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7,195
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7,195 results for “SET”
Training Set
<p>Dataset (training set) used for training the neural network</p>
Data set: Evidence for the validity of the Attitudes toward Sexual Behaviours Scale (ASBS) with Canadian young people
Open the record for dataset details and reuse information.
Data set "Correlation of in vitro biofilm formation capacity with persistence of antibiotic-resistant Escherichia coli on fresh leafy produce"
<p>This repository contains the metadata and raw data for the analysis of an <em>in vitro </em>screening of 174 antibiotic-resistance E. coli strains isolated from various sources to evaluate their ability and strength to form biofilms.</p> <p>This repository contains the raw data to characterise a subset of eleven <em>E. coli </em>strains in their population dynamics and persistance on lamb's lettuce (<em>Valerianella locusta</em>) leaves.</p> <p>Raw images (czi format) of live/dead stain of selected strains in <em>V. locusta </em>leaves are provided.</p> <p>Data analysis and image processing scripts can be found in the GitHub repository associated to the manuscript.</p>
Data Set available for Liquid Biopsy of CML Diagnosis
<p><em><span>Chronic Myeloid Leukemia, CML, is caused in patients with myeloproliferative disorders by the presence of BCR::ABL1. The research discussed the integration of microfluidic chips with biosensors for the purpose of detecting biomarkers in peripheral blood thereby showing the raw experimental data set avaialble, liquid bipsy sample set available and Calculation for methods and data Simulation along with graphs, which could obviate the need for painful biopsies, thereby improving diagnostic accuracy and accessibility.</span></em></p>
Time-Motion Data Set of Construction Work
<p>This open-access dataset, provides a detailed <strong>time-motion study</strong> of construction work, specifically focusing on <strong>MEP (Mechanical, Electrical, and Plumbing) activities</strong>. The dataset is intended to facilitate research and analysis to improve operational efficiency and safety within the construction industry. It includes anonymized and pseudonymized data, ensuring privacy while still offering valuable insights into worker activities.</p> <p>Contents: (1)Time-motion study dataset: Captures categorized work activities by MEP workers at a second-to-second level. (2) Description of work activities: Provides detailed classifications of the tasks performed, allowing for in-depth analysis.</p> <p>This dataset has been made publicly available under the <strong>CC-BY-SA license</strong>, encouraging reuse and redistribution with proper attribution and share-alike terms. By downloading the dataset, users acknowledge and agree to comply with the terms outlined above.</p> <p>Funding and Support: This work has been supported by the “Hukka LVI- ja sähkötöissä” (Waste in Plumbing and Electrical Work) project, funded by STUL (Electrical Contractor Association), LVI-TU (HVAC Contractor Association), and STTA (Electrical Employers Union) from Finland.</p> <p>This comprehensive dataset offers valuable resources for research and analysis purposes. <strong>For further information or collaboration inquiries, feel free to reach out to discuss data collection methods and potential research partnerships: olli.seppanen@aalto.fi & christopher.gorsch@vtt.fi.</strong></p>
Data set for "EghiFit: Smartphone based Behaviour Monitoring and Health Recommendation in a Weight Loss Intervention Study"
<p>Dataset has been created for "EghiFit: Smartphone based Behaviour Monitoring and Health Recommendation in a Weight Loss Intervention Study" paper.</p> <p>We have created a smartphone based behaviour monitoring and recommendation system to aid patients recruited in a weight loss intervention programme.<br>The main interaction element for the patients is <strong>EghiFit</strong> application which was used in context of this dataset for data acquisition and secure transmission to our servers.</p> <p>The data consists of application usage per patient, steps achieved, nutritional information of meals logged, heart rate data, interaction data and more.<br>For more information about the dataset, please take a look at <strong>readme.md</strong> file and our paper.</p>
Consistency Bias Data Sets
<p>This is the data set correspond to the calculation of Consistency Bias. This has been submitted for publication to a journal and will be linked directly to the manuscript when published.</p>
Asia Boundary Maps in Sets
<p>Three sets of boundary maps, defining the boundaries between:</p> <ul> <li>Kingdom of Siam and British Tenasserim in 1897</li> <li>Burma and Siam in 1897</li> <li>Burma and China in 1900</li> </ul> <p>A total of 17 maps.</p>
A data set on "High-Yield Production of SiV-Doped Nanodiamonds for Spectroscopy and Sensing Applications"
<p>The data set to paper: </p> <p>High-Yield Production of SiV-Doped Nanodiamonds for Spectroscopy and Sensing Applications</p> <p>Alexander Kromka1,*, Marián Varga1,2, Kateřina Aubrechtová Dragounová1,3, Oleg Babčenko1, René. Pfeifer1, Assegid M. Flatae4, Florian Sledz4, Farzana Akther4, Mario Agio4,5, Štěpán Potocký1, Štěpán Stehlík1</p> <p>1 Institute of Physics, Czech Academy of Sciences, Prague 6, Czech Republic<br>2 Institute of Electrical Engineering, Slovak Academy of Sciences, Bratislava, Slovakia<br>3 Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Prague 1, Czech Republic<br>4 Laboratory of Nano-Optics and Cμ, University of Siegen, Walter-Flex-Str. 3, 57072 Siegen, Germany <br>5 National Institute of Optics (INO-CNR), Largo Enrico Fermi 6, 50125 Florence, Italy</p> <p>*corresponding authors: kromka@fzu.cz</p> <p>Data manager: Kristýna Dostálová: dostalovak@fzu.cz</p> <p>Date of data collection: 1. 2. 2023 - 31. 7. 2024</p> <p>All the data showed in the pictures are provided in X-Y format with described sample. Always, the respective figure to which the data belong is provided in high resolution. <br>The data are in the following formats: <br>Scheme 1: pdf<br>Figure 1: pdf<br>Figure 2: pdf<br>Figure 3: pdf, csv<br>Figure 4: pdf, csv<br>Figure 5: pdf, csv<br>Figure 6: pdf, csv<br>Figure 7: pdf, csv<br>Figure S1: pdf, csv<br>Figure S2: pdf, csv</p> <p>The comma separated values file (csv) always contain the description of the columns in the first row. In case of composed image the name of the file corresponds to the corresponding figure.</p> <p>Data acquistion and processing is provided in the Experimental part in the publication: DOI: 10.1021/acsanm.4c04676</p>
Weekly supervised Multilingual Data Set to train Named Entity Recognition for Symptom Extraction
<p>Data Sets were generated using the Weakly Supervised NER pipeline (https://github.com/HUMADEX/Weekly-Supervised-NER-pipline) to train the symptom extraction NER models. </p> <p><strong>Supported Languages and dataset locations for the specific language:</strong></p> <p> English (base language): https://huggingface.co/HUMADEX/english_medical_ner<br> German: https://huggingface.co/HUMADEX/german_medical_ner<br> Italian: https://huggingface.co/HUMADEX/italian_medical_ner<br> Spanish: https://huggingface.co/HUMADEX/spanish_medical_ner<br> Greek: https://huggingface.co/HUMADEX/german_medical_ner<br> Slovenian: https://huggingface.co/HUMADEX/slovenian_medical_ner<br> Polish: https://huggingface.co/HUMADEX/polish_medical_ner<br> Portuguese: https://huggingface.co/HUMADEX/portugese_medical_ner</p> <p> </p> <p><strong>Dataset Building </strong></p> <ul> <li>Data Integration and Preprocessing</li> <li>Data Cleaning</li> <li>Annotation with Stanza's i2b2 Clinical Model </li> <li>Translation into the targeted language</li> <li>Word Alignment </li> <li>Data Augmentation </li> </ul> <p><strong>Acknowledgement</strong><br>This dataset had been created as part of joint research of HUMADEX research group (https://www.linkedin.com/company/101563689/) and has received funding by the European Union Horizon Europe Research and Innovation Program project SMILE (grant number 101080923) and Marie Skłodowska-Curie Actions (MSCA) Doctoral Networks, project BosomShield ((rant number 101073222). Responsibility for the information and views expressed herein lies entirely with the authors.</p> <p><strong>Authors:</strong><br>dr. Izidor Mlakar, Rigona Sallauka, dr. Umut Arioz, dr. Matej Rojc</p> <p><strong>Please cite as:</strong></p> <p><span>Article title: Weakly-Supervised Multilingual Medical NER For Symptom Extraction For Low-Resource Languages</span><br><span>Doi: 10.20944/preprints202504.1356.v1</span><br><span>Website: </span><a title="https://www.preprints.org/manuscript/202504.1356/v1" href="https://www.preprints.org/manuscript/202504.1356/v1">https://www.preprints.org/manuscript/202504.1356/v1</a></p>
General Maps -- not in sets
<p>The Survey of India produced many single maps that did not fall into large sets. This section includes an assortment of such maps, organized in chronological order, but not organized by subject. The subject of each map is revealed by the title. When possible, the creator of the map is also given in the file name.</p>
IG receptor germline set for species: Human set_name: IGLambda_VJ
<p>Germline Reference set published on the Open Germline Receptor Database (OGRDB)</p>
IG receptor germline set for species: Human set_name: IGKappa_VJ
<p>Germline Reference set published on the Open Germline Receptor Database (OGRDB)</p>
IG receptor germline set for species: Human set_name: IGH_VDJ
<p>Germline Reference set published on the Open Germline Receptor Database (OGRDB)</p>
IG receptor germline set for species: Mouse subgroup: C57BL/6 set_name: C57BL/6 IGH
<p>Germline Reference set published on the Open Germline Receptor Database (OGRDB)</p>
IG receptor germline set for species: Mouse subgroup: PWD/PhJ set_name: PWD/PhJ IGH
<p>Germline Reference set published on the Open Germline Receptor Database (OGRDB)</p>
CO2 fluxes measurement data set for the lower segment of the River Noce, Italy
<p>This data set reports on data recorded during four measurement campaigns in the lower segment of the River Noce, Italy.<br>The measurements were aimed at characterising CO2 fluxes along the river, and were conducted using a combination of loggers (time-resolved data) and sampling (space-resolved data).<br>The data were collected in the Santa Giustina reservoir (upstream reservoir), along the residual flow reaches that extend between the Santa Giustina reservoir and the outlet of the hydropower diversion system in Mezzocorona, and downstream of the diversion outlet.</p> <p>The data is provided as .csv and Matlab .mat files.<br>For more information about the data set and data acquisition, please contact the author or refer to the following article:</p> <p><br>Dolcetti, G., Piccolroaz, S., Bruno, M. C., Calamita, E., Larsen, S., Zolezzi, G. & Siviglia, A. Quantification of carbopeaking and CO2 fluxes in a regulated Alpine river.</p>
Mastodon example data set - 10 tracked phallusia mammillata embryos
<ul> <li>This dataset originates from the Phallusia mammillata embryonic development dataset, which was published on figshare. <ul> <li><a href="https://figshare.com/projects/Phallusiamammillata_embryonic_development/64301" rel="nofollow">https://figshare.com/projects/Phallusiamammillata_embryonic_development/64301</a>.</li> </ul> </li> <li>The dataset is described in the following publication: <ul> <li>Léo Guignard et al., Contact area–dependent cell communication and the morphological invariance of ascidian embryogenesis. Science369, eaar5663(2020). DOI:<a href="https://doi.org/10.1126/science.aar5663" rel="nofollow">10.1126/science.aar5663</a></li> </ul> </li> <li>The dataset has been converted to Mastodon file format using these scripts: <ul> <li>Astec file format to CSV: <a href="https://github.com/GuignardLab/LineageTree/blob/master/src/LineageTree/legacy/export_csv.py">https://github.com/GuignardLab/LineageTree/blob/master/src/LineageTree/legacy/export_csv.py</a></li> <li>CSV to Mastodon file format: <a href="https://github.com/mastodon-sc/mastodon-deep-lineage/blob/master/src/test/java/org/mastodon/mamut/astec/AstecReader.java">https://github.com/mastodon-sc/mastodon-deep-lineage/blob/master/src/test/java/org/mastodon/mamut/astec/AstecReader.java</a></li> </ul> </li> <li>Limitations resulting from the conversion: <ul> <li>Image data is not included in the projects. This would require additional effort. However, it is probably not necessary for the intended use cases (tests of lineage clustering, lineage registration, and Blender visualization).</li> <li>Only datasets Pm01 – Pm10 have been converted.</li> <li>The cell names were missing for Pm11-Pm13. To handle this, the conversion scripts would need to be adapted. Thus, these datasets are not included.</li> <li>The uploaded data for Pm14 is faulty on figshare. Thus it is not included.</li> <li>Half-Pm1, U0126PM1 and U0126PM2 do not contain lineages or coordinates. Thus, these datasets are not included.</li> <li>When opening the Mastodon projects, a message appears stating that the image data is missing. It can be chosen, to open the project without image data ("dummy image data"). This must be confirmed. After that, it should work.</li> </ul> </li> </ul>
Cubic insulin data set collected from multiple lattices on i03 at Diamond Light Source
<p>Dataset collected as part of routine commissioning work, found to have more than one crystal present at the point where data were collected, allowing multiple lattices to be processed. </p> <p> </p> <p>While three lattices are present one is substantially weaker than the other two.</p> <p> </p> <p>Uploading to enable methods development and also to use for tutorials on how to use dials software.</p> <p> </p> <p>Processing data with the usual dials scripts (which will point to this deposition) result in statistics shown below. Tutorial to be uploaded to https://github.com/graeme-winter/dials_tutorials when available.</p> <p> </p> <p><code> -------------Summary of merging statistics-------------- </code></p> <p><code> Suggested Low High Overall</code><br><code>High resolution limit 1.51 4.10 1.51 1.48</code><br><code>Low resolution limit 54.89 54.93 1.54 54.89</code><br><code>Completeness 98.9 100.0 83.7 95.3</code><br><code>Multiplicity 54.7 78.1 4.6 53.5</code><br><code>I/sigma 18.9 89.2 0.3 18.4</code><br><code>Rmerge(I) 0.139 0.060 1.423 0.139</code><br><code>Rmerge(I+/-) 0.138 0.060 1.316 0.138</code><br><code>Rmeas(I) 0.140 0.061 1.590 0.140</code><br><code>Rmeas(I+/-) 0.140 0.060 1.609 0.140</code><br><code>Rpim(I) 0.016 0.007 0.683 0.016</code><br><code>Rpim(I+/-) 0.023 0.009 0.894 0.023</code><br><code>CC half 1.000 1.000 0.271 1.000</code><br><code>Anomalous completeness 97.8 100.0 67.8 92.3</code><br><code>Anomalous multiplicity 28.7 43.6 2.6 28.3</code><br><code>Anomalous correlation 0.038 0.276 -0.050 0.049</code><br><code>Anomalous slope 0.667 </code><br><code>dF/F 0.060 </code><br><code>dI/s(dI) 0.629 </code><br><code>Total observations 669615 52255 2342 670277</code><br><code>Total unique 12231 669 507 12529</code></p>
Data set of study Examining the impact of cognitive control and discrimination on mental health outcomes in diverse Pakistani and Afghan communities
<div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <p>This dataset is part of a study examining the impact of cognitive control and discrimination on mental health outcomes among diverse Pakistani and Afghan communities. It includes demographic variables such as age, gender, and ethnicity, as well as psychological measures assessing cognitive control, discrimination experiences, and various mental health outcomes.</p> </div> </div> </div> </div> <div> <div> <div> <div> </div> <div><span>4o</span></div> <span></span></div> </div> </div> <div> </div> <div> <div> </div> </div> </div> <div> <div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> </div> <div> <div> </div> </div> <div> <div> <div> <div> <div> <div> <div> <div> </div> </div> </div> </div> </div> <div> <div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
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.