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19 results for “Open access database”
The Brazilian Soil Spectral Library (VIS-NIR-SWIR-MIR) Database: Open Access
<p><strong>Abstract:</strong></p> <p>NEW VERSION V.002 (Some Lat Long Coordinates added).</p> <p>Soil spectroscopy has emerged as a solution to the limitations associated with traditional soil surveying and analysis methods, addressing the challenges of time and financial resources. Analyzing the soil's spectral reflectance enables to observe the soil composition and simultaneously evaluate several attributes because the matter, when exposed to electromagnetic energy, leaves a "spectral signature" that makes such evaluations possible. The Soil Spectral Library (SSL) consolidates soil spectral patterns from a specific location, facilitating accurate modeling and reducing time, cost, chemical products, and waste in surveying and mapping processes. Therefore, an open access SSL benefits society by providing a fine collection of free data for multiple applications for both research and commercial use.</p> <p><strong>BSSL Description and Usefulness</strong></p> <p>The Brazilian Soil Spectral Library (BSSL), available at <a href="https://bibliotecaespectral.wixsite.com/english">https://bibliotecaespectral.wixsite.com/english</a>, is a comprehensive repository of soil spectral data. Coordinated by JAM Demattê and managed by the GeoCiS research group, the BSSL was initiated in 1995 and published by Demattê and collaborators in 2019. This initiative stands out due to its coverage of diverse soil types, given Brazil's significance in the agricultural and environmental domains and its status as the fifth largest territory in the world (IBGE, 2023). In addition, a Middle Infrared (MIR) dataset has been published (Mendes et al., 2022), part of which is included in this repository. The database covers 16,084 sites and includes harmonized physicochemical and spectral (Vis-NIR-SWIR and MIR range) soil data from various sources at 0-20 cm depth. All soil samples have Vis-NIR-SWIR data, but not all have MIR data.</p> <p>The BSSL provides open and free access to curated data for the scientific community and interested individuals. Unrestricted access to the BSSL supports researchers in validating their results by comparing measured data with predicted values. This initiative also facilitates the development of new models and the improvement of existing ones. Moreover, users can employ the library to test new models and extract information about previously unknown soil properties. With its extensive coverage of tropical soil classes, the BSSL is considered one of the most significant soil spectral libraries worldwide, with 42 institutions and 61 researchers participating. However, 47 collaborators from 29 institutions have authorized the data opening. Other researchers can also provide their data upon request through the coordinator of this initiative.</p> <p>The data from the BSSL project can also help wet labs to improve their analytical capabilities, contributing to developing hybrid wet soil laboratory techniques and digital soil maps while informing decision-makers in formulating conservation and land use policies. The soil's capacity for different land uses promotes soil health and sustainability.</p> <p><strong>Coverage</strong></p> <p>The BSSL data covers all regions of Brazil, including 26 states and the Federal District. It is in a <em>.xlsx</em> format and has a total size of 305 Mb. The table is structured in sheets with rows for observations, and columns, representing various soil attributes in the surface layer, from 0 to 20 cm depth. The database includes environmental and physicochemical properties (22 columns and 16,084 rows), Vis-NIR-SWIR spectral bands (2151 columns and 16,084 rows), and MIR channels (681 columns and 1783 rows). An ID unique column can merge the sheet for each attribute or spectral range.</p> <p><strong>Accessing original data source</strong></p> <p>Using these data requires their reference in any situation under copyright infringement penalty. Three mechanisms are available for users to reach the original and complete data contributors:</p> <p>a) Refer to sheet two for name and code-based searches;</p> <p>b) Visit the website <a href="https://bibliotecaespectral.wixsite.com/english/lista-de-cedentes">https://bibliotecaespectral.wixsite.com/english/lista-de-cedentes</a> or locate the contributors' list by Brazilian state;</p> <p>c) Visit the website of the Brazilian Soil Spectral Service – Braspecs <a href="http://www.besbbr.com.br/">http://www.besbbr.com.br/</a>, an online platform for soil analysis that uses part of the current SSL (Demattê et al., 2022) - It was developed and managed by GeoCiS. There, owners from all over the country can be found.</p> <p><strong>Proceeding to data analysis</strong></p> <p>We registered and organized the samples at the ESALQ/USP Soil Laboratory. Some samples arrived without preliminary data analyses, so we analyzed them for soil organic matter (SOM), granulometry, cation exchange capacity (CEC), pH in water, and the presence of Ca, Mg, and Na, following the recommendations of Donagemma et al. (2011).</p> <p>The GeoCiS research group performed spectral analyses following the procedures described by Bellinaso et al. (2010). Demattê et al. (2019) provide detailed methods for sampling, preparation, and soil analyses, including reflectance spectroscopy. Latitude and longitude data can be requested directly from the data owner. In summary, the following steps are involved in data acquisition.</p> <p>a) We subjected the soil samples to a preliminary treatment, which involved drying them in an oven at 45°C for 48 hours, grinding them, and sieving them through a 2mm mesh;</p> <p>b) We placed the samples in Petri dishes with a diameter of 9 cm and a height of 1.5 cm;</p> <p>c) We homogenized and flattened the surface of the samples to reduce the shading caused by larger particles or foreign bodies, making them ready for spectral readings;</p> <p>d) The spectral analyses took place in a darkened room to avoid interference from natural light. We used a computer to record the electromagnetic pulses through an optical fiber connected to the sensor, capturing the spectral response of the soil sample;</p> <p>e) We obtained reflectance data in the Visible-Near Infrared-Shortwave Infrared (Vis-NIR-SWIR) range using a FieldSpec 3 spectroradiometer (Analytical Spectral Devices, ASD, Boulder, CO), which operates in the spectral range from 350 to 2500 nm;</p> <p>f) The sensor had a spectral resolution of 3 nm from 350-700 nm and 10 nm from 700-2500 nm, automatically interpolated to 1 nm spectral resolution in the output data, resulting in 2151 channels (or bands); and</p> <p>g) We positioned the lamps at 90° from each other and 35 cm away from the sample, with a zenith angle of 30°.</p> <p>The sensor captured the light reflected through the fiber optic cable, which was positioned 8 cm from the sample's surface.</p> <p>We used two 50W halogen lamps as the power source for the artificial light. It's important to note that we took three readings for each sample at different positions by rotating the Petri dish by 90°.</p> <p>Each reading represents the average of 100 scans taken by the sensor. From these three readings, we calculated the final spectrum of the samples. Notably, the laboratory's equipment and procedures for soil sample spectral analyses followed the ASD's recommendations, particularly about sensor calibration using a white spectralon plate as a 100% reflectance standard.</p> <p>For the analysis in the Middle Infrared (MIR) spectral region, we followed the procedures outlined by Mendes et al. (2022). We milled the soil fraction smaller than 2 mm, sieved it to 0.149 mm, and scanned it using a Fourier Transform Infrared (FT-IR) alpha spectroradiometer (Bruker Optics Corporation, Billerica, MA 01821, USA) equipped with a DRIFT accessory.</p> <p>The spectroradiometer measured the diffuse reflectance using Fourier transformation in the spectral range from 4000 cm<sup>-1</sup> to 600 cm<sup>-1</sup>, with a resolution of 2 cm<sup>-1</sup>. We conducted these measurements in the Geotechnology Laboratory of the Department of Soil Science at Esalq-USP. We took the average of 32 successive readings to obtain a soil spectrum. Sensor calibration took place before each spectral acquisition of the sample set by standardizing it against the maximum reflectance of a gold plate.</p> <p> </p> <p><strong>Dataset characterization</strong></p> <p>The database, named BSSL_DB_Key_Soils, has five sheets containing the key soil attributes, Vis-NIR-SWIR and MIR datasets, descriptions of the contributors and the proximal sensing methods used for spectral soil analysis. The sheets can be linked by "ID_Unique" columns, which bring the corresponding rows according to the data type. Some cells are empty because collaborators have already provided data in this way. However, we have decided to keep them in the database because they have other soil key attributes. Every Column in the data sheets is described as follows:</p> <p> </p> <p><strong>Sheet 1. BSSL_Soil_Attributes_Dataset</strong></p> <p>Column 1. <strong>ID_unique</strong>: Sequential code assigned to every record;</p> <p>Column 2. <strong>Owner code</strong>: Acronym assigned to each contributor who allowed access to their proprietary data;</p> <p>Column 3. <strong>Vis_NIR_SWIR_availability</strong>: availability of spectral data in visible, near-infrared, and shortwave infrared ranges;</p> <p>Column 4. <strong>MIR_availability</strong>: availability of spectral data in the middle infrared range;</p> <p>Column 5. <strong>Sampling</strong>: type of soil sampling;</p> <p>Column 6. <strong>Depth_cm</strong>: soil surface layer depth in centimeters; </p> <p>Column 7. <strong>Lat</strong>: Latitude; </p> <p>Column 8. <strong>Lat</strong>: Longitude; </p> <p>Column 9. <strong>Region</strong>: Brazilian geographical region of samples' source;</p> <p>Column 10. <strong>Municipality</strong>: Brazilian municipality of samples' source;</p> <p>Column 11. <strong>State</strong>: Brazilian Federation Unit of samples' source;</p> <p>Column 12. <strong>Vegetation</strong>: type of vegetal covering;</p> <p>Column 13. <strong>Biome</strong>: groupings of ecosystems that share similar characteristics and span different regions;</p> <p>Column 14. <strong>Geology</strong>: type of rock matter from local soil sampling;</p> <p>Column 15. <strong>Sand_gkg</strong>: Content of the soil fraction with grain size between 2 and 0.053 mm, expressed in grams per kilogram;</p> <p>Column 16. <strong>Clay_gkg</strong>: Content of soil fraction with grain size smaller than 0.002 mm, expressed in grams per kilogram;</p> <p>Column 17. <strong>SOM_gkg</strong>: Soil organic matter content, expressed in grams per kilogram;</p> <p>Column 18. <strong>pH_H2O</strong>: Soil hydrogen ion potential measured in water;</p> <p>Column 19. <strong>Ca_mmolkg</strong>: Exchangeable calcium content in the soil, expressed in millimoles per kilogram;</p> <p>Column 20. <strong>Mg_mmolkg</strong>: Exchangeable magnesium content in the soil, expressed in millimoles per kilogram;</p> <p>Column 21. <strong>Na_mmolkg</strong>: Exchangeable sodium content in the soil, expressed in millimoles per kilogram; and</p> <p>Column 22. <strong>CEC_Ph7_mmolkg</strong>: Cation exchange capacity of the soil at neutral pH, expressed in millimoles per kilogram.</p> <p> </p> <p><strong>Sheet 2. BSSL_Vis_NIR_SWIR_Dataset</strong></p> <p>Column 1. <strong>ID_Unique</strong>: Sequential code assigned to every record;</p> <p>Column 2. <strong>Owner code</strong>: Acronym assigned to each contributor who allowed access to their proprietary data; and</p> <p>Column 3 – 2153. <strong>350 – 2500</strong>: Reflectance in 2151 spectral bands in nanometers from visible and near-infrared to shortwave infrared range (350 – 2500 nm).</p> <p> </p> <p><strong>Sheet 3. BSSL_MIR_Dataset</strong></p> <p>Column 1. <strong>ID_Unique:</strong> Sequential code assigned to every record;</p> <p>Column 2. <strong>Owner_code:</strong> Acronym assigned to each contributor who allowed access to their proprietary data; and</p> <p>Column 3 – 683. <strong>4000 – 600:</strong> Reflectance in 681 spectral bands in centimeters in the middle infrared range (4000 – 600 cm<sup>-1</sup>).</p> <p> </p> <p><strong>Sheet 4. Contributors</strong></p> <p>Column 1. <strong>Owner_code</strong>: Acronym assigned to each contributor who allowed access to their proprietary data, which identifies and links it to datasets;</p> <p>Column 2. <strong>Owner</strong>: Name of the collaborator who agreed to the availability of the data;</p> <p>Column 3. <strong>E-mail</strong>: Contact the e-mail of the owner for more information or a data request;</p> <p>Column 4. <strong>Institution</strong>: Contributor's affiliation;</p> <p>Column 5. <strong>Samples NIR</strong>: Number of Vis-NIR-SWIR samples sent to the BSSL collection;</p> <p>Column 6. <strong>Samples MIR</strong>: Number of MIR samples sent to the BSSL collection;</p> <p> </p> <p><strong>Sheet 5. Metadata</strong></p> <p>Column 1. <strong>Material and Methods</strong>: Description of procedures performed for soil data analyses</p> <p> </p> <p><strong>Expectation and Social Relevance</strong></p> <p>These data can impact various disciplines such as soil surveying, soil attribute mapping, soil analysis, soil mineralogy, soil management zones, precision agriculture, development of new datasets and scientific groups, and others. We expect this contribution to be valuable and useful to the soil research community in promoting this non-renewable natural resource's conservation and sustainable use.</p>
pofatu/pofatu-data: Pofatu, a curated and open-access database for geochemical sourcing of archaeological materials
<p>Geochemical fingerprinting of artefacts and sources has proven to be the most effective way to use material evidence in order to reconstruct strategies of raw material procurement, exchange systems, and mobility patterns among past societies. In order to facilitate access to this growing body of data and to promote comparability and reproducibility in provenance studies, we designed Pofatu, the first online and open-access database presenting geochemical compositions and contextual information for archaeological sources and artefacts.</p> <p>The data repository includes a compilation of geochemical data and supporting analytical metadata, as well as the archaeological provenance and context for each sample. All information on Samples related to sources and artefacts can be accessed on this platform or downloaded from Zenodo or GitHub.</p> <p>While most prehistoric quarries and surface procurement sources used in the past have yet to be identified, provenance studies must also integrate wide and reliable geological data. For this reason, we advise Pofatu users to also consult other open-access repositories focusing specifically on geological samples, such as GeoRoc and EarthChem.</p>
Sharkipedia: A Curated Open Access Database of Shark and Ray Life History Traits and Abundance Time-series
<p>This dataset represent the intial launch of Sharkipedia: a curated open access database of shark and ray life history traits and abundance time-series. A curated database of shark and ray biological data is increasingly necessary both to support fisheries management and conservation efforts, and to test the generality of hypotheses of vertebrate macroecology and macroevolution. Sharks and rays are one of the most charismatic, evolutionary distinct, and threatened lineages of vertebrates, comprising around 1,250 species. To accelerate shark and ray conservation and science, we developed Sharkipedia as a curated open-source database and research initiative to make all published biological traits and population trends accessible to everyone. Sharkipedia hosts information on 58 life history traits from 264 sources, for 170 species, from 39 families, and 12 orders related to length (n=9 traits), age (8), growth (12), reproduction (19), demography (5), and allometric relationships (5), as well as 871 population time-series from 202 species. Sharkipedia relies on the backbone taxonomy of the IUCN Red List and the bibliography of Shark-References. Sharkipedia has profound potential to support the rapidly growing data demands of fisheries management, international trade regulation as well as anchoring vertebrate macroecology and macroevolution.</p>
An Open-access Database for the Evaluation of Cardio-mechanical Signals from Patients with Valvular Heart Diseases
<p>This dataset is for the paper "An Open-access Database for the Evaluation of Cardio-mechanical Signals from Patients with Valvular Heart Diseases" published to Frontiers in Physiology. Please cite "Yang C, Fan F, Aranoff N, Green P, Li Y, Liu C and Tavassolian N (2021) An Open-Access Database for the Evaluation of Cardio-Mechanical Signals From Patients With Valvular Heart Diseases.<br> Front. Physiol. 12:750221. doi: 10.3389/fphys.2021.750221" when using this database.</p> <p>The archive comprises SCG and GCG recordings sourced from and processed at multiple sites worldwide, including Columbia University Medical Center and Stevens Institute of Technology in the USA, as well as Southeast University, Nanjing Medical University, and the first affiliated hospital of Nanjing Medical University in China. It includes electrocardiogram (ECG), SCG, and GCG recordings collected from 100 patients with various conditions of valvular heart diseases, such as aortic and mitral stenosis. The recordings were collected from clinical environments with the same types of wearable sensor patch. Besides the raw recordings of ECG, SCG and GCG signals, a set of hand-corrected fiducial point annotations is provided by manually checking the results of the annotated algorithm. The database also includes relevant echocardiogram parameters associated with each subject such as ejection fraction, valve area, and mean gradient pressure.</p>
How can we find an open access scientific journal indexed in the Scopus database?
<p><a href="https://www.youtube.com/watch?v=fIcjrwFKdP4&t=33s">To find open access journals indexed by Scopus</a>, you can follow these steps: Go to the Scopus website (www.scopus.com) and click on the "Journals" tab. On the "Journals" page, select "Advanced Search." In the "Advanced Search" page, select "Open Access" under the "Source Type" filter. You can further narrow down your search by using additional filters such as subject area, publication type, and more. The search results will show a list of open access journals indexed by Scopus. <a href="https://www.youtube.com/hashtag/scopus">#Scopus</a> <a href="https://www.youtube.com/hashtag/openaccessjournals">#OpenAccessJournals</a> <a href="https://www.youtube.com/hashtag/scientificjournals">#ScientificJournals</a> <a href="https://www.youtube.com/hashtag/indexedjournals">#IndexedJournals</a> <a href="https://www.youtube.com/hashtag/searchingjournals">#SearchingJournals</a> <a href="https://www.youtube.com/hashtag/findingjournals">#FindingJournals</a> <a href="https://www.youtube.com/hashtag/scopusdatabase">#ScopusDatabase</a> <a href="https://www.youtube.com/hashtag/elsevierscopus">#ElsevierScopus</a> <a href="https://www.youtube.com/hashtag/openaccesspublishing">#OpenAccessPublishing</a> <a href="https://www.youtube.com/hashtag/researchpublishing">#ResearchPublishing</a> <a href="https://www.youtube.com/hashtag/journalsearch">#JournalSearch</a> <a href="https://www.youtube.com/hashtag/scholarlypublishing">#ScholarlyPublishing</a> <a href="https://www.youtube.com/hashtag/scientificpublishing">#ScientificPublishing</a> <a href="https://www.youtube.com/hashtag/journalselection">#JournalSelection</a> <a href="https://www.youtube.com/hashtag/researchcommunication">#ResearchCommunication</a></p> <p> </p>
Experimental data for "An open-access database for the assessment of particle damper simulation tools"
<p>Experimental data for "An open access database for the assessment of particle damper simulation tools"</p>
An open-access database of infectious disease transmission trees to explore superspreader epidemiology
Historically, emerging and reemerging infectious diseases have caused large, deadly, and expensive multinational outbreaks. Often outbreak investigations aim to identify who infected whom by reconstructing the outbreak transmission tree, which visualizes transmission between individuals as a network with nodes representing individuals and branches representing transmission from person to person. We compiled a database, called OutbreakTrees, of 382 published, standardized transmission trees consisting of 16 directly transmitted diseases ranging in size from 2 to 286 cases. For each tree and disease, we calculated several key statistics, such as tree size, average number of secondary infections, the dispersion parameter, and the proportion of cases considered superspreaders, and examined how these statistics varied over the course of each outbreak and under different assumptions about the completeness of outbreak investigations. We demonstrated the potential utility of the database through 2 short analyses addressing questions about superspreader epidemiology for a variety of diseases, including Coronavirus Disease 2019 (COVID-19). First, we found that our transmission trees were consistent with theory predicting that intermediate dispersion parameters give rise to the highest proportion of cases causing superspreading events. Additionally, we investigated patterns in how superspreaders are infected. Across trees with more than 1 superspreader, we found preliminary support for the theory that superspreaders generate other superspreaders. In sum, our findings put the role of superspreading in COVID-19 transmission in perspective with that of other diseases and suggest an approach to further research regarding the generation of superspreaders. These data have been made openly available to encourage reuse and further scientific inquiry.
Open access database of raw ultrasonic signals acquired from malignant and benign breast lesions
<p>The dataset presented in: H. Piotrzkowska-Wróblewska, K. Dobruch-Sobczak, M. Byra, A. Nowicki, "Open access database of raw ultrasonic signals acquired from malignant and benign breast lesions", Medical Physics, http://dx.doi.org/10.1002/mp.12538. </p>
Open access GIS database using T2 outputs
<p>Open access GIS database using the ouputs from BRIDGE Task 2 (Innovation Laboratory - InnoLab). </p> <p>This GIS dataset is composed by several shape files and pdf files with the inputs and results from the participatory mapping process. </p> <p>For more information on the BRIDGE InnoLab Participatory Mapping process please look for the project deliverables and papers available in the BRIDGE Zenodo community (https://zenodo.org/communities/bridge_community/records?q=&l=list&p=1&s=10&sort=newest) or in the BRIDGE website (https://bridgecomunidade.pt/recursos/).</p>
A comprehensive open-access database of electron backscattering coefficients for energies ranging from 0.1 KeV to 15 MeV
<p>The database provides measured values of electron backscattering coefficient for 50 elements and 19 compounds at electron energies from 0.1keV to 15MeV.</p>
Introducing FAMM: an open-access database of Fossil Arctic Marine Mammals
Open the record for dataset details and reuse information.
An open-access database of infectious disease transmission trees to explore superspreader epidemiology
Open the record for dataset details and reuse information.
dbMMR-Chinese database: the open-access database for variants in mismatch repair genes in Chinese population
<p>Mutation in mismatch repair genes (MMR) is the genetic predisposition for gastrointestinal cancer represented by the Lynch Syndrome. Identification of the mutation carrier is critical in prevention and treatment of the cancer. Chinese is the largest ethnic population with the largestgastrointestinal cancer cases in the world, but systematic knowledge for the mutation in MMR is lack in Chinese population. Through comprehensive data mining, we collected nearly all MMR data derived from 33,998 Chinese of 23,938 cancer and 10,060 non-cancer cases reported from 1997 to 2019. Upon standardization and re-annotation, the data following international standards, we identified a total of 540 distinct MMR variants including 487 single base change and indel, and 53 large deletion/duplication in four MMR genes of <em>MLH1</em>, <em>MSH2</em>, <em>MSH6</em>and <em>PMS2</em>; 153 of the variants were classified as Pathogenic or Likely Pathogenic. This MMR dataset is the largest collection from a single, non-Caucasian population. We developed an open-access database, dbMMR-Chinese (<a href="https://dbmmr-chinese.fhs.um.edu.mo/">https://dbMMR-chinese.fhs.um.edu.mo</a>), to share with community for MMR mutation-related cancer study and clinical application.</p>
Dataset of biofouling epibionts on microalgae compiled from literature and environmental variables from open access databases
Open the record for dataset details and reuse information.
PatCID: an open-access database of chemical structures in patent documents
<p>PatCID is a chemical-structure database automatically created from images in patent documents. It contains 13M unique molecules, 80M molecule images, and 1.2M annotated documents from the United States (USPTO), Europe (EPO), Japan (JPO), Korea (KIPO), and China (CNIPA).</p> <p>Leveraging state-of-the-art document understanding models, PatCID enables accurate document and molecule retrieval in patents.</p> <p>Examples of how to use PatCID can be found on the <a href="https://github.com/DS4SD/PatCID">PatCID GitHub repository</a> </p>
The survey of the Marega Collection at the Vatican Library and the construction of a digital open access database (マリオ・マレガ収集資料の調査とデータベース)
<p>Paper presented on Friday 11 June 2021 at the Digital Medievalist Global Symposium <em>The past, present, and future of Digital Medieval Studies</em> for the Asia & Oceania Panel, in the session Reading Indic and Japanese scripts.</p> <p>In 2011, about 14,000 documents related to the ban of Christianity in Japan and the surveillance over the family of former Christians were found at the Vatican Library. The documents, roughly spanning from the 17th to the 19th century, were originally collected by Father Mario Marega, a Salesian missionary, who resided in the Oita Prefecture, Kyushu, Japan, since 1929. Marega then sent the documents to the Vatican Library in the 1950s, where, for various reasons, they were set aside and forgotten until their rediscovery during the Library’s renovation works in 2010. Researchers from Italy and Japan have conducted research for a decade to catalogue and publish the collection, recently also made available in a digital database. In this presentation, we will introduce the surveying method and the guiding principles of the database structure and its data model of the <a href="https://base1.nijl.ac.jp/~marega/en/">Mario Marega Archive</a>. We will also elaborate on the results achieved and the issues that the research process and the database definition presented.</p> <p>2011年にバチカン図書館で17ー19世紀の日本におけるキリスト教統制に関する文書(約14,000点)が発見された。1929年より日本の大分県に滞在したイタリアのマリオ・マレガ神父が収集し、1950年代にバチカン図書館へ送ったものである。これらを研究・公開するため、イタリア・日本の研究者が共同で調査およびデータベース構築を行っている。今回の発表では、調査の方法やデータベースの考え方などを紹介し、その成果と課題を展望する。</p> <p> </p> <p> </p> <p> </p>
Biodiversidata: An Open-Access Biodiversity Database for Uruguay
<p>We present a comprehensive database of tetrapod occurrence records native from Uruguay, with the latest taxonomic updates and geographic location accuracy. The dataset provides primary biodiversity data on extant Amphibia, Reptilia, Aves and Mammalia species recorded within the country area. The total number of records collated is 69,380, including 673 species. This is the largest and most geographically and taxonomically comprehensive database of Uruguayan tetrapod species available to date, and it represents the first open repository for the country.</p>
A verified open-access AI-based chemical microparticle image database for in-situ particle visualization and quantification in multi-phase flow
<p>This report provided a new method and idea for the detection, segmentation, classification, and quantitative analysis of four dispersed phase particles - "DPPs" (agglomeration, bubble, crystal, and droplet) in chemical multi-phase flow processes.</p>
Open Access Database of Standing Full Body Radiographs in Asymptomatic Volunteers
ClinicalTrials.gov study NCT03076658. IPD Sharing: YES. Countries: 1. Publications: 0.
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.