar a visualização dos dados climáticos futuros, concentrando-se nas principais variáveis climáticas, índices de conforto térmico e visualização dos dados. Ele permite que os usuários filtrem por cidade e calculem automaticamente todos os índices, fornecendo análises detalhadas e comparações de diferentes cenários. Ao oferecer uma ferramenta gratuita, de acesso aberto, multiplataforma, extensível, personalizável e de fácil manutenção, o projeto visa a facilitar atualizações contínuas, novos recursos e correções. Essa ferramenta apoia a tomada de decisões em políticas públicas e planejamento urbano, promovendo um ambiente construído mais sustentável e resiliente em face das mudanças climáticas.</p> <p> </p> <h1>2. Mais detalhes sobre a metodologia</h1> <p>Detalhes sobre a seleção dos índices de conforto e como o estudo foi conduzido podem ser encontrados em Vaz et al. [3]. O repositório GitHub em <a title="Future-EPW-analysis" href="https://github.com/igorcmvaz/future-EPW-analysis" target="_blank" rel="noopener">future-EPW-analysis</a> [4] também inclui detalhes sobre os procedimentos passo a passo.</p> <h3>Tabela 1 - Parâmetros usados na simulação do FWG</h3> <table> <tbody> <tr> <td> <p><strong>Parâmetro</strong></p> </td> <td> <p><strong>Dados utilizados na simulação</strong></p> </td> </tr> <tr> <td> <p>Arquivos base</p> </td> <td> <p>578 cidades do COB</p> </td> </tr> <tr> <td> <p>Modelos CMIP-6</p> </td> <td> <p>BCC-CSM2-MR, CAS-ESM2.0, CMCC-ESM2, CNRM-CM6.1-HR, CNRM-ESM2.1, EC-Earth3, EC-Earth3-Veg, MIROC-ES2H, MIROC6, MRI-ESM2.0, UKESM1.0-LL</p> </td> </tr> <tr> <td> <p>Malha</p> </td> <td> <p>Interpolação bilinear dos quatro pontos mais próximos</p> </td> </tr> <tr> <td> <p>Suavização da transição mensal</p> </td> <td> <p>72 horas</p> </td> </tr> <tr> <td> <p>Aplicar limites das variáveis</p> </td> <td> <p>Sim</p> </td> </tr> <tr> <td> <p>Cenários</p> </td> <td> <p>Total de nove cenários: Um arquivo base em 2021 e oito arquivos futuros (SSP1-2.6, SSP2-4.5, SSP3-7.0 e SSP5-8.5 para 2050 e 2080)</p> </td> </tr> <tr> <td> <p>Correção de hora solar</p> </td> <td> <p>Feita por dia</p> </td> </tr> <tr> <td> <p>Modelo de radiação difusa</p> </td> <td> <p>Engerer (2015)</p> </td> </tr> </tbody> </table> <p> </p> <h1>3. Referências</h1> <p>[1] E. Rodrigues, M.S. Fernandes, D. Carvalho, Future weather generator for building performance research: An open-source morphing tool and an application, Building and Environment 233 (2023) 110104. https://doi.org/10.1016/j.buildenv.2023.110104.</p> <p>[2] F. Tartarini, S. Schiavon, pythermalcomfort: A Python package for thermal comfort research, SoftwareX 12 (2020) 100578. https://doi.org/10.1016/j.softx.2020.100578.</p> <p>[3] Vaz, I.C.M.; Ghisi, E.; Thives, L.P.; Vieira, A.S.; Rupp, R.F.; da Rosa, A.S.; Flores, R.A.; Bastos, M.B.; Marinoski, D.L.; Silva, A.S.; Weeber, M.; Invidiata, A. (2024). Dashboard for interpreting future climate files used in the simulation of buildings – an outdoor thermal comfort approach. Under submission.</p> <p>[4] Future EPW Analysis - A pipeline of processes aimed at providing future EPW files based on existing models from the literature. Available at: https://github.com/igorcmvaz/future-EPW-analysis.</p> <p> </p> <h1>Versão atual do <em>dashboard</em>: 1.0.0. </h1> <h1>Disponível em <a title="Dashboard comfort - 1.0.0." href="https://app.powerbi.com/view?r=eyJrIjoiNWI0ZTk5YjMtZjA5Ny00ZjE3LTk2ZDUtNDA1OThhNWQ3NWYxIiwidCI6ImZhNzk1MzFjLThjZTUtNGJkMy05N2VlLTI0NWU2ZWUyNjZiOCJ9" target="_blank" rel="noopener">Dashboard conforto.</a></h1> <p>As sugestões de melhorias podem ser feitas diretamente no repositório do GitHub em <a title="Future-EPW-analysis" href="https://github.com/igorcmvaz/future-EPW-analysis" target="_blank" rel="noopener">future-EPW-analysis</a> ou enviadas para igorcmvaz@gmail.com.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p>
Individual work of Bachelor students monitored using a dynamic assessment dashboard
<p>This document explain how data were generated and how to interpret them. </p> <p><strong>LICENSE: CC0</strong><br> But if you want to combine data with other datasets, feel free to use them as if they were published under CC0 license. <br> Data were published in February 2017. At that time, Zenodo only provided CC BY, CC BY-SA, CC BY-NC, CC BY-ND and CC BY-NC-ND. No CC0 option was available.</p> <p> </p> <p><strong>HOW DATA WERE COLLECTED</strong><br> Data provided in this dataset were collected in Fall 2016 as part of the Bachelor course 'Formation des usagers en bibliothèques' taught at the University of applied sciences Geneva (Information Sciences Department). <br> Data were generated by the interaction of the students with DAD, the Dynamic assessment dashboard, software to be released in 2017 (https://github.com/grolimur/DAD). Only data related to individual activities are published in this set. They were then cleaned and anonymised.</p> <p>The activities.csv file is provided for information purpose.</p> <p>Data are provided in 2 formats:</p> <p>* Comma-spearated values (submissions.csv)<br> * SQLite (submissions.sqlite)</p> <p>CSV is not correctly read by Excel, it's recommended to convert it into an .xslx file before using it. <br> SQLite is provided in order to apply different sorting and filters to the data. It can be read using SQLite manager for Firefox (https://addons.mozilla.org/en-US/firefox/addon/sqlite-manager/).</p> <p> </p> <p><strong>CODEBOOK</strong><br> Here is the name, the meaning and the possible values of the columns (name - description [possible values]).</p> <p>ID - database row ID [(database unique row ID)]<br> ActivityID - unique activity ID [1, 2, 3, 4, 10, 14, 16, 21, 22, 23, 41]<br> StudentID - unique anonymous student ID [#50 to #72]<br> date - recorded by the database [timestamp]<br> year - year extracted from date [2016, 2017]<br> month - month extracted from date [01, 11, 12]<br> day - day extracted from date [1 to 31]<br> weekday - weekday calculated from date [1, 2, 3, 4, 5, 6, 7]<br> hour - hour extracted from date - 00 to 23]<br> minute - minute extracted from date [00 to 59]<br> second - second extracted from date [00 to 59]<br> gr - group activity or not [0]<br> validated - was the submission validated or not [-1 or 1]<br> week - week number [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, H1, H2]</p> <p><em>ActivityID meaning</em><br> Activities are described in activities.csv. This file conatins only individual activities description. <br> The 2 activities iwth the bonus particularity are not present in the data because students didn't need to apply for them. A bonus was automatically added when activity 1 AND 2 AND 3 AND 4 (bonus = activity 5) as well as when activities 21 AND 22 AND 23 (activity 24) were completed.</p> <p><em>Date format</em><br> The date is formatted like YYYY-MM-DD hh:mm:ss.</p> <p><em>Weekday meaning</em><br> 1 stands for Monday, 2 for Tuesday and so on until 7 for Sunday.</p> <p><em>Group meaning</em><br> 0 stands for 'individual activity' and 1 would stand for group activity'. <br> Only entries with gr="0" appears in this dataset.</p> <p><em>Validated meaning</em><br> -1 stands for 'rejected' and 1 for 'validated'.</p> <p><em>Week meaning</em><br> The course lasts 10 weeks. 1 stands for the 1st week and so on until 10 that stands for the 10th week. <br> H1 and H2 stand for the Christmas holiday weeks that took place between week 8 and 9. <br> No submission were sent in weeks 1, 2 and 10.</p> <p> </p>
Supplementary material 12: Collector dashboard: specimens collected by Y. M. Marusik from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Dashboard charts showing only specimens collected by Y. M. Marusik. This page shows data from species-rank treatments. When viewed using a browser (such as Google Chrome) with an internet connection, this page sends a series of queries to Plazi and integrates the results with the Google Charts API to produce 37 interactive dashboard charts.
Supplementary material 9: Integrated legacy literature and prospective publishing dashboard: species-rank treatments from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Dashboard charts summarizing content from 42 articles published either as open access articles published in Zootaxa or in Biodiversity Data Journal, containing treatments on spiders. This page shows data from species-rank treatments. When viewed using a browser (such as Google Chrome) with an internet connection, this page sends a series of queries to Plazi and integrates the results with the Google Charts API to produce 37 interactive dashboard charts.
Supplementary material 8: Integrated legacy literature and prospective publishing dashboard: all treatments from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Dashboard charts summarizing content from 42 articles published either as open access articles published in Zootaxa or in Biodiversity Data Journal, containing treatments on spiders. This page shows data from all treatments. When viewed using a browser (such as Google Chrome) with an internet connection, this page sends a series of queries to Plazi and integrates the results with the Google Charts API to produce 37 interactive dashboard charts.
Supplementary material 5: Legacy literature dashboard: species-rank treatments from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Dashboard charts summarizing content from 37 open access articles published in Zootaxa containing treatments on spiders. This page shows data from species-rank treatments. When viewed using a browser (such as Google Chrome) with an internet connection, this page sends a series of queries to Plazi and integrates the results with the Google Charts API to produce 37 dashboard charts.
Supplementary material 16: Author dashboard: Jeremy A. Miller from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Dashboard charts showing content from articles by Jeremy A. Miller (lead author). When viewed using a browser (such as Google Chrome) with an internet connection, this page sends a series of queries to Plazi and integrates the results with the Google Charts API to produce 37 interactive dashboard charts.
Supplementary material 4: Legacy literature dashboard: all treatments from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Dashboard charts summarizing content from 37 open access articles published in Zootaxa containing treatments on spiders. This page shows data from all treatments. When viewed using a browser (such as Google Chrome) with an internet connection, this page sends a series of queries to Plazi and integrates the results with the Google Charts API to produce 37 dashboard charts.
Supplementary material 11: Collecting country dashboard: specimens collected in Russia from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Dashboard charts showing only specimens collected in Russia. This page shows data from species-rank treatments. When viewed using a browser (such as Google Chrome) with an internet connection, this page sends a series of queries to Plazi and integrates the results with the Google Charts API to produce 37 interactive dashboard charts.
Supplementary material 10: Institutional collection dashboard: specimens from the collection of the California Academy of Sciences (CAS) from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Dashboard charts showing only specimens from the collection of the California Academy of Sciences. This page shows data from species-rank treatments. When viewed using a browser (such as Google Chrome) with an internet connection, this page sends a series of queries to Plazi and integrates the results with the Google Charts API to produce 37 interactive dashboard charts.
Supplementary material 15: Species dashboard: Tenuiphantes tenuis from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Dashboard charts showing only specimens of the linyphiid spider Tenuiphantes tenuis. When viewed using a browser (such as Google Chrome) with an internet connection, this page sends a series of queries to Plazi and integrates the results with the Google Charts API to produce 37 interactive dashboard charts.
Supplementary material 7: Prospective publishing dashboard: species-rank treatments from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Dashboard charts summarizing content from 5 articles published in Biodiversity Data Journal containing treatments on spiders. This page shows data from species-rank treatments. When viewed using a browser (such as Google Chrome) with an internet connection, this page sends a series of queries to Plazi and integrates the results with the Google Charts API to produce 37 dashboard charts.
Supplementary material 14: Treatment dashboard: content from Pardosa zyuzini treatment in Kronestedt and Marusik (2011) from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Dashboard charts showing content from one treatment: Pardosa zyuzini in Kronestedt and Marusik (2011). When viewed using a browser (such as Google Chrome) with an internet connection, this page sends a series of queries to Plazi and integrates the results with the Google Charts API to produce 37 interactive dashboard charts.
Supplementary material 13: Article dashboard: content from Kronestedt and Marusik (2011) from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Dashboard charts showing content from one article, Kronestedt and Marusik 2011. This page shows data from all treatments. When viewed using a browser (such as Google Chrome) with an internet connection, this page sends a series of queries to Plazi and integrates the results with the Google Charts API to produce 37 interactive dashboard charts.
Supplementary material 6: Prospective publishing dashboard: all treatments from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Dashboard charts summarizing content from 5 articles published in Biodiversity Data Journal containing treatments on spiders. This page shows data from all treatments. When viewed using a browser (such as Google Chrome) with an internet connection, this page sends a series of queries to Plazi and integrates the results with the Google Charts API to produce 37 dashboard charts.
Dataset shown on the HMC FAIR Data Dashboard as of June 2023
<p>This dataset contains literature metadata harvested for 15 research centers of the Helmholtz Association of German research centers (KIT, FZJ, DESY, AWI, DLR, GFZ, HZB, GEOMAR, MDC, UFZ, HZDR, DZNE, GSI, CISPA, HMGU) during the first two quarters of 2023 as well as metadata of linked data publications, as extracted from the ScholExplorer API. F-UJI scores for these data publications are based on F-UJI version 1.4.7 using the FAIRsFAIR metrics 0.4. The status of the data represents the data shown on the HMC Dashboard on Open and FAIR Data in Helmholtz (<a title="https://fairdashboard.helmholtz-metadaten.de/" href="https://fairdashboard.helmholtz-metadaten.de/">https://fairdashboard.helmholtz-metadaten.de/</a>) as of June 2023.</p>
User Guide – Dashboard on Salmonella
<p>The EFSA dashboard on <em>Salmonella</em> is a graphical user interface for searching and querying the large amount of data collected each year by EFSA from EU Member States and other reporting countries based on Zoonoses Directive 2003/99/EC. The <em>Salmonella</em> dashboard shows summary statistics for the monitoring results of the pathogen with regard to major food categories and animal species, <em>Salmonella</em>-positive official samples in the context of food safety criteria and process hygiene criteria, the occurrence of <em>Salmonella</em> in major food categories and the achievement of <em>Salmonella</em> reduction targets in poultry populations. The <em>Salmonella</em> data and related statistics can be displayed interactively using charts, graphs and maps in the online EFSA dashboard. The main statistics can also be viewed and downloaded in tabular format. Detailed information on the use and features of the <em>Salmonella</em> dashboard can be found in the present user guide that can also be downloaded from the online tool.</p>
User Guide – Dashboard on Zoonotic tuberculosis: Mycobacterium
<p>User Guide – Dashboard on Zoonotic tuberculosis focusing on Mycobacterium bovis and M. caprae</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.