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7,378 results for “case”
Data for: Inadequate sampling of the soundscape leads to overoptimistic estimates of recogniser performance: A case study of two sympatric macaw species
<p><span></span></p> <p>Passive acoustic monitoring (PAM) offers the potential to dramatically increase the scale and robustness of species monitoring in rainforest ecosystems. PAM generates large volumes of data that require automated methods of target species detection. Species-specific recognisers, which often use supervised machine learning, can achieve this goal. However, they require a large training dataset of both target and non-target signals, which is time-consuming and challenging to create. Unfortunately, very little information about creating training datasets for supervised machine learning recognisers is available, especially for tropical ecosystems. Here we show an iterative approach to creating a training dataset that improved recogniser precision from 0.12 to 0.55. By sampling background noise using an initial small recogniser, we addressed one of the significant challenges of training dataset creation in acoustically diverse environments. Our work demonstrates that recognisers will likely fail in real-world settings unless the training dataset size is large enough and sufficiently representative of the ambient soundscape. We outline a simple workflow that can provide users with an accessible way to create a species-specific PAM recogniser that addresses these issues for tropical rainforest environments. Our work provides important lessons for PAM practitioners wanting to develop species-specific recognisers for acoustically diverse ecosystems.</p>
Relative role of border restrictions, case finding and contact tracing in controlling SARS-CoV-2 in the presence of undetected transmission: a mathematical modelling study
<p>Data and code for publication on <em>Relative role of border restrictions, case finding and contact tracing in controlling SARS-CoV-2 in the presence of undetected transmission: a mathematical modelling study</em></p>
Reproduction package for the paper "Prospects of Gravitational Wave Follow-up Through a Wide-field Ultra-violet Satellite: a Dorado Case Study"
<p>This is a basic reproduction package for the paper "Prospects of Gravitational Wave Follow-up Through a Wide-field Ultra-violet Satellite: a Dorado Case Study" by [Dorsman et al. (2022)](https://doi.org/10.48550/arXiv.2206.09696). This package provides (1) data and software to reproduce the results from the Bayesian inference, (2) the pre-computed results from the Bayesian inference for the purpose of reproducing the plots, and (3) the plots.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - ACUTE CASE 2 FOV1
<p><strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (ACUTE CASE 2 FOV1)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - PROLONGED CASE 5 FOV1
<p><strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (PROLONGED CASE 5 FOV1)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - ACUTE CASE 3 FOV1
<p><strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (ACUTE CASE 3 FOV1)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - ACUTE CASE 3 FOV2
<p><strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (ACUTE CASE 3 FOV2)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - ACUTE CASE 2 FOV2
<p><strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (ACUTE CASE 2 FOV2)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Case study of a rapid prototyping method for optimizing soft gripper structures with integrated piezoresistive sensors
<p>Closed-loop control systems and monitoring the activities of soft robots in the natural environment require sensing elements in soft actuator modules. In this study, additive manufacturing is used for sensorized soft actuator modules to investigate the influence of the Shore hardness and design aspects of an open-source tendon-based gripper structure, in a time-efficient way. Additionally, the placement of the piezoresistive sensing element (tension or compression side on the bending soft gripper) was investigated. A user-friendly method, based on thermoplastic material extrusion, has been explored to improve the future design optimization in of active soft robotic structures successfully. A higher Shore hardness resulted in a higher total deflection and a higher force to bend the gripper structure. By increasing the geometrical stiffness of the gripper printed with low Shore hardness, the total deflection was increased, but the force needed to activate the movement was higher in comparison to high Shore hardness and low geometrical stiffness. Moreover, the sensing element on the substrate of higher Shore hardness, leads to low drift, monotonic response, with good sensitivity, independent of the sampling rate. The gripper of higher Shore hardness had a larger functional range, being capable of gripping small and larger objects.</p>
Ressources - Video games as a tool for ecological learning : the case of Animal Crossing - COROLLER & FLINOIS - 2023
<p>The present repository includes : </p> <ol> <li>Survey answers - 200 people, anonymized. Quizz about animals and vegetals that are and are not present in the game ANIMAL CROSSING NEW HORIZONS. Personal questions (age,, gender, location). Self assesment of "Naturalistic Fiber". At the end, question about the video game Animal CROSSING, and then free space</li> <li>Translated R code, based on the present dataset. Runs figures and tests used in our paper "Video games as a tool for ecological learning : the case of "Animal Crossing : New Horizons" during Covid-19 quarantine"</li> <li>The survey itself is available (in french unfortunately) at the following link : <a href="https://forms.gle/GgwULMcg8KnBqDt26">https://forms.gle/GgwULMcg8KnBqDt26</a>,<br> if the link is broken, please don't hesitate to contact me</li> <li>Nintendo Game Content guide at the following link : <a href="https://www.nintendo.co.jp/networkservice_guideline/en/index.html">https://www.nintendo.co.jp/networkservice_guideline/en/index.html</a></li> <li>Appendix (S1 : table of raw datas ; S2 : Normal and QQ plots)</li> <li>Approval of Ethical Research Committee of Université de Sherbrooke (Canada, QC)<br> </li> </ol> <blockquote> <p><em>" [...] furthermore, after reviewing the application for review, no ethical issues were identified by the committee.The committee notes that:The data were collected from a population of individuals who do not a priori present the characteristics of a vulnerable population; The risks associated with participation in the research are minimal; The data collected are anonymous; Individuals have been informed that the data may be used for scientific purposes; However, we remind you that in the future, any research project, as defined in the policy, must be approved by the research ethics committee before proceeding with the collection of data. Therefore, please accept this letter in lieu of an ethics certificate from the </em><em>Research Ethics Board - Education and Social Sciences of the Université de Sherbrooke. This letter may be used when submitting for publication or presentation of the results of this study or for any results of this study or for any other request related to the ethical approval of this project. "</em></p> <p><em>Mme Ariane Tessier<br> Coordonnatrice à l'éthique de la recherche - Université de Sherbrooke, CA QC</em></p> </blockquote> <p>If you have any problem with the present ressources, or if you want to work and publish works containing these datas, please contact me at : <br> simoncoroller.biologie@gmail.com CC : Simon.Coroller@usherbrooke.ca</p> <p>I would gladly discuss with you ! </p> <p>Best wishes.<br> <br> COROLLER & FLINOIS</p> <p> </p>
Shifting environmental predictors of phenotypes under climate change: A case study of growth in high latitude seabirds
<p>Climate change is altering species' traits across the globe. To predict future trait changes and understand the consequences of those changes, we need to know the environmental drivers of phenotypic change. In the present study, we use multi-decadal long datasets to determine periods of within-year environmental variation that predict growth of three seabird species. We evaluate whether these periods changed over time and use them to predict future growth under climate change. We find that predictions of trait change could be improved by considering that 1) the timing of environmental factors used to predict traits (predictive-environmental features) can change over time, and 2) the type of predictive-environmental features can change over time. We find evidence of changes in the timing of environmental predictors in all populations studied and evidence for a change in the type of predictor in the studied Arctic murre population. Environmental models of growth predict that warming conditions will decrease growth rates and bird body sizes in two species (black-legged kittiwakem <em>Rissa</em> <em>tridactyla</em>, and glaucous-winged gullm <em>Larus</em> <em>glaucescens</em>), but not the third (thick-billed murrem <em>Uria</em> <em>lomvia</em>). Consequently, climate change is likely to decrease fledging rates in the gulls and kittiwakes. Further, we find that ice-cover historically predicted murre chick growth well, but no longer does – instead air temperature is now a better predictor of murre growth. Our study highlights a need to investigate whether environmental determinants of trait variation commonly shift in a changing climate and whether such changes have implications for adaptation to novel environments.</p>
DisTEMIST corpus: detection and normalization of disease mentions in spanish clinical cases
<p><strong>DisTEMIST corpus: training set + MULTILINGUAL RESOURCES + CROSSMAPPINGS + test set + background set</strong></p><p> </p><p><strong>Please cite if you use this dataset:</strong></p><p>Miranda-Escalada, A., Gascó, L., Lima-López, S., Farré-Maduell, E., Estrada, D., Nentidis, A., Krithara, A., Katsimpras, G., Paliouras, G., & Krallinger, M. (2022). Overview of DisTEMIST at BioASQ: Automatic detection and normalization of diseases from clinical texts: results, methods, evaluation and multilingual resources. <i>Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings</i></p><p>@article{miranda2022overview, title={Overview of DisTEMIST at BioASQ: Automatic detection and normalization of diseases from clinical texts: results, methods, evaluation and multilingual resources}, author={Miranda-Escalada, Antonio and Gascó, Luis and Lima-López, Salvador and Farré-Maduell, Eulàlia and Estrada, Darryl and Nentidis, Anastasios and Krithara, Anastasia and Katsimpras, Georgios and Paliouras, Georgios and Krallinger, Martin}, booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings}, year={2022} }</p><p> </p><p><strong>Introduction</strong></p><p>The DisTEMIST corpus is a collection of 1000 clinical cases with <strong>disease mention annotations manually mapped to</strong> <strong>Snomed-CT concepts</strong>. All documents are released in the context of the BioASQ DisTEMIST track for CLEF 2022. For more information about the track and its schedule, please <a href="https://temu.bsc.es/distemist/">visit the website</a>.</p><p> </p><p><strong>File structure:</strong></p><p>The DisTEMIST corpus has been randomly divided into a training set, containing 750 clinical cases, and a test set (584 in the case of subtrack2), consisting of 250 additional cases. Participants must train their systems using the train set and submit predictions for the test set, on which they will be evaluated. The file structure of the corpus is as follows:</p><ul><li><strong>train_set:</strong><ul><li><strong>text_files</strong>: Folder with plain text files of the clinical cases</li><li><strong>subtrack1_entities</strong>: It contains annotations in a tab-separated file (TSV) with the following columns:<ul><li><i>filename</i>: document name</li><li><i>mark</i>: identifier mention id</li><li><i>label</i>: mentions type (ENFERMEDAD)</li><li><i>off0</i>: starting position of the mention in the document</li><li><i>off1</i>: ending position of the mention in the document</li><li><i>span</i>: text span</li></ul></li><li><strong>subtrack2_linking: </strong>It contains annotations in a tab-separated file (TSV) with the following columns:<ul><li><i>filename</i>: document name</li><li><i>mark</i>: identifier mention id</li><li><i>label</i>: mentions type (ENFERMEDAD)</li><li><i>off0</i>: starting position of the mention in the document</li><li><i>off1</i>: ending position of the mention in the document</li><li><i>span</i>: text span</li><li><i>codes</i>: List of Snomed-CT concept codes linked to the mention. If there is more than one code associated with a mention, they will be concatenated by the symbol "+".</li><li><i>semantic relation</i>: the relationship between the assigned code and the mention. It can be EXACT, when the code corresponds exactly with the mention, or NARROW, when the mention corresponds to a narrower concept than the Snomed-CT code. For instance, the concept "Chorioretinal lacunae" does not exist in Snomed-CT. Then, it is normalized to the Snomed-CT ID 302893000 ("Chorioretinal disorder").</li><li>(Note: the training for entity linking were released in two parts, you must join them)</li></ul></li></ul></li></ul><p> </p><ul><li><strong>test_annotated:</strong><ul><li><strong>text_files</strong>: Folder with plain text files of the clinical cases</li><li><strong>brat</strong>: Folder with text files and their annotations in brat's .ann format</li><li><strong>subtrack1_entities: </strong>It contains annotations in a tab-separated file (TSV) with the same columns as the train set</li><li><strong>subtrack2_linking: </strong>It contains annotations in a tab-separated file (TSV) with the same columns as the train set</li></ul></li></ul><p> </p><ul><li><strong>test_background_unannotated/text_files: </strong>3000 clinical cases (test + background). In the the original task, participants had to make predictions for these 3000 files and they were evaluated on a subset of them.</li></ul><p> </p><ul><li><strong>multilingual-resources</strong>: we have generated the annotated training and validation sets in <strong>6 languages</strong>: <i><strong>English</strong></i>, <i><strong>Portuguese</strong></i>, <i><strong>Catalan</strong></i>, <i><strong>Italian</strong></i>, <i><strong>French</strong></i> and <i><strong>Romanian</strong></i>. The process was:<ol><li>The text files were translated with a neural machine translation system.</li><li>The annotations were translated with the same neural machine translation system.</li><li>The translated annotations were transferred to the translated text files using an annotation transfer technology.</li><li>The text files are stored in the multilingual_resources/training-text-files subfolder.</li><li>The annotated TSV files are stored in the multilingual_resources/lang subfolders.</li><li>If you want to visualize the multilingual resources, check out this Brat server: <a href="https://temu.bsc.es/mDistemist/#/translations/">https://temu.bsc.es/mDistemist/#/translations/</a><br>For instance, you can see the parallel annotations in <a href="http://temu.bsc.es/mDistemist/diff.xhtml#/translations/fr/train/es-S0004-06142008000100011-1?diff=/translations/en/train/">English vs in French</a>, or in <a href="https://temu.bsc.es/mDistemist/diff.xhtml#/translations/cat/train/S0004-06142005000500011-1?diff=/gold-standard/train/">Spanish (the gold standard) vs in Catalan</a>.</li></ol></li></ul><p> </p><ul><li><strong>cross-mappings. </strong>We include the same entities as in DISTEMIST-linking but mapped to Snomed-CT, <a href="https://www.ncbi.nlm.nih.gov/mesh/"><strong>MeSH</strong></a>, <a href="https://icd.who.int/browse10/2019/en#/"><strong>ICD-10</strong></a>, <a href="https://hpo.jax.org/"><strong>HPO</strong></a>, and <a href="https://www.omim.org/"><strong>OMIM</strong></a>. The original mappings are manual and to Snomed-CT. The mapping to the other terminologies was done through the UMLS Metathesaurus.</li></ul><p> </p><p><strong>Resources</strong> </p><ul><li><a href="https://temu.bsc.es/distemist/">Task Web</a></li><li><a href="https://github.com/TeMU-BSC/distemist_evaluation_library">Evaluation Library</a></li><li><a href="https://doi.org/10.5281/zenodo.6458114"><strong>DISTEMIST gazetteer</strong></a></li><li><a href="https://doi.org/10.5281/zenodo.6458078"><strong>DISTEMIST guidelines</strong></a></li><li><strong>Citation: </strong>Miranda-Escalada, A., Gascó, L., Lima-López, S., Farré-Maduell, E., Estrada, D., Nentidis, A., Krithara, A., Katsimpras, G., Paliouras, G., & Krallinger, M. (2022). Overview of DisTEMIST at BioASQ: Automatic detection and normalization of diseases from clinical texts: results, methods, evaluation and multilingual resources. <i>Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings</i></li><li><a href="https://ceur-ws.org/Vol-3180/paper-11.pdf"><strong>Overview paper</strong></a></li><li><a href="https://ceur-ws.org/Vol-3180/"><strong>CLEF/BioASQ Workshop proceedings</strong></a></li><li><a href="https://www.youtube.com/watch?v=KvvhogUMjl8&list=PL5uSCzf1azhC_Sg9leXavmcA4nJ4HQa_n"><strong>Youtube videos (overview & teams)</strong></a></li><li><a href="https://www.slideshare.net/MartinKrallinger/overview-of-distemist-at-bioasq-automatic-detection-and-normalization-of-diseases-from-clinical-texts-results-methods-evaluation-and-multilingual-resources-at-bioasq-workshop-of-clef-conference"><strong>DISTEMIST overview talk slides</strong></a></li></ul><p> </p><p><strong>License</strong></p><p>This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p><p> </p><p><strong>Additional resources and corpora</strong></p><p>If you are interested in DisTEMIST, you might want to check out these corpora and resources:</p><ul><li><a href="https://zenodo.org/records/8413866">SymTEMIST</a> (Corpus of symptoms, signs and findings mentions and normalization to SNOMED CT, same document collection)</li><li><a href="https://zenodo.org/records/8224056">MedProcNER </a>(Corpus of clinical procedure mentions and normalization to SNOMED CT, same document collection)</li><li><a href="https://zenodo.org/records/4270158">PharmaCoNER</a> (Corpus of medications, drugs, chemical substances, genes, proteins and vaccine mentions and normalization, same document collection)</li><li><a href="https://zenodo.org/records/7116201">MEDDOPROF</a> (Corpus of mentions of professions, occupations and working status and normalization, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/8403498">MEDDOPLACE</a> (Corpus of mentions of place-related entity mentions, including departments, nationalities or patient movements etc.. and normalization, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/4279323">MEDDOCAN</a> (Corpus of mentions of Personal Health Identifiers (PHI), modified synthetic verions of the document collection)</li><li><a href="https://zenodo.org/records/3978041">CANTEMIST</a> (Corpus of cancer tumor morphology mentions and normalization, different document collection)</li><li><a href="https://zenodo.org/records/3837305">CodiESp</a> (Corpus of clinical case reportes with assigned clinical codes from ICD10, Spanish version, same document collection)</li><li><a href="https://zenodo.org/records/7684093">LivingNER</a> (Corpus of mentions of species, including human/family members, pathogens, food, etc.. and normalization to NCBI Taxonomy, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/2560344">SPACCC-POS</a> (Corpus of clinical case reports in Spanish annotated with POS-tags, same document collection)</li><li><a href="https://zenodo.org/records/2560338">SPACCC-TOKEN</a> (Corpus of clinical case reports in Spanish annotated with token-tags (word mention boundaries), same document collection)</li><li><a href="https://zenodo.org/records/2560338">SPACCC-SPLIT</a> (Corpus of clinical case reports in Spanish annotated with sentence boundary-tags, same document collection)</li><li><a href="https://zenodo.org/records/5602914">MESINESP-2</a> (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, clinical trials, and patent abstracts, different document collection)</li></ul><p><strong>Contact</strong></p><p>If you have any questions or suggestions, please contact us at:</p><p><br>- Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p>
Case Study Videos for DoppelTest
<p>The video recording of 3 case studies discussed in the paper titled "Doppelganger Test Generation for Revealing Bugs in Autonomous Driving Software". These videos were recorded using <a href="https://doi.org/10.5281/zenodo.7622089">Apollo 7.0</a></p>
(Dataset) Evaluating tomotectonic plate reconstructions using geodynamic models with data assimilation, the case for North America
<p>Dataset for the paper:</p> <p>Evaluating tomotectonic plate reconstructions using geodynamic models with data assimilation, the case for North America</p> <p>For more infomation, please look into the README file or contact ljliu@illinois.edu, thank you!</p>
Database of RF fingerprinting on use case IoT devices
<p>This document is a dataset of radiofrequency signals. It is composed of 1000 signals coming emitted by 10 different devices. This dataset was developped for benchmarking machine learning methods on an Internet of Things classification task: recognizing which device emitted a signal.</p> <p>This dataset is in an adaptation of the dataset collected by Basak et al. in “Drone classification from RF fingerprints using deep residual nets” (IEEE COMSNETS conference, 2021).</p> <p>Basak et al. collected signals from six commercial drones, three drone radio-controllers and one WiFi router. The conducted the measurements in an anechoic chamber, using a universal software radio peripheral (USRP X310) placed seven meters apart from the devices . The signals were all in the 2.4 GHz ISM band and the whole 100 MHz band was received instantaneously using a receiving sampling rate of 100 MSps (i.e. the system down-converted the signal frequencies to the 0-100 MHz band to sample them correctly).</p> <p>While the original dataset by Basak et al. consisted in spectrograms of 256 frequency bins by 256 time frames, we have converted in into averaged spectra of 256 frequency bins. Furthermore, while Basak et al. have considered several noise levels, here we only consider the lowest noise level available (-60 dBm).</p> <p>The database is stored in an h5 file, a format adapted to databases. Inside the file there are two datasets: the signals (‘Signals’) and the targets (‘Targets’). The targets correspond to the ten different classes of signals: Parrot Disco (0), Q205 (1), Tello (2), MultiTx (3), Nine Eagles (4), Spektrum DX4e (5), Spectrum DX6i (6), Wltoys (7), S500 (8) and Linkys router (9).</p> <p>This dataset corresponds to the Deliverable D6.2 of the RadioSpin EU funded project.</p>
Crowdsourced COVID-19 Cases and Outbreaks across Canadian Schools 2020-21: COVID Schools Canada
<p>This archive contains the final data freeze for COVID Schools Canada, and the software used to compile, clean, and plot the data. </p> <p>The <strong>Canada COVID-19 School Tracker</strong> was a 100% volunteer-led project tracking COVID-19 cases and outbreaks in schools across Canada from September 2020 to June 2021. The goal of this project was to highlight the impact of COVID-19 on schools and families; to advocate for safer schools; and to advocate for transparency in our educational system.<br> This project is an initiative of grassroots advocacy group <a href="https://masks4canada.org/">Masks4Canada</a>.</p> <p>To learn more about project and see the interactive map of COVID-19 cases and outbreaks across Canadian schools as compiled by this project, visit <a href="https://covidschoolscanada.org/">https://covidschoolscanada.org/ </a></p> <p>For questions about these data, please contact <a href="mailto:shraddha.pai@utoronto.ca?subject=COVID%20Schools%20Canada%20Zenodo%20archive">Shraddha Pai</a>.</p>
Case study for Sustainable agri-food supply chain planning through multi-objective optimisation
<p>Case study used to solve the multi-objective model proposed in: Sustainable agri-food supply chain planning through multi-objective optimisation</p>
Species distribution and abundance modelling with dynamicSDM: a case study analysis of the red-billed quelea (Quelea quelea).
<p><strong>GBIF_all_aves_2000_2020.csv</strong><br> A dataset containing e-Bird sampling events for all bird species across southern Africa between 2000-2020 (Fink et al., 2021, GBIF, 2021). <br> <br> Fink, D., T. Auer, A. Johnston, M. Strimas-Mackey, O. Robinson, S. Ligocki, W. Hochachka, L. Jaromczyk, C. Wood, I. Davies, M. Iliff, L. Seitz. 2021. eBird Status and Trends, Data Version: 2020; Released: 2021. Cornell Lab of Ornithology, Ithaca, New York. \doi{10.2173/ebirdst.2020}<br> GBIF.org (12 July 2021) GBIF Occurrence Download \doi{10.15468/dl.ppcu6q}</p> <p><strong>RBQ_full_analysis.R</strong></p> <p>An R script for the generation of dynamic species distribution and abundance models for nomadic bird, the red-billed quelea (<em>Quelea quelea</em>) using dynamicSDM package functions. </p> <p><strong>Unfiltered_quelea_occurrence.csv</strong><br> A dataset containing species occurrence and abundance records for the bird species, the red-billed quelea (<em>Quelea quelea</em>) between 1976-2021 (GBIF 2021 & GBIF 2022 & sources listed in Table 1). <br> <br> GBIF.org (12 July 2021) GBIF Occurrence Download \doi{10.15468/dl.ppcu6q}<br> <br> GBIF.org (25 July 2022) GBIF Occurrence Download \doi{10.15468/dl.k2kftv}<br> </p> <p><strong>Table S1. </strong>Red-billed quelea (<em>Quelea quelea</em>) occurrence and abundance data sources.</p> <table align="left"> <tbody> <tr> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Sources</strong></p> </td> </tr> <tr> <td> <p><strong>Control operation </strong></p> </td> <td> <ul> <li>Information Core for Southern African Migrant Pests (ICOSAMP, 2001-2005).</li> <li>Centre for Overseas Pest Research (COPR), Natural History Museum, Tring.</li> <li>Botswana Ministries of Agriculture.</li> <li>Mozambique Ministry of Agriculture</li> </ul> </td> </tr> <tr> <td> <p><strong>Citizen science</strong></p> </td> <td> <ul> <li>Global Biodiversity Information Facility, including iNaturalist, eBird, South Africa Bird Atlas Project (SABAP) and South Africa Bird Ringing Unit (SAFRING) sources.</li> </ul> </td> </tr> <tr> <td> <p><strong>Independent research</strong></p> </td> <td> <ul> <li>EXCEL File "NfA-yearposRAC" (unpublished data set complied by R. A. Cheke, 2010).</li> </ul> </td> </tr> </tbody> </table>
Data for "Measuring Back: Bibliodiversity and the Journal Impact Factor brand. A Case study of IF-journals included in the 2021 Journal Citations Report."
<p>This is the open data for the preprint "Measuring Back: Bibliodiversity and the Journal Impact Factor brand. A Case study of IF-journals included in the 2021 Journal Citations Report."</p>
Medication and condition codes used to develop a computable phenotype for Crohn's Disease incident cases
<p>Lists of medication and condition concepts used for Crohn's disease incident case phenotyping as described in my Master's Thesis "Machine Learning Based Prediction of Incident Cases of Crohn’s Disease Using Electronic Health Records From a Large Integrated Health System".</p> <ul> <li>ibd_medication.csv contains medication names, OMOP Concept IDs, RxNorm codes and a flag indicating whether the medication is IBD-specific (i.e., antibiotics and glucocorticoides are marked as unspecific)</li> <li>ibd_conditions.csv contains condition names, OMOP Concept IDs, SNOMED CT codes and a categorical column indicating whether the condition refers to Crohn's Disease (CD), Ulcerative Colitis (UC), or Inflammatory bowel disease unclassified (IBD-U)</li> <li>ibd_symptoms.csv contains symptom names, OMOP Concept IDs, containing symptom group categories, and a flag indicating whether the symptom was added because it is a SNOMED CT descendent code of another code on the list. The list was created based on the IBD symptoms Read Code list provided by Blackwell et. al, 2021, doi:10.1093/ecco-jcc/jjaa146</li> </ul> <p>IBD, Inflammatory Bowel Disease; OMOP, Observational Medical Outcomes Partnership; SNOMED CT, Systematized Nomenclature of Medicine Clinial Terms.</p>
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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.