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1,970 results for “concepts”
Data set associated to the publication "An active source seismo-acoustic experiment using tethered balloons to validate instrument concepts and modelling tools for atmospheric seismology"
<p>Data set of the scientific publication entitled "An active source seismo-acoustic experiment using tethered balloons to validate instrument concepts and modelling tools for atmospheric seismology":</p> <p>Seismological sensors</p> <p>Microphones</p> <p>Barometers</p> <p>Accelerometers</p> <p>Detailed test report.</p>
Data from: Telomere heritability and parental age at conception effects in a wild avian population
<p>Individual variation in telomere length is predictive of health and mortality risk across a range of species. However, the relative influence of environmental and genetic variation on individual telomere length in wild populations remains poorly understood. Heritability of telomere length has primarily been calculated using parent–offspring regression which can be confounded by shared environments. To control for confounding variables, quantitative genetic 'animal models' can be used, but few studies have applied animal models in wild populations. Furthermore, parental age at conception may also influence offspring telomere length, but most studies have been cross-sectional. We investigated within- and between- parental age at conception effects and heritability of telomere length in the Seychelles warbler using measures from birds caught over 20 years and a multi-generational pedigree. We found a weak negative within-paternal age at conception effect (as fathers aged, their offspring had shorter telomeres) and a weak positive between-maternal age at conception effect (females that survived to older ages had offspring with longer telomeres). Animal models provided evidence that heritability and evolvability of telomere length was low in this population, and that variation in telomere length was not driven by early-life effects of hatch period or parental identities. qPCR plate had a large influence on telomere length variation and not accounting for it in the models would have underestimated heritability. Our study illustrates the need to include and account for technical variation in order to accurately estimate heritability, as well as other environmental effects, on telomere length in natural populations. </p>
Data: Evolution of anatomical concept usage over time: Mining 200 years of biodiversity literature
<p>This data package contains data and results corresponding to the paper titled "Evolution of anatomical concept usage over time: Mining 200 years of biodiversity literature". </p>
Encyclopedia of Life v2: Taxon Hierarchies and Associated Taxon Concepts
<p>This archive contains a snapshot of the taxon hierarchies, and associated scientific name strings and image thumbnails, used by the Encyclopedia of Life v2 (Parr et al. 2014, http://eol.org). See https://github.com/jhpoelen/eol-globi-data/issues/274 and https://github.com/EOL/tramea/issues/366 for discussion threads. Taxon hierarchy providers include, but are not limited to, Integrated Taxonomic Information System (ITIS, http://itis.gov) and World Register of Marine Species (WoRMS, http://marinespecies.org).</p>
Concept detection scores for the IACC.3 dataset (TRECVID AVS Task)
<p>We provide concept detection scores for the IACC.3 dataset (600 hr internet archive videos), which is used in the TRECVID Ad-hoc Video Search (AVS) task [1]. Concept detection scores for 1345 concepts (1000 ImageNet concepts provided for the ILSVRC challenge [2] and 345 TRECVID SIN concepts [3]) have been generated as follows:<br> 1) To generate scores for the ImageNet concepts, 5 pre-trained ImageNet networks were applied on the IACC.3 dataset and their output was fused in terms of arithmetic mean.<br> 2) To generate scores for the TRECVID SIN concepts, two pre-trained ImageNet networks were fine-tuned on these concepts using a combination of our methods presented in the following papers: [4], [5]. We provide two different sets of concept scores for the TRECVID SIN concepts: a) The output of the two fine-tuned networks was fused in terms of arithmetic mean in order to return a single score for each concept. b) The last fully-connected layer was used as feature to train SVM classifiers separately for each fine-tuned network and each concept. Then, the SVM classifiers were applied on the IACC.3 dataset and the prediction scores of the SVMs for the same concept were fused in terms of arithmetic mean in order to return a single score for each concept. We evaluated the two different sets of concepts in terms of MXInfAP on a subset of 38 TRECVID SIN concepts for which ground-truth annotation exists, and the MXInfAP of each set of concept scores is: a) 30.04% for the networks' direct output, b) 35.81% for the SVM classifiers.</p> <p>Three different files of concept detection scores can be downloaded (after unpacking the compressed file):<br> 1) scores_ImageNet.txt<br> 2a) scores_SIN_direct.txt<br> 2b) scores_SIN_svm.txt<br> In total there are 335944 rows in each file; 1002 columns in the first file and 347 columns in each of the other two. Each row in any of these files corresponds to a different video shot; the video shot IDs appear in the first two columns. (Note: the shot IDs are the ones from the mp7 files in the TRECVID AVS master shot reference, with the format shotFILENUMBER_SHOTNUMBER). Then, each column (except for the fist two) corresponds to a different concept, with all concept scores being in [0,1] range. The higher the score the more likely that the corresponding concept appears in the video shot. Files “concept_names_ImageNet.txt” and “concept_names_SIN.txt” indicate the order of the concepts that is used in the concept score files. </p> <p>[1] G. Awad, J. Fiscus, M. Michel et al. 2016. TRECVID 2016: Evaluating Video Search, Video Event Detection, Localization, and Hyperlinking. In TRECVID 2016 Workshop. NIST, USA.<br> [2] O. Russakovsky, J. Deng, H. Su et al. 2015. ImageNet Large Scale Visual Recognition Challenge. Int. Journal of Computer Vision (IJCV) 115, 211–252.<br> [3] G. Awad, C. Snoek, A. Smeaton, and G. Quénot. 2016. TRECVid semantic indexing of video: a 6-year retrospective. ITE Transactions on Media Technology and Applications, 4 (3). pp. 187-208.<br> [4] N. Pittaras, F. Markatopoulou, V. Mezaris, I. Patras. 2017. Comparison of Fine-tuning and Extension Strategies for Deep Convolutional Neural Networks, Proc. 23rd Int. Conf. on MultiMedia Modeling (MMM'17), Reykjavik, Iceland, Springer LNCS vol. 10132, pp. 102-114, Jan. 2017.<br> [5] F. Markatopoulou, V. Mezaris, and I. Patras. 2016. Deep Multi-task Learning with Label Correlation Constraint for Video Concept Detection, Proc. ACM Multimedia 2016, Amsterdam, Oct. 2016.</p>
Concept detection scores for the MED16train dataset (TRECVID MED task)
<p>We provide concept detection scores for the MED16train dataset which is used at the TRECVID Multimedia Event Detection (MED) task [1]. First, each video is decoded into a set of keyframes at fixed temporal intervals (2 keyframes per second). Then, we calculated concept detection scores for the two following concept sets: i) 487 sport-related concepts from YouTube Sports-1M Dataset[1] and ii) 345 TRECVID SIN concepts [3]. The scores have been generated as follows:<br> 1) For the 487 concepts for the Sports-1M Dataset, a Googlenet network [4] originally trained on 5055 ImageNet concepts was fine-tuned, following the extension strategy of [2] with one extension layer of dimension 128.<br> 2) For the 345 TRECVID SIN concepts, a pre-trained Googlenet network [4] on 5055 ImageNet concepts was fine-tuned on these concepts, again following the extension strategy of [2] with one extension layer of dimension 1024. </p> <p>After unpacking the compressed file two different folders can be found, namely "Prob_sports_MED16train" and "Prob_SIN_MED16train", one for each concept set. We provide one file for every video of the MED16train dataset for each concept set. Each file consists of N columns (where N = 345 for TRECVID SIN and N = 487 for Sports-1M Dataset) and M rows (where M is the number of extracted keyframes for the corresponding video). Each column corresponds to a different concept, with all concept scores being in the range [0,1]. The higher the score the more likely that the corresponding concept appears in the keyframe. Two additional files are provided; files "sports_487_Classes.txt" and "SIN_345_Classes.txt" indicate the order of the concepts that is used in the concept score files.</p> <p>[1] A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar and L. Fei-Fei, "Large-scale video classification with convolutional neural networks", In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 1725-1732, 2014.<br> [2] N. Pittaras, F. Markatopoulou, V. Mezaris and I. Patras, "Comparison of Fine-tuning and Extension Strategies for Deep Convolutional Neural Networks", Proc. 23rd Int. Conf. on MultiMedia Modeling (MMM'17), Reykjavik, Iceland, Springer LNCS vol. 10132, pp. 102-114, Jan. 2017.<br> [3] G. Awad, C. Snoek, A. Smeaton, and G. Quénot, "TRECVid semantic indexing of video: a 6-year retrospective", ITE Transactions on Media Technology and Applications, 4 (3). pp. 187-208, 2016.<br> [4] C. Szegedy, Wei Liu, Yangqing Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke and A. Rabinovich, "Going deeper with convolutions", In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-9, 2015.</p>
Comparative Analysis of Anthraquinone and Chalcone Derivatives-Based Virtual Combinatorial Library. A Cheminformatics "Proof-of-Concept" Study
<p>This computational “proof-of-concept” study illustrated the combinatorial approach used to explain how the selected natural products' structures undergo molecular diversity analysis. A virtual combinatorial library (1.6M) based on 20 anthraquinones and 24 chalcones were enumerated. The resulting compounds were optimized to the near drug-likeness properties and the physicochemical descriptors were calculated for all datasets including FDA, Non-FDA, and natural products (NPs) datasets from ZINC 15. UMAP and principal component analysis (PCA) were applied to compare and represent the chemical space coverage of each dataset. Subsequently, the Laplacian score, and Gini coefficient, were applied to delineate feature selection, and selectivity among properties respectively. Finally, we demonstrated the diversity between the datasets by employing Murcko’s, and central scaffolds systems, calculated three fingerprint descriptors, and analyzed their diversity by PCA and self-organizing maps (SOM). The optimized enumeration resulted in 1,610,268 compounds with NP-Likeness, and synthetic feasibility mean scores close to FDA, Non-FDA, and NPs datasets. The overlap between the chemical space of 1.6M was more prominent with NPs. Laplacian score has prioritized NP-likeness and hydrogen bond acceptor properties (1.0 and 0.923) respectively, while the Gini coefficient showed that all properties have selective effects on datasets (0.81 to 0.93). Scaffold and fingerprint diversity indicated that the descending order for the tested datasets was FDA, Non-FDA, NPs, 1.6M. Virtual combinatorial libraries based on NPs can be considered as a source of the combinatorial compound with NP-likeness properties. Furthermore, measuring molecular diversity is supposed to be performed by different methods to allow for comparison and better judgment. </p> <p>This link provides an illustration of the whole virtual combinatorial library using the TMAP algorithm in addition to the complete dataset. TMAP is a recent algorithm applied to visualize ultra-large high-dimensional chemical libraries for structures and physicochemical properties (Probst & Reymond, 2020). This approach creates and distributes intuitive tree representations of big data sets with arbitrary dimensionality in the order of 10<sup>7</sup>.</p> <p><strong>To visualize the whole library of compounds, download the "index(2).rar", then extract the index.html that pop-up in the WinRAR application.</strong></p>
The FAIR-Device - a non-lethal and generalist semi-automatic Malaise trap for insect biodiversity monitoring: Proof of concept - Supplementary Material
<h3>Abstract</h3> <p>Field monitoring plays a crucial role in understanding insect dynamics within ecosystems. It facilitates pest distribution assessment, control measure evaluation, and prediction of pest outbreaks. Additionally, it provides important information on bioindicators with which the state of biodiversity and ecological integrity in specific habitats and ecosystems can be accurately assessed. However, traditional monitoring systems can present various difficulties, leading to a limited temporal and spatial resolution of the obtained information. Despite recent advancements in automatic insect monitoring traps, also called e-traps, most of these systems focus exclusively on studying agricultural pests, rendering them unsuitable for monitoring diverse insect populations. To address this issue, we introduce the Field Automatic Insect Recognition (FAIR)-Device, a novel non-lethal field tool that relies on semi-automatic image capture and species identification using artificial intelligence via the iNaturalist platform. Our objective was to develop an automatic, cost-effective, and non-specific monitoring solution capable of providing high-resolution data for assessing insect diversity. During a 26-day proof-of-concept evaluation, the FAIR-Device recorded 24.8 GB of video, identifying 431 individuals from 9 orders, 50 families, and 69 genera. While improvements are possible, our device demonstrated potential as a cost-effective, non-lethal tool for monitoring insect biodiversity. Looking ahead, we envision new monitoring systems such as e-traps as valuable tools for real-time insect monitoring, offering unprecedented insights for ecological research and agricultural practices.</p> <h3>Description of the data and file structure</h3> <p>This repository complements the publication <a href="https://www.biorxiv.org/content/10.1101/2024.03.22.586299v2" target="_blank" rel="noopener">"The FAIR-Device - a non-lethal and generalist semi-automatic Malaise trap for insect biodiversity monitoring: Proof of concept".</a> It contains the result data from the proof of concept field test of V1.0 of the FAIR-Device, conducted between July and August 2021 at the Thünen Institute of Agricultural Technology in Braunschweig. The repository comprises three compressed files (.zip):</p> <ul> <li><strong>FAIR-D_captures.zip</strong>: <ul> <li>Video captures from the field tests organized by recording day.</li> <li>Filenames indicating recording time (hh-mm-ss).</li> </ul> </li> </ul> <ul> <li><strong>FAIR-D_Tables&Code.zip</strong>: <ul> <li>Processed results from the obtained image captures, organized into: <ul> <li><strong>Monitoring2021_TotalPeriod.xlsx: </strong>Result table with taxonomic classifications, data analysis, and charts.</li> <li><strong>iNat_observations.xlsx: </strong>iNaturalist reviews analysis table.</li> <li><strong>R: </strong>code for generating article graphics.</li> </ul> </li> </ul> </li> </ul> <ul> <li><strong>FAIR-D_V1.0_3D_Models.zip</strong>: <ul> <li>Complete 3D design of the FAIR-D V1.0 in .stl format, organized into: <ul> <li><strong>3D_print_parts</strong>: 3D-printable parts with spatial coordinates for correct positioning in CAD software.</li> <li><strong>other_3Dparts</strong>: Non-printable parts, also in .stl format with spatial coordinates.</li> </ul> </li> </ul> </li> </ul> <p> </p> <p>NOTE: For visualizing the videos, we recommend <strong>VLC media player</strong> - <a href="https://www.videolan.org/vlc/">https://www.videolan.org/vlc/</a> </p>
Ontology Enrichment from Texts (OET): A Biomedical Dataset for Concept Discovery and Placement
<p>A biomedical dataset supporting ontology enrichment from texts, by concept discovery and placement, adapting the MedMentions dataset (PubMed abstracts) with SNOMED CT of versions in 2014 and 2017 under the Diseases (disorder) sub-category and the broader categories of Clinical finding, Procedure, and Pharmaceutical / biologic (CPP) product.</p> <p>The dataset is documented in the work, <em>Ontology Enrichment from Texts: A Biomedical Dataset for Concept Discovery and Placement</em>, on arXiv: <a href="https://arxiv.org/abs/2306.14704">https://arxiv.org/abs/2306.14704</a> (CIKM 2023). The companion code is available at https://github.com/KRR-Oxford/OET.</p> <p>Out-of-KB mention discovery (including the settings of mention-level data) is further partly documented in the work, <em>Reveal the Unknown: Out-of-Knowledge-Base Mention Discovery with Entity Linking</em>, on arXiv: <a href="https://arxiv.org/abs/2302.07189">https://arxiv.org/abs/2302.07189</a> (CIKM 2023).</p> <p>ver4: we made a version of mention-level data for out-of-KB discovery and concept placement separately: the former (for out-of-KB discovery) has out-of-KB mentions in training data, while the latter (for concept placement) has only out-of-KB mentions during the evaluation (validation and test) and not in the training data. Also, we split the original "test-NIL.jsonl" (now "test-NIL-all.jsonl") into "valid-NIL.jsonl" and "test-NIL.jsonl" for a better evaluation.</p> <p>ver3: we revised and updated mention-level data (syn_full, synonym augmentation setting) and the folder structure, and also updated the edge catalogues with complex edges.</p> <p>ver2: we revised the mention-level data by only keeping out-of-KB mentions (or "NIL" mentions) associated with one-hop edges (including leaf nodes, as <leaf node, NULL>) and two-hop edges in the ontology (SNOMED CT 20140901).</p> <p>Acknowledgement of data sources and tools below:</p> <p>* SNOMED CT https://www.nlm.nih.gov/healthit/snomedct/archive.html (and use snomed-owl-toolkit to form .owl files)<br>* UMLS https://www.nlm.nih.gov/research/umls/licensedcontent/umlsarchives04.html (and mainly use MRCONSO for mapping UMLS to SNOMED CT)<br>* MedMentions https://github.com/chanzuckerberg/MedMentions (source of entity linking)</p> <p>* Protégé http://protegeproject.github.io/protege/<br>* snomed-owl-toolkit https://github.com/IHTSDO/snomed-owl-toolkit<br>* DeepOnto https://github.com/KRR-Oxford/DeepOnto (based on OWLAPI https://owlapi.sourceforge.net/) for ontology processing and complex concept verbalisation</p>
Supplementary Data for ZeroPM D2.3 - Lessons learnt from previous applications of the essential-use concept
<p>This dataset contains the data collected for all the studies which are mentioned in the ZeroPM deliverable D2.3 titled "Lessons learnt from previous applications of the essential-use concept".</p>
Resilience and related concepts in a manufacturing system
<p>Those drawings stress the definition of the resilience of a manufacturing system. This concept is positioned regarding related concepts: robustness, flexibility and rapidity. An English and a French versions are provided.</p>
juan-duenas/NHESS: Soil conditioner mixtures as an agricultural management alternative to mitigate drought impacts: a proof-of-concept.
<p>The dataset and the R script have been enhanced and corrected, respectively. The main figures of the associated publication have been added in two different qualities.</p> <p>This data is associated to a paper that will appear in an special issue of the journal Natural Hazards and Earth System Sciences. https://nhess.copernicus.org/articles/special_issue1295.html</p>
Joint Communication and Sensing: a Proof of Concept and Datasets for Greenhouse Monitoring using LoRaWAN
<p>The goal of these LoRaWAN based greenhouse monitoring datasets, is to provide the global research community with a benchmark tool to evaluate different techniques for precision agriculture in large greenhouse environments. An identical collection methodology was used for both of the two datasets over the same tomato crop: during a period of five months, respectively. Together with temperature and humidity values, network information such as receiving time of the message and Received Signal Strength Indicator (RSSI) were stored in the greenhouse monitoring datasets:</p> <ul> <li><strong>Greenhouse-1.csv</strong> <ul> <li>Data from 27 sensors denoted as AF 16-42 with an average of 19687 LoRaWAN messages per sensor from April till August 2020, obtained in the greenhouse for tomato crop in Belgium.</li> </ul> </li> <li><strong>Greenhouse-2.csv</strong> <ul> <li>Data from 19 sensors denoted as AF 49-67 with an average of 19009 LoRaWAN messages per sensor from July till November 2020, obtained in the other greenhouse for tomato crop in the Netherlands.</li> </ul> </li> <li><strong>Greenhouse-1-Transformed-Data.csv</strong> <ul> <li>Mean temperature, humidity, and RSSI values along with plant height for the same period.</li> </ul> </li> </ul> <p>Both the greenhouses, had no LoRaWAN connectivity, so individual gateway were installed for both locations. For Greenhouse-1 data, sensors were switched on in a room on 10<sup>th</sup> of April and brought to the greenhouse chamber on 17<sup>th</sup> April at 06:38 am for sensing. It would be crucial to accordingly use data set, considering the above time period.</p> <p>The collection methodology of datasets, and first results of a joint communication and sensing proof-of-concept are documented in the journal paper : https://www.mdpi.com/1424-8220/22/4/1326.</p> <p> </p>
Concept maps as a novel assessment tool in medical education
<p>This dataset accompanies the manuscript entitled "Concept maps as a novel assessment tool in medical education". The dataset contains data from eight participants in a pilot study investigating the use of Concept Maps (CMs) in medical education. Each participant constructed one CM for three different Problem-Based Learning (PBL) cases. Participant CMs, demographic data, results from pre-intervention questionnaire on their learning style (VARK Learning Styles Self-Assessment Questionnaire) and post-intervention questionnaire results measuring the level of clinical and critical thinking are included. In addition expert CMs are also provided for each of the three PBL cases.</p> <p> </p> <p><br> <br> <br> </p>
Home-based measurements of dystonia and choreoathetosis in cerebral palsy using smartphone-coupled inertial sensor technology and machine learning: A proof-of-concept study - dataset
<p>Home-based measurements of dystonia in cerebral palsy using smartphone-coupled inertial sensor technology and machine learning: A proof-of-concept study</p> <p> </p> <p>This project contains:</p> <p>- 1 main MATLAB script: MODYSathome_main.m<br> - 12 MATLAB functions:<br> - function_calc_mean_recall_precision.m<br> - function_create_dataframes.m<br> - function_deep_learning.m<br> - function_determine_best_ML_model.m<br> - function_display_DL_results.m<br> - function_display_ML_results.m<br> - function_index_extremities.m<br> - function_machine_learning.m<br> - function_oversample.m<br> - function_partition_data.m<br> - function_pick_best_models.m<br> - function_prepare_DL_data.m</p> <p>Downloading the Matlab scripts</p> <p> - Create a folder named 'MODYS' and create a subfolder named 'results'<br> - Download the zip file via <a href="https://zenodo.org/record/6379348">RehabAUmc/modys-at-home: v1.0 | Zenodo</a><br> - Unzip the zip file in the path MODYS\</p> <p>STEPS<br> 1. Open MATLAB<br> 2. In MATLAB, go to the 'HOME' tab and click on 'Set Path'<br> 3. Click on 'Add Folder' and browse to MODYS/RehabAUmc-modys-at-home-86b14c3/functions<br> 4. Click on 'Select Folder' and click on 'Save'<br> 5. Click on 'Browse to folder' and browse to a patients' data in MODYS/data/PatientXXX, then click on 'Select Folder'<br> 6. In the 'HOME' tab click on 'Open' and open MODYSathome.m in MODYS/RehabAUmc-modys-at-home-86b14c3<br> 7. In the 'EDITOR' tab click on 'Run Section' to run the script<br> 8. When the code has been run, the results are displayed in the Command Window and saved in MODYS/results/PatientXXX</p>
A list of body and object concepts
<p>The list includes 784 human body parts and object concepts. The concepts are based on the available concept sets in <a href="https://concepticon.clld.org/">Concepticon</a> (List et al. <a href="https://aclanthology.org/L16-1379/">2016</a>, <a href="https://doi.org/10.5281/zenodo.596412">2021</a>) and were tagged as <em>human body part,</em> <em>animal</em>, <em>clothing</em>, <em>food</em>, <em>household</em> <em>items</em>, <em>instrument</em>, <em>landscape</em>, <em>plant</em>, <em>spatial</em> <em>relation</em>, <em>tool</em>, and <em>vehicle</em>.</p>
Data Instances for: Who moves the locker? A benchmark study of alternative mobile parcel locker concepts
<p>|C|_h.txt</p> <p>|C|: number of customers<br> h: instance</p> <p>|C|;|P|;</p> <p>|C|: number of customers<br> |P|: number of parking spaces</p> <p>Customer (c;size;max_dist;min_time;L;x_1;y_1;...;x_L;y_L;s_1;e_1;...;s_L;e_L)</p> <p>c: customer index <br> size: parcel size<br> max_dist: maximum walking distance<br> min_time: minimum overlap time<br> L: number of whereabouts<br> (x_i,y_i): position of whereabouts i<br> [s_i,e_i]: time window of whereabouts i</p> <p>Parking space (p;x;y)<br> p: parking space index <br> (x,y): position</p>
ClinSpEn-OC (Ontology Concepts) Test + Background Set
<p>This repository contains the test and background data for the ClinSpEn-Ontology Concepts sub-track. ClinSpEn is part of the Biomedical WMT 2022 shared task, having the aim to promote the development and evaluation of machine translation systems adapted to the medical domain with three highly relevant sub-tracks: clinical cases, medical controlled vocabularies/ontologies, and clinical terms and entities extracted from medical content.</p> <p>The data is made up of a TSV file with two columns: concept number and English concept. The direction of this sub-track is EN>ES. Ontologies and structured vocabularies represent a key resource for semantic interoperability, entity linking, biomedical knowledge bases and precision medicine, and thus there is a pressing need to generate multilingual biomedical ontologies for a range of clinical applications</p> <p> </p> <p>Related Links:</p> <p><strong>- Data website with more information: </strong><a href="https://temu.bsc.es/clinspen/">https://temu.bsc.es/clinspen/</a></p> <p><strong>- WMT website (includes schedule, registration, ...): </strong><a href="https://www.statmt.org/wmt22/">https://www.statmt.org/wmt22/</a></p> <p><strong>- CodaLab: </strong><a href="https://codalab.lisn.upsaclay.fr/competitions/6696">https://codalab.lisn.upsaclay.fr/competitions/6696</a></p> <p> </p> <p>ClinSpEn SAMPLE SETS:</p> <p><strong>- ClinSpEn-CC Sample Set (Clinical Cases):</strong> <a href="https://doi.org/10.5281/zenodo.6497350">https://doi.org/10.5281/zenodo.6497350</a></p> <p><strong>- ClinSpEn-CT Sample Set (Clinical Terms): </strong><a href="https://doi.org/10.5281/zenodo.6497372">https://doi.org/10.5281/zenodo.6497372</a></p> <p><strong>- ClinSpEn-OC Sample Set (Ontology Concepts): </strong><a href="https://doi.org/10.5281/zenodo.6497388">https://doi.org/10.5281/zenodo.6497388</a></p> <p>ClinSpEn TEST SETS:</p> <p><strong>- ClinSpEn-CC Test Set (Clinical Cases): </strong><a href="https://doi.org/10.5281/zenodo.6948634">https://doi.org/10.5281/zenodo.6948634</a></p> <p><strong>- ClinSpEn-CT Test Set (Clinical Terms): </strong><a href="https://doi.org/10.5281/zenodo.6948669">https://doi.org/10.5281/zenodo.6948669</a></p> <p><strong>- ClinSpEn-OC Test Set (Ontology Concepts): </strong><a href="https://doi.org/10.5281/zenodo.6948679">https://doi.org/10.5281/zenodo.6948679</a></p> <p> </p>
DATASET: Using auxiliary electrochemical working electrodes as probe during contact glow discharge electrolysis: A proof of concept study
<p>This project contains all the data shown in the figures of the manuscript (and the supporting information) entitled:<br> 'Using auxiliary electrochemical working electrodes as probe during contact glow discharge electrolysis: A proof of concept study'<br> (doi:10.26434/chemrxiv-2022-0v5sc).</p> <p>The data to each figure is provided in a subfolder where each curve is stored as a single CSV.<br> The filenames contain labels describing the curves.</p>
Feasibility and acceptability of personalized breast cancer screening (DECIDO Study): A single-arm proof-of-concept trial
<p>The aim of this study was to assess the acceptability and feasibility of offering risk-based breast cancer screening and its integration into regular clinical practice. A single-arm proof-of-concept trial was conducted with a sample of 387 women aged 40–50 years residing in the city of Lleida (Spain). The study intervention consisted of breast cancer risk estimation, risk communication and screening recommendations, and a follow-up. A polygenic risk score with 83 single nucleotide polymorphisms was used to update the Breast Cancer Surveillance Consortium risk model and estimate the 5-year absolute risk of breast cancer. The women expressed a positive attitude towards varying the frequency of breast screening according to individual risk and, especially, more frequently inviting women at higher-than-average risk. A lower intensity screening for women at lower risk was not as welcome, although half of the participants would accept it. Knowledge of the benefits and harms of breast screening was low, especially with regard to false positives and overdiagnosis. The women expressed a high understanding of individual risk and screening recommendations. The participants' intention to participate in risk-based screening and satisfaction at 1-year were very high.</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.