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85 results for “standard dataset”
Dataset of report "A.2.2.6: Validation of the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for 2 terpenes using TD-GC/MS/FID and the static standards produced in A1.1.2."
<p>Dataset of report "A.2.2.6: Validation of the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for 2 terpenes using TD-GC/MS/FID and the static standards produced in A1.1.2."</p>
Perturbative gravitational wave predictions for the real scalar extended Standard Model, dataset
<p>This deposit contains data from a perturbative study of cosmological phase transitions in the real singlet scalar extension of the Standard Model (xSM). The data relates to the paper "Perturbative gravitational wave predictions for the real scalar extended Standard Model". Everything is contained within the archive file <em>xsm_results.tar.gz</em>, a tarball compressed with Gzip.</p> <p>The data covers phase transition properties for a scan of 100,000 parameter points in the xSM. Further details on the contents of the dataset are explained in the <em>README.md</em> within the tarball.</p>
zbMATHOpenRec: A Gold Standard Dataset for Recommending Scientific Documents with Mathematical Content
<p> </p> <p>Here we include the first gold standard dataset for recommending scientific documents with mathematical content. </p> <p><strong>Contents: </strong></p> <p>As of Feb-2023, there are 421 recommendation pairs with 80 seed documents.</p> <ol> <li>All recommendation pairs are available: recommendationPairs.csv</li> <li>Each document's contents, such as title, abstract/review/summary, authors, MSC codes, Full-text link, references, etc. are available in: documentContents.csv</li> </ol> <p><strong>Dataset construction process</strong>:</p> <p>This is the first gold standard content-based RS dataset, consisting of 421 scientific research entry recommendation pairs with mathematical content. The purpose is to enable math in scientific documents for document recommendations, meaning if two documents have similar math content, one could be recommended to the other. </p> <p>To create this dataset, we analyzed 4.5 million research entires from zbMATH Open (https://zbmath.org/) and performed the following steps to obtain the final dataset:</p> <ol> <li>We selected 80 seeds that capture the most word and math tokens in zbMATH Open using statistical measures.</li> <li>Three experts, one with several years of experience reviewing research entries in mathematics, curated the recommendations for 80 seeds.</li> </ol> <p>Using this dataset, researchers can accelerate the development and testing of recommendation approaches for scientific literature with mathematical content, improving recommendations for the STEM fields where mathematical content is currently being ignored</p> <p>## License </p> <p>Legal restrictions and copyright: The zbMATH Open data is subject to the Terms and Conditions for the zbMATH Open API Service of FIZ Karlsruhe – Leibniz-Institut für Informationsinfrastruktur GmbH. Content generated by zbMATH Open, such as reviews, classifications, software, or author disambiguation data, are distributed under CC-BY-SA 4.0. This defines the license for the whole dataset, which also contains non-copyrighted bibliographic metadata and reference data derived from I4OSC (CC0).</p>
[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>
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>
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>
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>
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>
CLDF dataset on Panoan Languages in Standardized Transcription derived from Key and Comrie's "Intercontinental Dictionary Series" from 2023
<p>Cite the source of the dataset as:</p> <blockquote> <p>Miller, J. and List, J.-M. (2024): Providing standardized phonetic transcriptions for the Panoan languages in the Intercontinental Dictionary Series. Computer-Assisted Language Comparison in Practice 7.2. URL: https://calc.hypotheses.org/7503.</p> </blockquote>
MADERA: A standardized Pan-Amazonian dataset for tropical timber species
<p>We compiled and presented a dataset for all timber species reported in the Amazon region from all nine South American Amazonian countries. This was based on official information from every country, as well as from two substantial scientific references. We verified the standard taxonomic names from each individual source, using the Taxonomic Name Resolution Service (TNRS) and considered all Amazonian tree species with DBH ≥ 10 cm. We also obtained estimates of the current population size for most species from a published approach based on data from 1,900 tree inventory plots (1-hectare each) distributed across the Amazon region and part from the Amazon Tree Diversity Network (ATDN). We then identified the hyperdominant timber species. In addition, we overlapped our timber species list with data for species that are used for commercial purposes, according to the International Tropical Timber Organization (ITTO), the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) and the International Union for Conservation of Nature (IUCN) taxa assessment and Red List categories. Finally, we also included IUCN Red List categories based on combined deforestation, and climate change scenarios for these species. Our final Amazonian timber species dataset contains 1,112 unique species records, which belong to 337 genera and 72 families from the lowland Amazonian rainforest, with associated information related to population, conservation, and trade status of each species. The authors of this research expect that the information provided will be useful to strengthen the public forestry policies of the Amazon countries, inform ecological studies, as well for forest management purposes. </p>
Dataset - A Standard-compliant Assessment of Beyond-eMBB QoS/QoE in 5G Networks
<p>The present dataset is open-sourced with the paper "A Standard-compliant Assessment of Beyond-eMBB QoS/QoE in 5G Networks".</p> <p>The paper is authored by Giuseppe Caso, Mohammad Rajiullah, Anna Brunstrom, Luca De Nardis, Ozgu Alay, and Marco Neri. </p> <p>If you use the dataset for your own research activities and publications, please consider citing the paper as follows: </p> <p><strong>G. Caso et al., "A Standard-compliant Assessment of Beyond-eMBB QoS/QoE in 5G Networks," in Proceedings of the IEEE Conference on Standards for Communications and Networking (IEEE CSCN'24), pp. 1-7, 2024.</strong></p> <p><strong>@inproceedings{caso2024standard,</strong><br><strong> title={{A Standard-compliant Assessment of Beyond-eMBB QoS/QoE in 5G Networks}},</strong><br><strong> author={Caso, Giuseppe and Rajiullah, Mohammad and Brunstrom, Anna, and De Nardis, Luca and Alay, Ozgu and Neri, Marco},</strong><br><strong> booktitle={Proceedings of the IEEE Conference on Standards for Communications and Networking (IEEE CSCN'24)},</strong><br><strong> pages={1--7},</strong><br><strong> year={2024}</strong><br><strong>}</strong></p> <p>A detailed description of the dataset is provided in the paper, and the README file provides additional details.</p> <p>Contact Giuseppe Caso (giuseppe.caso@kau.se) for more information on the dataset and potential access to additional data. </p>
Data associated with "Developing a standardized but extendable framework to increase the findability of infectious disease datasets"
<p><strong>Data associated with "Developing a standardized but extendable framework to increase the findability of infectious disease datasets"</strong></p> <p> </p> <p>Includes:</p> <ul> <li>NIAID Dataset schema</li> <li>NIAID ComputationalTool schema</li> <li>Crosswalk between NIAID schemas and common schemas</li> <li>Survey of Schema.org-compliant repositories</li> </ul> <p><br> The open access movement and scientific reproducibility concerns have led the biomedical research community to embrace efforts to make scientific datasets openly accessible. While many datasets are now available, there are still challenges in ensuring that they are Findable, Accessible, Interoperable, and Reusable (FAIR). To improve the FAIRness of datasets, we evaluated dataset repositories for compliance with Schema.org standards – a collection of standards developed to increase metadata searchability across the internet. Adoption of the Schema.org Dataset standard was highly variable in biomedical research datasets, and the standard omitted many desirable metadata fields. We customized the Schema.org Dataset standard to catalog datasets collected across a Systems Biology research consortium consisting of 15 Centers. We developed a reusable process for creating a schema which is interoperable with other standards, but still extendable and customizable to a particular context. Here, we describe our process along with the associated gains in FAIRness, and discuss ongoing challenges with dataset discoverability – the first step to ensure that the vast amount of open data published by the research community is reused to its maximum value.</p>
Indoor Environmental Quality in Schools: NOTECH Solution vs. Standard Solution - dataset
<p>Dataset for "Indoor Environmental Quality in Schools: NOTECH Solution vs. Standard Solution"</p>
A Standardized European Hexagon Gridded Dataset Based on OpenStreetMap POIs
<p>Point of interest (POI) data refers to information about the location and type of amenities, services, and attractions within a geographic area. This data is used in urban studies research to better understand the dynamics of a city, assess community needs, and identify opportunities for economic growth and development. POI data is beneficial because it provides a detailed picture of the resources available in a given area, which can inform policy decisions and improve the quality of life for residents. This paper presents a large-scale, standardized POI dataset from OpenStreetMap (OSM) for the European continent. The dataset's standardization and gridding make it more efficient for advanced modeling, reducing 7,218,304 data points to 988,575 without significant resolution loss, suitable for a broader range of models with lower computational demands. The resulting dataset can be used to conduct advanced analyses, examine POI spatial distributions, conduct comparative regional studies, enhancing understanding of the economic activity, distribution, attractions, and subsequently, economic health, growth potential, and cultural opportunities. The paper describes the materials and methods used in generating the dataset, including OSM data retrieval, processing, standardization, and hexagonal grid generation. The dataset can be used independently or integrated with other relevant datasets for more comprehensive spatial distribution studies in future research.</p>
DATASET OF "Impact of short-acting vs. standard anaesthetic agents on obstructive sleep apnoea: a randomised, controlled, triple-blind trial"
<p>Sleep apnoea is associated with negative outcomes following general anaesthesia. Current recommendations suggest using short-acting anaesthetic agents in preference to standard agents to reduce this risk, but there is currently no evidence to support this. This randomised controlled triple-blind trial tested the hypothesis that a combination of short-acting agents (desflurane-remifentanil) would reduce the postoperative impact of general anaesthesia on sleep apnoea severity compared with standard agents (sevoflurane-fentanyl). Sixty patients undergoing hip arthroplasty under general anaesthesia were randomised to anaesthesia with desflurane-remifentanil or sevoflurane-fentanyl. Respiratory polygraphy was performed before surgery and on the first and third postoperative nights. The primary outcome was the supine apnoea-hypopnoea index on the first postoperative night. Secondary outcomes were the supine apnoea-hypopnoea index on the third postoperative night, and the oxygen desaturation index on the first and third postoperative nights. Additional outcomes included intravenous morphine equivalent consumption and pain scores on postoperative days 1, 2 and 3. Pre-operative sleep study data were similar between groups. Mean (95%CI) values for the supine apnoea-hypopnoea index on the first postoperative night were 18.9 (12.7–25.0) and 21.4 (14.2–28.7) events.h<sup>-1</sup>, respectively, in the short-acting and standard anaesthesia groups (p=0.64). Corresponding values on the third postoperative night were 28.1 (15.8–40.3) and 38.0 (18.3–57.6) events.h<sup>-1</sup> (p=0.34). Secondary sleep- and pain-related outcomes were generally similar in the two groups. In conclusion, short-acting anaesthetic agents did not reduce the impact of general anaesthesia on sleep apnoea severity compared with standard agents. These data should prompt an update of current recommendations.</p>
Dataset for comparison of QuantumPower method to the reference power standard
<p>Dataset for comparison of QuantumPower method to the power standard Radian RD-22.</p> <p>The QuantumPower method was compared to a power standard Radian RD-22. As a device under test, a Fluke 6100 power calibrator was used.</p> <p>To obtain the data, QPSW software was used:</p> <p>https://github.com/KaeroDot/QPsw</p> <p>Author: Martin Šíra</p> <p>Contact: Czech Metrology Institute, Okružní 31, 638 00 Brno, msira@cmi.cz</p> <p>Part of project Quantum traceability for AC power standards, QuantumPower, Project Number: 19RPT01.<em> </em>This project (19RPT01) has received funding from the EMPIR programme co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation programme.</p> <p>https://www.euramet.org/research-innovation/search-research-projects/details/project/quantum-traceability-for-ac-power-standards/</p>
GeoEDdA: A Gold Standard Dataset for Named Entity Recognition and Span Categorization Annotations of Diderot & d'Alembert's Encyclopédie
<p>This repository contains a gold standard dataset for named entity recognition and span categorization annotations from Diderot & d’Alembert’s Encyclopédie entries.</p> <p>The dataset is available in the following formats:</p> <ul> <li>JSONL format provided by <a href="https://prodi.gy/" rel="nofollow">Prodigy</a></li> <li>binary spaCy format (ready to use with the spaCy train pipeline)</li> </ul> <p>The Gold Standard dataset is composed of 2,200 paragraphs out of 2,001 Encyclopédie's entries randomly selected. All paragraphs were written in 19th-century French.</p> <p>The spans/entities were labeled by the project team along with using pre-labelling with early machine learning models to speed up the labelling process. A train/val/test split was used. Validation and test sets are composed of 200 paragraphs each: 100 classified under 'Géographie' and 100 from another knowledge domain. The datasets have the following breakdown of tokens and spans/entities.</p> <h2>Tagset</h2> <ul> <li><strong>NC-Spatial</strong>: a common noun that identifies a spatial entity (nominal spatial entity) including natural features, e.g. <code>ville</code>, <code>la rivière</code>, <code>royaume</code>.</li> <li><strong>NP-Spatial</strong>: a proper noun identifying the name of a place (spatial named entities), e.g. <code>France</code>, <code>Paris</code>, <code>la Chine</code>.</li> <li><strong>ENE-Spatial</strong>: nested spatial entity , e.g. <code>ville de France</code> , <code>royaume de Naples</code>, <code>la mer Baltique</code>.</li> <li><strong>Relation</strong>: spatial relation, e.g. <code>dans</code>, <code>sur</code>, <code>à 10 lieues de</code>.</li> <li><strong>Latlong</strong>: geographic coordinates, e.g. <code>Long. 19. 49. lat. 43. 55. 44.</code></li> <li><strong>NC-Person</strong>: a common noun that identifies a person (nominal spatial entity), e.g. <code>roi</code>, <code>l'empereur</code>, <code>les auteurs</code>.</li> <li><strong>NP-Person</strong>: a proper noun identifying the name of a person (person named entities), e.g. <code>Louis XIV</code>, <code>Pline</code>.</li> <li><strong>ENE-Person</strong>: nested people entity, e.g. <code>le czar Pierre</code>, <code>roi de Macédoine</code>.</li> <li><strong>NP-Misc</strong>: a proper noun identifying entities not classified as spatial or person, e.g. <code>l'Eglise</code>, <code>1702</code>, <code>Pélasgique</code></li> <li><strong>ENE-Misc</strong>: nested named entity not classified as spatial or person, e.g. <code>l'ordre de S. Jacques</code>, <code>la déclaration du 21 Mars 1671</code>.</li> <li><strong>Head</strong>: entry name</li> <li><strong>Domain-Mark</strong>: words indicating the knowledge domain (usually after the head and between parenthesis), e.g. <code>Géographie</code>, <code>Geog.</code>, <code>en Anatomie</code>.</li> </ul> <h2>HuggingFace</h2> <p>The GeoEDdA dataset is available on the HuggingFace Hub: <a href="https://huggingface.co/datasets/GEODE/GeoEDdA">https://huggingface.co/datasets/GEODE/GeoEDdA</a></p> <h2>spaCy Custom Spancat trained on Diderot & d’Alembert’s Encyclopédie entries</h2> <p>This dataset was used to train and evaluate a custom spancat model for French using <a href="https://spacy.io/" rel="nofollow">spaCy</a>. The model is available on HuggingFace's model hub: <a href="https://huggingface.co/GEODE/fr_spacy_custom_spancat_edda" rel="nofollow">https://huggingface.co/GEODE/fr_spacy_custom_spancat_edda</a>.</p> <h2>Acknowledgement</h2> <p>The authors are grateful to the <a href="https://aslan.universite-lyon.fr/" rel="nofollow">ASLAN project</a> (ANR-10-LABX-0081) of the Université de Lyon, for its financial support within the French program "Investments for the Future" operated by the National Research Agency (ANR). Data courtesy the <a href="https://artfl-project.uchicago.edu/" rel="nofollow">ARTFL Encyclopédie Project</a>, University of Chicago.</p>
A Global Dataset of Standardized Moisture Anomaly Index Incorporating Snow Dynamics (SZIsnow) from 1948 to 2010
<p>The SZI<sub>snow</sub> dataset was calculated based on systematic physical fields from the Global Land Data Assimilation System Version 2 (GLDAS-2) with the Noah land surface model. This SZI<sub>snow</sub> dataset considers different physical water-energy processes, especially snow processes. The evaluation shows the dataset is capable of investigating different types of droughts across different timescales. The assessment also indicates that the dataset has an adequate performance to capture droughts across different spatial scales. The consideration of snow processes improved the capability of SZI<sub>snow</sub>, and the improvement is evident over snow-covered areas (e.g., Arctic region) and high-altitude areas (e.g., Tibet Plateau). Moreover, the analysis also implies that SZI<sub>snow</sub> dataset is able to well capture large-scale drought events across the world. This drought dataset has high application potential for monitoring, assessing, and supplying information on drought, and also can serve as a valuable resource for drought studies.</p>
EvaNIL: silver standard dataset for large-scale NIL entity linking evaluation
<p>The EvaNIL dataset can be used to train or evaluate approaches developed for NIL entity linking. It was built from several Biomedical and Life Sciences corpora:</p> <ul> <li>PubMed DS</li> <li>CRAFT corpus</li> <li>MedMentions</li> </ul> <p>These corpora contain entities associated with knowledge base concepts. To build the EvaNIL dataset, we assumed that those knowledge base concepts did not exist in the respective knowledge bases, so each entity is associated instead with the direct ancestors of those original concepts.</p> <p>The EvaNIL dataset is divided into 6 partitions including annotations from several knowledge bases:</p> <ul> <li>"medic" (CTD-MEDIC)</li> <li>"ctd_anatomy" (CTD-Anatomy)</li> <li>"ctd_chemicals" (CTD-Chemicals)</li> <li>"chebi" (ChEBI)</li> <li>"go_bp" (GO-Biological Process)</li> <li>"hp" (HPO)</li> </ul> <p> </p>
ScienceDex guides
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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.