Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
5,738
datasets available to search
ShareScore release 0.9.0
Dataset results
5,738 results for “Standardization”
[MedMNIST+] 18x Standardized Datasets for 2D and 3D Biomedical Image Classification with Multiple Size Options: 28 (MNIST-Like), 64, 128, and 224
<h2><strong>Code</strong> [<a href="https://github.com/MedMNIST/MedMNIST" target="_blank" rel="noopener">GitHub</a>] | <strong>Publication</strong> [<a href="https://doi.org/10.1038/s41597-022-01721-8" target="_blank" rel="noopener">Nature Scientific Data'23</a> / <a href="https://doi.org/10.1109/ISBI48211.2021.9434062" target="_blank" rel="noopener">ISBI'21</a>] | <strong>Preprint</strong> [<a href="https://arxiv.org/abs/2110.14795" target="_blank" rel="noopener">arXiv</a>]</h2> <p> </p> <p><strong>Abstract</strong></p> <p>We introduce MedMNIST, a large-scale MNIST-like collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D. All images are pre-processed into 28x28 (2D) or 28x28x28 (3D) with the corresponding classification labels, so that no background knowledge is required for users. Covering primary data modalities in biomedical images, MedMNIST is designed to perform classification on lightweight 2D and 3D images with various data scales (from 100 to 100,000) and diverse tasks (binary/multi-class, ordinal regression and multi-label). The resulting dataset, consisting of approximately 708K 2D images and 10K 3D images in total, could support numerous research and educational purposes in biomedical image analysis, computer vision and machine learning. We benchmark several baseline methods on MedMNIST, including 2D / 3D neural networks and open-source / commercial AutoML tools. The data and code are publicly available at <a href="https://medmnist.com/">https://medmnist.com/</a>.</p> <p><em><strong>Disclaimer</strong></em>: The only official distribution link for the MedMNIST dataset is <a href="https://doi.org/10.5281/zenodo.10519652">Zenodo</a>. We kindly request users to refer to this original dataset link for accurate and up-to-date data.</p> <p><strong><em>Update</em>:</strong> We are thrilled to release <a href="https://github.com/MedMNIST/MedMNIST/blob/main/on_medmnist_plus.md">MedMNIST+</a> with larger sizes: 64x64, 128x128, and 224x224 for 2D, and 64x64x64 for 3D. As a complement to the previous 28-size MedMNIST, the large-size version could serve as a standardized benchmark for medical foundation models. Install the latest API to try it out!</p> <p> </p> <p><strong>Python Usage</strong></p> <p>We recommend our official <a href="https://github.com/MedMNIST/MedMNIST">code</a> to download, parse and use the MedMNIST dataset:</p> <blockquote> <pre>% pip install medmnist<br>% python</pre> <div> <div>To use the standard 28-size (MNIST-like) version utilizing the downloaded files:</div> <br> <div>>>> from medmnist import PathMNIST</div> <div>>>> train_dataset = PathMNIST(split="train")</div> <br> <div>To enable automatic downloading by setting `download=True`:</div> <br> <div>>>> from medmnist import NoduleMNIST3D</div> <div>>>> val_dataset = NoduleMNIST3D(split="val", download=True)</div> <br> <div>Alternatively, you can access MedMNIST+ with larger image sizes by specifying the `size` parameter:</div> <br> <div>>>> from medmnist import ChestMNIST</div> <div>>>> test_dataset = ChestMNIST(split="test", download=True, size=224)</div> </div> </blockquote> <p> </p> <p><strong>Citation</strong></p> <p>If you find this project useful, please cite both v1 and v2 paper as:</p> <blockquote> <p>Jiancheng Yang, Rui Shi, Donglai Wei, Zequan Liu, Lin Zhao, Bilian Ke, Hanspeter Pfister, Bingbing Ni. Yang, Jiancheng, et al. "MedMNIST v2-A large-scale lightweight benchmark for 2D and 3D biomedical image classification." Scientific Data, 2023.</p> <p>Jiancheng Yang, Rui Shi, Bingbing Ni. "MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis". IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021.</p> </blockquote> <p>or using bibtex:</p> <blockquote> <pre>@article{medmnistv2, title={MedMNIST v2-A large-scale lightweight benchmark for 2D and 3D biomedical image classification}, author={Yang, Jiancheng and Shi, Rui and Wei, Donglai and Liu, Zequan and Zhao, Lin and Ke, Bilian and Pfister, Hanspeter and Ni, Bingbing}, journal={Scientific Data}, volume={10}, number={1}, pages={41}, year={2023}, publisher={Nature Publishing Group UK London} } @inproceedings{medmnistv1, title={MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis}, author={Yang, Jiancheng and Shi, Rui and Ni, Bingbing}, booktitle={IEEE 18th International Symposium on Biomedical Imaging (ISBI)}, pages={191--195}, year={2021} }</pre> </blockquote> <p>Please also cite the corresponding paper(s) of source data if you use any subset of MedMNIST as per the description on the <a href="https://medmnist.github.io/">project website</a>.</p> <p> </p> <p><strong>License</strong></p> <p>The MedMNIST dataset is licensed under <em>Creative Commons Attribution 4.0 International</em> (<a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a>), except DermaMNIST under <em>Creative Commons Attribution-NonCommercial 4.0 International</em> (<a href="https://creativecommons.org/licenses/by-nc/4.0/">CC BY-NC 4.0</a>).</p> <p>The code is under <a href="https://github.com/MedMNIST/MedMNIST/blob/main/LICENSE">Apache-2.0 License</a>.</p> <p> </p> <p><strong>Changelog</strong></p> <p><a href="https://doi.org/10.5281/zenodo.10519652">v3.0</a> (this repository): Released MedMNIST+ featuring larger sizes: 64x64, 128x128, and 224x224 for 2D, and 64x64x64 for 3D.</p> <p><a href="https://doi.org/10.5281/zenodo.10519195">v2.2</a>: Removed a small number of mistakenly included blank samples in OrganAMNIST, OrganCMNIST, OrganSMNIST, OrganMNIST3D, and VesselMNIST3D. </p> <p><a href="https://doi.org/10.5281/zenodo.6496656">v2.1</a>: Addressed an issue in the NoduleMNIST3D file (i.e., nodulemnist3d.npz). Further details can be found in this <a href="https://github.com/MedMNIST/MedMNIST/issues/22#issuecomment-1103438191">issue</a>.</p> <p><a href="https://doi.org/10.5281/zenodo.5208230">v2.0</a>: Launched the initial repository of MedMNIST v2, adding 6 datasets for 3D and 2 for 2D.</p> <p><a href="https://doi.org/10.5281/zenodo.4269852">v1.0</a>: Established the initial repository (in a separate repository) of MedMNIST v1, featuring 10 datasets for 2D.</p> <p> </p> <p><strong>Note</strong>: This dataset is <strong>NOT</strong> intended for clinical use.</p>
Standard Solar Models B23 / SF-III
<p>This package contains data from a variety of up-to-date Solar Models as well as a routine to generate variations of them in a Linear Solar Model (LSM) approximation. The provided data includes the solar inner structure for several physical variables and abundances for chemical species of interest, as well as neutrino outflows and distributions from different sources and their correlation matrices.</p> <p>The available Solar Models differ in the proposed solar composition, and were all computed with a modified version of <em>GarStEC</em>, using the latest prescription for nuclear reaction rates and opacity data from the upcoming <em><strong>Solar Fusion III</strong></em> review article by Acharya et al. (submitted: <a href="https://arxiv.org/abs/2405.06470">arXiv:2405.06470</a>), and references therein. The proposed solar compositions are (in chronological order):</p> <ul> <li>"GS98" :: Grevesse & Sauval (1998), Space Sci. Rev., 85, 161.</li> <li>"AGSS09" :: Asplund et al. (2009), ARA&A, 47, 481.</li> <li>"C11" :: Caffau et al. (2011), Sol. Phys., 268, 255.</li> <li>"AAG21" :: Asplund et al. (2021), A&A 653, A141.</li> <li>"MB22m" :: Magg et al. (2022), A&A 661, A140. (Meteoritic)</li> <li>"MB22p" :: Magg et al. (2022), A&A 661, A140. (Photospheric)</li> </ul> <p> See the ReadMe file included in the package for a detailed list of content.</p> <p>Developed by Y. Herrera , and A. Serenelli ,<br>Institute of Space Sciences (ICE - CSIC), Barcelona, Spain.<br>This package is made available as part of the ChETEC-INFRA collaboration. </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>
Annotated Dataset for Uncertainty Mining : Gold Standard
<p> </p> <h1>Description of the dataset</h1> <p>In order to study the expression of uncertainty in scientific articles, we have put together an interdisciplinary corpus of journals in the fields of Science, Technology and Medicine (STM) and the Humanities and Social Sciences (SHS). The selection of journals in our corpus is based on the Scimago Journal and Country Rank (SJR) classification, which is based on Scopus, the largest academic database available online. We have selected journals covering various disciplines, such as medicine, biochemistry, genetics and molecular biology, computer science, social sciences, environmental sciences, psychology, arts and humanities. For each discipline, we selected the five highest-ranked journals. In addition, we have included the journals PLoS ONE and Nature, both of which are interdisciplinary and highly ranked.</p> <p>Based on the corpus of articles from different disciplines described above, we created a set of annotated sentences as follows:</p> <ul> <li>593 were pre-selected automatically, by studying the occurrences of the lists of uncertainty indices proposed by Bongelli et al. (2019), Chen et al. (2018) and Hyland (1996).</li> <li>The remaining sentences were extracted from a subset of articles, consisting of two randomly selected articles per journal. These articles were examined by two human annotators to identify sentences containing uncertainty and to annotate them.</li> <li>600 sentences not expressing scientific uncertainty were manually identified and reviewed by two annotators<br><br></li> </ul> <p>The sentences were annotated by two independent annotators following the annotation guide proposed by Ningrum and Atanassova (2024). The annotators were trained on the basis of an annotation guide and previously annotated sentences in order to guarantee the consistency of the annotations. <br>Each sentence was annotated as expressing or not expressing uncertainty (<strong>Uncertainty</strong> and <strong>No Uncertainty)</strong>.<br>Sentences expressing uncertainty were then annotated along five dimensions: Reference , Nature, Context , Timeline and Expression. <br>The annotators reached an average agreement score of 0.414 according to Cohen's Kappa test, which shows the difficulty of the task of annotating scientific uncertainty.<br>Finally, conflicting annotations were resolved by a third independent annotator.</p> <p><br>Our final corpus thus consists of a total of 1 840 sentences from 496 articles in 21 English-language journals from 8 different disciplines.<br>The columns of the table are as follows:</p> <ol> <li><strong>journal</strong>: name of the journal from where the article originates</li> <li><strong>article_title</strong>: title of the article from where the sentence is extracted</li> <li><strong>publication_year</strong>: year of publication of the article</li> <li><strong>sentence_text</strong>: text of the sentence expressing or not expressing uncertainty</li> <li><strong>uncertainty</strong>: 1 if the sentence expresses uncertainty and 0 otherwise;</li> <li><strong>ref, nature, context, timeline, expression</strong>: annotations of the type of uncertainty according to the annotation framework proposed by Ningrum and Atanassova (2023). The annotation of each dimension in this dataset are in numeric format rather than textual. The mapping betwen textual and numeric labels is presented in the Table below.</li> </ol> <table> <tbody> <tr> <td>Dimension</td> <td>1</td> <td>2</td> <td>3</td> <td>4</td> <td>5</td> </tr> <tr> <td>Reference</td> <td>Author</td> <td>Former</td> <td>Both</td> <td> </td> <td> </td> </tr> <tr> <td>Nature</td> <td>Epistemic</td> <td>Aleatory</td> <td>Both</td> <td> </td> <td> </td> </tr> <tr> <td>Context</td> <td>Background</td> <td>Methods</td> <td>Res&Disc</td> <td>Conclusion</td> <td>Others</td> </tr> <tr> <td>Timeline</td> <td>Past</td> <td>Present</td> <td>Future</td> <td> </td> <td> </td> </tr> <tr> <td>Expression</td> <td>Quantified</td> <td>Unquantified</td> <td> </td> <td> </td> <td> </td> </tr> </tbody> </table> <p><br>This gold standard has been produced as part of the <a href="https://project-inscim.github.io/">ANR InSciM (Modelling Uncertainty in Science) project.</a> </p> <h1>References</h1> <p><br>Bongelli, R., Riccioni, I., Burro, R., & Zuczkowski, A. (2019). Writers’ uncertainty in scientific and popular biomedical articles. A comparative analysis of the British Medical Journal and Discover Magazine [Publisher: Public Library of Science]. PLoS ONE, 14 (9). <a href="https://doi.org/10.1371/journal.pone.0221933">https://doi.org/10.1371/journal.pone.0221933</a></p> <p>Chen, C., Song, M., & Heo, G. E. (2018). A scalable and adaptive method for finding semantically equivalent cue words of uncertainty. Journal of Informetrics, 12 (1), 158–180. <a href="https://doi.org/10.1016/j.joi.2017.12.004">https://doi.org/10.1016/j.joi.2017.12.004</a></p> <p><br>Hyland, K. E. (1996). Talking to the academy forms of hedging in science research articles [Publisher: SAGE Publications Inc.]. Written Communication, 13 (2), 251–281. <a href="https://doi.org/10.1177/0741088396013002004">https://doi.org/10.1177/0741088396013002004</a></p> <p>Ningrum, P. K., & Atanassova, I. (2023). Scientific Uncertainty: An Annotation Framework and Corpus Study in Different Disciplines. 19th International Conference of the International Society for Scientometrics and Informetrics (ISSI 2023). <a href="https://doi.org/10.5281/zenodo.8306035">https://doi.org/10.5281/zenodo.8306035</a></p> <p>Ningrum, P. K., & Atanassova, I. (2024). Annotation of scientific uncertainty using linguistic patterns. Scientometrics. <a href="https://doi.org/10.1007/s11192-024-05009-z">https://doi.org/10.1007/s11192-024-05009-z</a></p>
Gold standard for tractograms and meshes io testing
<p>Gold standard for tractograms (<em>trk, tck, trx, dpy, vtk, fib</em>) and meshes (<em>gii, pial, vtk, ply, stl, obj</em>) IO testing for the StatefulTractogram (SFT) and StatefulSurface (SFS) in Dipy.</p>
Clumped Isotope Data from Gar Scale Bioapatite Samples and Lab Standards
<p>We measured the clumped isotope, Δ<sub>47</sub>, composition of the carbonate in the bioapatite of modern gar scales from 19 specimens collected from eight locations in North America, with mean water temperatures ranging from 11.3 to 27.0 °C. Samples were reacted at 90 °C and standards were reacted at both 25 °C and 90 °C. Samples and standards were run on a MAT 253 dual-inlet gas-source isotope ratio mass spectrometer (Thermo Scientific, USA) housed at the Yale University Analytical and Stable Isotope Center. Raw data was processed using the most up-to-date methods (Petersen et al., 2019). Our sample reproducibility was 0.021‰ (1 SD). Our standards included cylinder CO<sub>2 </sub>(Airgas, USA), CO<sub>2</sub> equilibrated with water at 25 °C and 50 °C, Carrara marble, and the ETH carbonate anchors. Δ<sub>47</sub> data is presented as ‰, InterCarb-Carbon Dioxide Equilibrium Scale (I-CDES) 90 °C for samples and standards reacted at 90 °C and I-CDES 25 °C for standards reacted at 25 °C.</p> <p> </p> <p>We use modern climate data from Daymet V4 (Thornton et al., 2022). We convert variable water temperatures into an effective temperature <em>T</em><sub>e</sub>, which accounts for the influence of temperature on growth rate. <em>T</em><sub>e</sub> for our gar samples ranges from 13.8 to 27.1 °C. We used this dataset to create a new calibration relating temperature to the Δ<sub>47</sub> in gar scale bioapatite. The resulting calibration curve is: Δ<sub>47</sub> = (0.1206 ± 0.0171) x 10<sup>6</sup>/<em>T</em><sub>e</sub><sup>2</sup> – (0.7429 ± 0.0587) (1 SE), with <em>R</em><sup>2</sup> = 0.75.</p>
Coastal and Marine Ecological Classification Standard (CMECS) Catalog
<p>The <strong>Coastal and Marine Ecological Classification Standard (CMECS) Catalog</strong> is the authoritative collection of ecological units (terms + definitions) and unit relationships (the CMECS classification framework).</p> <p>The CMECS Catalog is the complete representation of the CMECS classification. It contains all units that are or have been members of the CMECS classification throughout its lifecycle, as well as various annotations that provide metadata for each unit that enable Findability, Accessibility, Interoperability, and Reuse (FAIR, <a href="https://www.go-fair.org/fair-principles/" rel="nofollow">https://www.go-fair.org/fair-principles/</a>). The CMECS Catalog (cmecs.owl) file is stored and managed in a Git repository; authoritative versions are publicly released via <a href="https://github.com/NOAA-OCM/cmecs" target="_blank" rel="noopener">the NOAA-OCM/cmecs GitHub</a> as changes are made. Version releases also include the CMECS Catalog in CSV and XLSX formats. A browsable text output of the CMECS Catalog ecological units and implementation guidance, the <a href="https://github.com/NOAA-OCM/cmecs/wiki/CMECS-Thesaurus-Quick-Link"><strong>CMECS Thesaurus</strong></a>, is also available in PDF and MD formats.</p> <p>This release includes changes to the Substrate Component Unit Codes and fixes to Biotic Component typographical errors. Details are available on the <a href="https://github.com/NOAA-OCM/cmecs/releases/tag/v1.1.1" target="_blank" rel="noopener">CMECS GitHub v1.1.1 Release Page.</a></p> <p><strong>Questions? Please contact the CMECS Implementation Group at ocm.cmecs-ig@noaa.gov</strong></p> <p>For more information about the CMECS Catalog, see the <a href="https://github.com/NOAA-OCM/cmecs/wiki">https://github.com/NOAA-OCM/cmecs/wiki.</a></p> <p>For more information about CMECS, including technical guidance and classification examples, visit the <a href="https://iocm.noaa.gov/standards/cmecs-home.html" rel="nofollow">NOAA Integrated Ocean and Coastal Mapping (IOCM) team's CMECS webpage</a>.</p> <p>CMECS follows a Dynamic Standard Process to review and adopt changes that are proposed by the CMECS user community when necessary. More information about CMECS maintenance can be found on the <a href="https://www.ncei.noaa.gov/products/coastal-marine-ecological-classification-standard" rel="nofollow">NOAA National Centers for Environmental Information (NCEI) CMECS webpage</a> under the <strong>Vocabulary Maintenance</strong> section, along with instructions for proposing revisions to CMECS and a form for submitting proposals.</p>
Standard Cross-Cultural Sample of Religion
<p>The Standard Cross-Cultural Sample of Religion is a product of the <a href="https://religiondatabase.org/" rel="nofollow">Database of Religious History (DRH)</a>. The DRH is a qualitative-quantitative encyclopedic database of historical religious data across time and space. Data are contributed to the project by academic <a href="https://religiondatabase.org/landing/about/people/experts" rel="nofollow">experts</a> and overseen by a panel of <a href="https://religiondatabase.org/landing/about/people/editors" rel="nofollow">editors</a>. The data take the form of answers (provided by experts) to a long list of standard questions grounded in time and space.</p> <p>The Standard Cross-Cultural Sample of Religion is “standard” in a different way than its namesake, The Standard Cross-Cultural Sample (SCCS). The SCCS was designed to control for region and cultural relatedness. Because of our mostly bottom-up, expert-driven data gathering method, DRH data is heavily overweighted in certain time/space regions. Analysts will have to control for this as they see fit.</p> <p>On the other hand, DRH data is “standard” in the sense that whatever Group, Place of Text is being portrayed, experts are answering a standardized set of questions, allowing a degree of comparison and quantitative analysis that has simply never been possible before. As the DRH grows, top-down data-gathering pushes will be targeted at underrepresented regions of the world, with the goal of making future versions of the SCCSR more and more comprehensive.</p> <p>The Standard Cross-Cultural Sample of Religion (SCCSR.v2) is provided under CC-BY-4.0 license.</p>
SCALE-WIN19 Standard CTD Rosette Data
<p>The files here contain the standard CTD bottle and sensor files for the SCALE Winter Cruise. </p> <p>Oxygen sensor data is now included - except for stations PUZ, SAZ2, GT1, GTE, GT1, MIZ1 and MIZ2.</p> <p>For notes on how the data was processed please refer to the SCALE CTD Processing Report.</p> <p> </p> <table style="border-collapse: collapse; width: 100%; height: 156.75px;"><colgroup><col style="width: 50%;"><col style="width: 50%;"></colgroup> <tbody> <tr style="height: 19.5938px;"> <td style="height: 19.5938px;"><strong>Variable</strong></td> <td style="height: 19.5938px;"><strong>Units</strong></td> </tr> <tr style="height: 19.5938px;"> <td style="height: 19.5938px;">Temperature</td> <td style="height: 19.5938px;">degrees C</td> </tr> <tr style="height: 19.5938px;"> <td style="height: 19.5938px;">Conductivity</td> <td style="height: 19.5938px;">S/m</td> </tr> <tr style="height: 19.5938px;"> <td style="height: 19.5938px;">Salinity</td> <td style="height: 19.5938px;">PSU</td> </tr> <tr style="height: 19.5938px;"> <td style="height: 19.5938px;">Oxygen in situ/Oxygen theoretical/AOU</td> <td style="height: 19.5938px;">mL/L</td> </tr> <tr style="height: 19.5938px;"> <td style="height: 19.5938px;">Oxygen Saturation</td> <td style="height: 19.5938px;">%</td> </tr> <tr style="height: 19.5938px;"> <td style="height: 19.5938px;">Density</td> <td style="height: 19.5938px;">kg/m3</td> </tr> <tr style="height: 19.5938px;"> <td style="height: 19.5938px;">Chlorophyll</td> <td style="height: 19.5938px;">mg/m3</td> </tr> <tr> <td>Beam Transmission</td> <td>%</td> </tr> <tr> <td>Beam Attenuation</td> <td>m</td> </tr> </tbody> </table>
Data from PV module energy rating standard IEC 61853-3 intercomparison
<p>This is the data from PV module energy rating standard IEC 61853-3 intercomparison.</p> <p>Details can be found in:</p> <p>M. R. Vogt, S. Riechelmann, A. M. Gracia-Amillo, A. Driesse, A. Kokka, K. Maham, P. Kärhä, R. Kenny, C. Schinke, K. Bothe, J. C. Blakesley, E. Music, F. Plag, G. Friesen, G. Corbellini, N. Riedel-Lyngskær, R. Valckenborg, M. Schweiger, W. Herrmann, „PV module energy rating standard IEC 61853-3 intercomparison and best practice guidelines for implementation and validation”, accepted IEEE JPV. DOI (identifier) 10.1109/JPHOTOV.2021.3135258</p>
Standard Reference Database : ITV-CORE
<p>Decisions demand data, and poor quality data can lead to wrong, inaccurate, or late decisions. The way in which data is collected, stored and shared will reverberate in its quality and accuracy, consequently, reflecting on the ability to understand the aspects they represent. The private sector acting in the environmental area demands objectivity and assertiveness, and that is why it is essential to treat the data that subsidize conservation and restoration actions with exceptional care. To assess the state of biodiversity and environmental impacts, extensive field surveys are often required; for this, independent service providers are hired, who are specialized in obtaining a variety of types of information. Consequently, different collection methods are applied, and almost always methodological and formatting inconsistencies can be found in the resulting data. For the subsequent integration of this data into databases, it will be necessary to extract, adjust and standardize them, generating an entirely new demand, consuming time, human effort and financial resources. In addition, this demand also increases the risk of misinterpretation, typing and digitization errors, which can compromise quality, or even lead to loss of information. The standardization of data used in the survey, inventories, storage and sharing processes is a strategic solution to increase efficiency, reduce costs and risks of information degradation and loss. Furthermore, it brings a number of other benefits, such as the transformation of the analogic field recording system (field notebooks) to an entirely digital format, with the integration of cameras, tablets, dataloggers, and other widely available technologies. When it comes to preparing a recommendation for the standardization of data in a comprehensive and inclusive way, we mapped the biodiversity data frequently used by researchers from the Biodiversity and Ecosystem Services group at The Instituto Tecnológico Vale. Through this mapping, we seek to understand the types of data that already exist, how they have been used, stored and shared in databases, but also their convergence and peculiarities. With the participation of researchers, we seek to develop and validate a preliminary system of terms and metadata, including recommendations for best practices, aiming to improve the use of environmental and biodiversity data. The mapping showed a series of correspondences regarding the types of data used by the BES-ITV group, especially in the data applied in studies of Conservation and Restoration, Landscape Ecology, Genomics and Radio Frequency Identification. But also a great diversity of research topics (Total=29), focusing on six large biological groups, aspects that demonstrate the high multidisciplinary and wide coverage of environmental, ecological, genetic and biodiversity data used by the group. Based on these results, a system of terms and metadata is being developed, as well as the idealization of a modular system for the automatic generation of field digital spreadsheets, in order to simplify data collection through exclusively digital means.</p> <p> </p>
WaterGAP2.2d model derived Potential evapotranspiration and Renewable water resources variables with standard and modified PET calculation methods
<p>This data set is produced as a part of the ''Improving the quantification of climate change hazards by hydrological models: A simple ensemble approach for considering the uncertain effect of vegetation response to climate change on potential evapotranspiration" journal publication (in preparation). WaterGAP2.2d global hydrological model with two different settings; 1) with standard PET method Priestley-Taylor (PT) and 2) with modified approach (PT-MA) (please refer to the publication for more details on the method) used to derive the data set. The bias-adjusted GCM-derived (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, and MIROC5) climate data under RCP2.6 and RCP8.5 emission scenarios were used as the input. The model-derived potential evapotranspiration and the renewable water resources variables are available from 1981 to 2099 on the monthly scale for each land grid cell (spatial resolution: 0.5 degrees x 0.5 degrees). The data files are in the netCDF format (.nc4). </p>
Distinguishing between canonical and non-canonical tRNA genes reveals that Thermococcaceae adhere to the standard archaeal tRNA gene set
<p><strong>Abstract</strong></p> <p>Automated genome annotation is an essential tool for extracting biological information from sequence data. The identification and annotation of tRNA genes is frequently performed by the software package tRNAscan-SE, the output of which is listed – for selected genomes – in the Genomic tRNA database (GtRNAdb). Given the central role of tRNA in molecular biology, the accuracy and proper application of tRNAscan-SE is important for both interpretation of the output, and continued improvement of the software. Here, we report a manual annotation of the predicted tRNA gene sets for 20 complete genomes from the archaeal taxon Thermococcaceae. According to GtRNAdb, these 20 genomes contain a number of putative deviations from the standard set of canonical tRNA genes in Archaea. However, manual annotation reveals that only one represents a true divergence; the other instances are either (i) non-canonical tRNA genes resulting from the integration of horizontally transferred genetic elements, or CRISPR-Cas activity, or (ii) attributable to errors in the input DNA sequence. To distinguish between canonical and non-canonical archaeal tRNA genes, we recommend using a combination of automated pseudogene detection by tRNAscan-SE and the tRNAscan-SE isotype score, greatly reducing manual annotation efforts and leading to improved predictions of tRNA gene sets in Archaea.</p> <p> </p> <p><strong>Repository contents</strong></p> <p><strong>01_workflow_tRNAscanSE_predictions_210archaea.html </strong>contains the workflow and graphical output for tRNA gene set predictions in 20 Thermococcaceae genomes and 210 archaeal genomes. Files 03 to 06 below are the files quoted in this workflow.</p> <p><strong>02_workflow_tRNAscanSE_predictions_210archaea.Rmd </strong>contains the markdown file associated with 01_workflow_tRNAscanSE_predictions_210archaea.html above.</p> <p><strong>03_thermo_trnas_GtRNAdb.txt</strong><strong> </strong>contains the predicted tRNA gene sets of 20 Thermococcaceae genomes as listed on GtRNAdb (Data Release 19 (June 2021)).</p> <p><strong>04_Archaea_genome_list.txt </strong>contains the details of all 217 archaeal genomes listed on GtRNAdb (Data Release 19 (June 2021)). The seven genomes for which the NCBI genome sequences were no longer available are indicated by #### preceding the name.</p> <p><strong>05_thermo_tRNAs_genome.txt</strong><strong> </strong>contains the predicted tRNA gene sets of 20 Thermococcaceae genomes as predicted by locally run tRNAscan-SE (version 2.0.6), with standard settings for Archaea (option -A). To display the output, options -H and --detail were added. We note that pseudogene detection is active under these conditions.</p> <p><strong>06_Archaea_210_GtRNAdb_tRNAs.txt </strong>contains the predicted tRNA gene sets of the 210 archaeal genomes as listed on GtRNAdb (Data Release 19 (June 2021)).</p> <p><strong>07_Archaea_210genomes_tRNAs.txt</strong> contains the predicted tRNA gene sets of the 210 archaeal genomes as predicted by locally run tRNAscan-SE (version 2.0.6), with standard settings for Archaea (option -A). To display the output, options -H and --detail were added. We note that pseudogene detection is active under these conditions.</p> <p><strong>08_NCBI_genomes.zip</strong> contains the NCBI GenBank genome sequence files used in this study. These include the 20 Thermococcaceae genomes, the wider 210 archaeal genomes, and several others of interest. </p> <p><strong>09_phylogeny.tar.zip</strong> contains the data used to draw a phylogenetic tree for the 20 Thermococcaceae organisms. The folder includes a file listing the details of all data in the folder (Readme.md), a workflow file (workflow_UndinMarkers_v2.md), and data folders.</p> <p> </p> <p><strong>Notes</strong></p> <p>The extended TIGRFAM database referred to in the phylogenetic tree construction process can be found at <a href="https://zenodo.org/record/3839790#.YjByaVzMI3g">https://zenodo.org/record/3839790#.YjByaVzMI3g</a></p> <p>The perl script used during phylogenetic tree construction, catfasta2phyml.pl, is available in the GitHub repository <a href="https://github.com/nylander/catfasta2phyml">https://github.com/nylander/catfasta2phyml</a></p> <p>tRNAscan-SE is a freely available resource available online (<a href="http://lowelab.ucsc.edu/tRNAscan-SE/">http://lowelab.ucsc.edu/tRNAscan-SE/</a>)</p> <p>GtRNAdb is a publicly accessible resource available online (<a href="http://gtrnadb.ucsc.edu/">http://gtrnadb.ucsc.edu/</a>)</p> <p>NCBI is a publicly accessible resource available online (<a href="https://www.ncbi.nlm.nih.gov/">https://www.ncbi.nlm.nih.gov/</a>)</p> <p>rrnDB is a publicly accessible resource available online (<a href="https://rrndb.umms.med.umich.edu/">https://rrndb.umms.med.umich.edu/</a>)</p> <p>BLAST is a publicly accessible resource available online (<a href="https://blast.ncbi.nlm.nih.gov/Blast.cgi">https://blast.ncbi.nlm.nih.gov/Blast.cgi</a>)</p>
Diagnostic accuracy of a set of clinical and radiological criteria for screening of COVID-19 using RT-PCR as the reference standard - Dataset
<p>Dataset of a cohort whose summary is described below.</p> <p>Abstract</p> <p><strong>Objective:</strong> To evaluate the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of a set of clinical-radiological criteria for COVID-19 screening in patients with severe acute respiratory failure (SARF) admitted to intensive care units (ICUs), using reverse-transcriptase polymerase chain reaction (RT-PCR) as the reference standard. <strong>Method: </strong>Diagnostic accuracy study including a historical cohort of 1009 patients consecutively admitted to ICUs across six hospitals in Curitiba (Brazil) from March to September, 2020. The sample was stratified into groups by the strength of suspicion for COVID-19 (strong <em>versus</em> weak) using parameters based on three clinical and radiological (chest computed tomography) criteria. The diagnosis of COVID-19 was confirmed by RT-PCR (referent). <strong>Results:</strong> With respect to RT-PCR, the proposed criteria had 98.5% (95% confidence interval [95% CI] 97.5–99.5%) sensitivity, 70% (95% CI 65.8–74.2%) specificity, 85.5% (95% CI 83.4–87.7%) accuracy, PPV of 79.7% (95% CI 76.6–82.7%) and NPV of 97.6% (95% CI 95.9–99.2%). <strong>Conclusion: </strong>The proposed set of clinical-radiological criteria were accurate in identifying patients with strong <em>versus</em> weak suspicion for COVID-19 and had high sensitivity and considerable specificity with respect to RT-PCR. These criteria may be useful for screening COVID-19 in patients presenting with SARF.</p>
Example subjects for Mobilise-D data standardization
<p>Standardized data from Mobilise-D participants (YAR dataset) and pre-existing datasets (ICICLE, MSIPC2, Gait in Lab and real-life settings, MS project, UNISS-UNIGE) are provided in the shared folder, as an example of the procedures proposed in the publication "Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization" that is currently under review in Scientific data. Please refer to that publication for further information. Please cite that publication if using these data. </p> <p>The code to standardize an example subject (for the ICICLE dataset) and to open the standardized Matlab files in other languages (Python, R) is available in github (https://github.com/luca-palmerini/Procedure-wearable-data-standardization-Mobilise-D).</p>
The Standardized Vegetation Optical Depth Index SVODI
<p><strong>Related paper with detailed description: </strong><a href="https://doi.org/10.5194/bg-19-5107-2022">https://doi.org/10.5194/bg-19-5107-2022</a></p> <p><strong>Short summary:</strong> The Standardized Vegetation Optical Depth index (SVODI) can be used to monitor the vegetation condition, such as whether the vegetation is unusually dry or wet. SVODI has global coverage and spans the past three decades and is derived from multiple space-borne passive microwave sensors of that period. SVODI is based on a new probabilistic merging method that allows the merging of normally distributed data, even if the data is not gap-free.</p> <p><strong>Files: </strong></p> <ul> <li>"SVODI_v01.zip" <ul> <li>Contains the bulk SVODI data, globally, from 1987-07-10 to 2019-12-31. Files are daily global netcdf images, with a 0.25 degree spatial resolution. Is unzipped roughly the same size.</li> </ul> </li> <li>"svodi_v01_0_2006-06-26.nc" <ul> <li>An arbitrary file from SVODI_V01.zip. For your convenience in case you want to see an example first without downloading the whole thing.</li> </ul> </li> <li>"ESA-CCI-SOILMOISTURE-LAND_AND_RAINFOREST_MASK-fv04.2.nc" <ul> <li>Grid of SVODI, Source: https://github.com/TUW-GEO/smecv-grid</li> </ul> </li> </ul> <p><strong>Data fields:</strong></p> <ul> <li>"svodi" <ul> <li>The standardized vegetation optical depth index</li> <li>unitless</li> <li>range: -inf, inf</li> </ul> </li> <li>"flag" <ul> <li>Bit-flag indicating which <sensor>-<band> combination contributed to each observation <ul> <li>1: AMSRE-C</li> <li>2: AMSR2-C</li> <li>3: WindSat-C</li> <li>4: AMSRE-X</li> <li>5: AMSR2-X</li> <li>6: WindSat-X</li> <li>7: TMI-X</li> <li>8: AMSRE-Ku</li> <li>9: AMSR2-Ku</li> <li>10: WindSat-Ku</li> <li>11: TMI-Ku</li> <li>12: SSMI-Ku</li> </ul> </li> </ul> </li> </ul>
Elliptical Alignment Holes Enabling Accurate Direct Assembly of Microchips to Standard Waveguide Flanges at sub-THz Frequencies - Dataset
<p>Current waveguide flange standards do not allow for the accurate fitting of microchips, due to the large mechanical tolerances of the flange alignment pins and the brittle nature of Silicon, requiring greatly oversized alignment holes on the chip to fit worst-case fabrication tolerances, resulting in unacceptably large misalignment error for sub-THz frequencies. This paper presents, for the first time, a new method for directly aligning micromachined Silicon chips to standard, i.e. unmodified, waveguide flanges with alignment accuracy significantly better than the waveguide-flange fabrication tolerances, through the combination of a tightly-fitting circular and an elliptical alignment hole on the chip. A Monte Carlo analysis predicts the reduction of the mechanical assembly margin by a factor of 5.5 compared to conventional circular holes, reducing the potential chip misalignment from 46 μm to 8.5 μm for a probability of fitting of 99.5%. For experimental verification, micromachined waveguide chips using either conventional (oversized) circular or the proposed elliptical alignment holes were fabricated and measured. A reduction in the standard deviation of the reflection coefficient by a factor of up to 20 was experimentally observed from a total of 200 measurements with random chip placement, exceeding the<br> expectations from the Monte Carlo analysis. To our knowledge, this paper presents the first solution for highly accurate assembly<br> of micromachined waveguide chips to standard waveguide flanges, requiring no custom flanges or other tailor-made split blocks.<br> </p>
Nonperturbative study of the electroweak phase transition in the real scalar singlet extended Standard Model: dataset
<p>Collection of simulation results as presented in figures of the paper "Nonperturbative study of the electroweak phase transition in the real scalar singlet extended Standard Model".</p>
ΔvapHm-VOC: Standard Molar Vaporization Enthalpy Database for Machine Learning Prediction Models
<p>We present the full database of the article "Data-Driven, Explainable Machine Learning Model for Predicting Volatile Organic Compounds’ Standard Vaporization Enthalpy".</p> <p>This is the database used for data driven, explainable supervised ML model to predict Δ<sub>vap</sub><em>H</em><sub>m</sub>° of VOCs. The model was built on an established experimental database of 2410 unique molecules and 223 VOCs categorized by chemical groups. Using supervised ML regression algorithms, the Random Forest successfully predicted VOCs’ Δ<sub>vap</sub><em>H</em><sub>m</sub>° with a mean absolute error of 3.02 kJ mol<sup>-1</sup> and a 94% test score. The model was successfully validated through the prediction of Δ<sub>vap</sub><em>H</em><sub>m</sub>° for a known database of VOCs and through molecular group hold-out tests.</p> <div> <div> <div> <div> <p>The model's database was built with a variety of molecules from diverse chemical families with known experimental Δ<sub>vap</sub><em>H</em><sub>m</sub>° values. Entries were collected from <a href="https://doi.org/10.1063/1.3309507" target="_blank" rel="noopener">Acree and Chickos’ 2010 compilation</a>, curated by <a href="https://doi.org/10.1016/j.fluid.2013.09.021" target="_blank" rel="noopener">Gharagheizi (2013)</a>, with experimental vaporization enthalpy at the standard temperature of 298.15 K. This database was selected as it is an open-access repository, generally presenting experimental values with low uncertainties and corrected for the real-to-ideal behavior of the gas phase. We introduced a routine to convert and present each chemical entry into a SMILES string, along with chemical family categorization. For VOCs, we built a specific database of compounds documented in a VOC regulatory environmental guideline (<a href="https://www.s-t-a.org/Files%20Public%20Area/Documents/The%20Categorisation%20of%20Volatile%20Organic%20Compounds%20HMIP%20(1996).pdf">Marlowe <em>et al</em>., 1995</a>), and we used our web-scrapping routine to gather experimental Δ<sub>vap</sub><em>H</em><sub>m</sub>° values. The external dataset for validation studies was also collected from <a href="https://doi.org/10.1016/j.fluid.2013.09.021" target="_blank" rel="noopener">Gharagheizi (2013)</a>.</p> <p>Along with Δ<sub>vap</sub><em>H</em><sub>m</sub>° experimental values, each molecule is represented by its CAS number, SMILES string and InChlKey. We generated 106 chemical descriptors for every molecule in the database, using <a href="http://http//www.rdkit.org/">RDKit</a> software version 2022.09.4, running on top of Python 3.9. Descriptors were calculated from the “MolFromSmiles” function in “RDKIT.Chem” as descriptors with non-numerical values were removed. The descriptors encode significant chemical information and are used to present physicochemical characteristics of compounds, building a relationship between structure and Δ<sub>vap</sub><em>H</em><sub>m</sub>°.</p> </div> </div> </div> </div> <p>Through chemical feature importance analysis, the explainable model revealed that VOC polarizability, connectivity indexes and electrotopological state are key for the model’s prediction accuracy. We thus present a replicable and explainable model, which can be further expanded towards the prediction of other thermodynamic properties of VOCs.</p>
Dataset for publication "Parallel experiments in electrochemical CO2 reduction enabled by standardized analytics"
<p>Dataset for the publication: "<strong>Parallel experiments in electrochemical CO<sub>2</sub> reduction </strong><strong>enabled by standardized analytics</strong>", https://doi.org/10.1038/s41929-024-01172-x,<strong> </strong>divided by paper Figure. The dataset contains data that are both raw and processed using the open-source software available at http://dgbowl.github.io </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.