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34,778 results for “diseases”
Assessment of skin autofluorescence and its association with glycated hemoglobin, cardiovascular risk markers and concomitant chronic diseases in children with type 1 diabetes
<p>This is the dataset for the publication "Assessment of skin autofluorescence and its association with glycated hemoglobin, cardiovascular risk markers and concomitant chronic diseases in children with type 1 diabetes".</p>
Dautan et al 2024 " Gut-Initiated Alpha Synuclein Fibrils Drive Parkinson's Disease Phenotypes: Temporal Mapping of non-Motor Symptoms and REM Sleep Behavior Disorder"
<p><span>Parkinson’s disease (PD) is characterized by progressive motor as well as less recognized non-motor symptoms that arise often years before motor manifestation, including sleep and gastrointestinal disturbances. Despite the heavy burden on the patient’s quality of life, these non-motor manifestations are poorly understood. To elucidate the temporal dynamics of the disease, we employed a mice model involving injection of alpha-synuclein (αSyn) pre-formed fibrils (PFF) in the duodenum and antrum as a gut-brain model of Parkinsonism. Using anatomical mapping of αSyn PFF propagation and behavioral and physiological characterizations, we unveil a correlation between post-injection time the temporal dynamics of αSyn propagation and non-motor/motor manifestations of the disease. We highlight the concurrent presence of aggregates in key brain regions, expressing acetylcholine or dopamine and their functions in sleep duration, wakefulness, and particularly REM-associated atonia corresponging to REM behavioral disorder-like symptoms. This study presents a novel and in-depth exploration into the multifaceted nature of PD, unraveling the complex connections between α-synucleinopathies, gut-brain connectivity, and the emergence of non-motor phenotypes.</span></p>
PlantVillage Disease Classification Challenge - Color Images
<p><br> This work is licensed under a <a href="http://creativecommons.org/licenses/by-sa/3.0/us/">Creative Commons Attribution-ShareAlike 3.0 United States License</a>.<br> <br> # Data origins<br> The dataset is originally hosted at <a href="https://www.crowdai.org/challenges/plantvillage-disease-classification-challenge">PlantVillage Disease Classification Challenge</a>.<br> We use the modified version in <a href="https://github.com/salathegroup/plantvillage_deeplearning_paper_dataset">this github repository</a> to do controlled experiments.<br> We only use the raw color images dataset and delete the unconventional characters in the classes directory name and `.csv` filenames.<br> <br> # Directory explanation<br> The `80-20` direcotry has multiple `.txt` files which contain the training (~80%), validation(~10%) and testing (~10%) datasets instances filenames and the corresponding label indexes. The validation dataset quantity is `5430` in all data separation. In our experiment code (not included in this archive), the validation and testing dataset are merged together.<br> <br> # Data usage<br> ## Replicate our experiments<br> We have used this dataset in writing our paper. The reference information can be seen at https://<a href="https://gitlab.com/huix/leaf-disease-plant-village">gitlab.com/huix/leaf-disease-plant-village</a>.<br> <br> ### Steps<br> 1. `cd` to the direcotry (e.g. `/home/usrname/plantvillage_deeplearning_paper_dataset`) that contains the `color` directory.<br> 2. run `python change_filename_prefix.py --prefix /home/usrname/plantvillage_deeplearning_paper_dataset` to modify the prefix path (which is `/home/h/plantvillage_deeplearning_paper_dataset` in our former generated datasets).<br> 3. Fin. You can use our <a href="https://gitlab.com/huix/leaf-disease-plant-village">opens ource codes repository</a> to do the later experiments.<br> <br> ## Generate your own training/validation/testing datasets<br> This data separation generating code isn't included in the dataset archive, it is in our open source code. Please see our <a href="https://gitlab.com/huix/leaf-disease-plant-village">open source code repository</a> for the detailed information.<br> If you have any questions, you can contact the author through email.<br> The email address is a QR code in the archive.</p>
Data for: Tang et al., Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline. bioRxiv 2018.
<p>Datasets containing 63 whole slide images (WSIs) and their segmented 256x256 pixel tiles with approximately 80,000 tile-level amyloid-β pathology expert annotations.</p> <p><strong>Paper</strong>: "Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline", bioRxiv 454793; DOI: <a href="https://doi.org/10.1101/454793">https://doi.org/10.1101/454793</a>.</p> <p><strong>Details:</strong> A total of 63 WSIs for 63 unique decedent cases spanning Alzheimer’s disease (AD) to non-AD and possessing a variety of CERAD scores. WSIs comprise three datasets as follows:</p> <ol> <li><em>Development (Phases I-II)</em>. 33 WSIs used for convolutional neural network (CNN) model development (29 training, 4 validation).</li> <li><em>Hold-out (Phase III)</em>. 10 WSIs selected by an expert neuropathologist as a held-out test set to assess the generalizability of the CNN model.</li> <li><em>CERAD-like hold-out</em>. 20 blinded WSIs collected solely for use in a CERAD-like scoring comparison study.</li> </ol> <p>Datasets 1 and 2 were color-normalized and segmented to 256x256 pixel image tiles for model training set (61,370 images), validation set (8,630 images), and hold-out test set (10,873 images). Dataset 3 was color-normalized but not segmented.</p> <p>Expert labels of plaques for Dataset 1 and 2 tiles are included in corresponding CSV files.</p> <p><strong>Slide source and preparation:</strong> All samples were retrieved from archives of the University of California, Davis Alzheimer’s Disease Center Brain Bank (<a href="https://www.ucdmc.ucdavis.edu/alzheimers/">https://www.ucdmc.ucdavis.edu/alzheimers/</a>). Archival samples analyzed in this study were 5 μm formalin fixed, paraffin embedded sections of the superior and middle temporal gyrus from human brain. The tissue had been previously stained with an amyloid-β antibody (4G8, recognizing residues 17-24, BioLegend, formerly Covance) that were first pretreated with formic acid to rid samples of endogenous protein. All slides were digitized using an Aperio AT2 up to 40x magnification.</p> <p><strong>Code:</strong> Please visit <a href="https://github.com/keiserlab/plaquebox-paper">https://github.com/keiserlab/plaquebox-paper</a></p> <p> </p>
A Twitter Dataset for Spatial Infectious Disease Surveillance
<p>Dengue is a mosquito-borne viral disease which infects millions of people every year, specially in developing countries. Some of the main challenges facing the disease are reporting risk indicators and rapidly detecting outbreaks. Traditional surveillance systems rely on passive reporting from health-care facilities, often ignoring human mobility and locating each individual by their home address. Yet, geolocated data are becoming commonplace in social media, which is widely used as means to discuss a large variety of health topics, including the users' health status. In this dataset paper, we make available two large collections of dengue related labeled Twitter data. One is a set of tweets available through the Streaming API using the keywords dengue and aedes from 2010 to 2016. The other is the set of all geolocated tweets in Brazil during the year of 2015 (available also through the Streaming API). We detail the process of collecting and labeling each tweet containing keywords related to dengue in one of 5 categories: personal experience, information, opinion, campaign, and joke. This dataset can be useful for the development of models for spatial disease surveillance, but also scenarios such as understanding health-related content in a language other than English, and studying human mobility.</p>
Estimating malaria disease burden in the Asia-Pacific
<p>This repository hosts all the Supplementary Material for the publication: Maude RJ, Mercado CEG, Rowley J, Ekapirat N, Dondorp AM. (2019) Estimating malaria disease burden in the Asia-Pacific (under review)</p>
Keystroke timing and pressure data captured during touchscreen typing by early Parkinson's disease patients and healthy controls
<p><strong>DATASET</strong></p> <p>The present dataset comprises keystroke timing and pressure data that correspond to short text excerpts typed by early Parkinson’s disease (PD) patients (n=18) and healthy controls (n=15) on a common touchscreen-equipped smartphone (LG Nexus 5X with a screen of 5.2 inches in diagonal and a resolution of 1080 × 1920 pixels, running native Android 7.0). Subjects were asked to transcribe up to 11 short text excerpts, with the initial one being 200 characters-long and common for all subjects, while the rest were 40-115 characters-long, pseudorandomly drawn from the fairy tale 'The Little Prince'. Data were recorded using a custom Android Operating System input method (keyboard), developed for the purposes of the study. Additional details on exepriment design, material and methods can be found in the related research article mentioned below. </p> <p>Data consist of sequences of raw press and release timestamps (in milliseconds), as well as of values of normalized pressure (0.000-1.000) applied to initiate keystrokes, corresponding to the consecutive keys tapped during the transcription of each text excerpt. Data included in the 'Data' folder are organised in sub-folders per subject. Each sub-folder contains a number of .txt files with each one corresponding to a text excerpt typed by the particular subject. Files are named using the format S##_TEX##.txt, with S## denoting the subject's coded ID and TEX## the serial number of the transcribed text excerpt. For all subjects, file S##_TEX01.txt corresponds to the initial and common 200 characters-long text excerpt. Each file contains the sequences of raw key press/release timestamps (Tp#, Tp#) and normalized pressure (NP#), applied to initiate each keystroke, in the following format:</p> <p>{<br> Press, Tp1, Release, Tr1, NP1<br> Press, Tp2, Release, Tr2, NP2<br> .<br> .<br> . <br> Press, Tpn, Release, Trn, NPn<br> }</p> <p>where 1,2,...,n denote the serial index of the key tapped during typing.</p> <p><em>Note:</em> Out of 33 subjects, 32 managed to transcribe 8 to 11 text excerpts, while the remaining one (Subject ID: 16) typed only 5. Ten subjects (Subject IDs: 6, 14, 16, 17, 25, 27, 29, 31, 32, 33) did not manage to type the initial 200 characters-long excerpt in its entirety.</p> <p>The dataset also includes a record, in Microsoft Excel format (Demographics_Clinical_Characteristics.xlsx), of the demographic and clinical characteristics (with respect to PD) of subjects. Entries of the Excel file are linked to subjects' sub-folders and individual keystroke data text files via the coded ID of the subject.</p> <p>Demographic characteristics included:</p> <p>Age; Gender; Education level; Years of smartphone usage; Dominant hand<sup>1</sup></p> <p>Clinical characteristics included:</p> <p>Group (PD, Control); Years from diagnosis; Hoehn-Yahr disease stage; Most affected side<sup>2</sup>; Levodopa Equivalent Daily Dose; UPDRS_III<sup>3</sup> total score; UPDRS_III Item 21 Tremor-Right hand; UPDRS_III Item 21 Tremor-Left hand; UPDRS_III Item 22 Rigidity-Right hand; UPDRS_III Item 22 Rigidity-Left hand; UPDRS_III Item 23 Finger taps-Right hand; UPDRS_III Item 23 Finger taps-Left hand; UPDRS_III Item 31 Body bradykinesia/ Hypokinesia</p> <p><sup>1</sup>Dominant hand: (Relating to handedness) the operant hand generally used for performing fine motor-skills tasks.<br> <sup>2</sup>Most affected body side by Parkinson's disease<br> <sup>3</sup>UPDRS_III: Unified Parkinson's Disease Rating Scale Part III (Motor section)</p> <p> </p> <p><strong>RELATED RESEARCH</strong></p> <p>This dataset was originally used and described in the OPEN ACCESS publication: </p> <p>[1] Iakovakis, D., Hadjidimitriou, S., Charisis, V., Bostantzopoulou, S., Katsarou, Z., & Hadjileontiadis, L. J. (2018). Touchscreen typing-pattern analysis for detecting fine motor skills decline in early-stage Parkinson’s disease. Scientific reports, 8(1), 7663. <a href="http://doi.org/10.1038/s41598-018-25999-0">https://doi.org/10.1038/s41598-018-25999-0</a> </p> <p>All documents and papers that report on research that uses this dataset will acknowledge this by citing the above publication.</p> <p> </p> <p><strong>ETHICS & FUNDING</strong></p> <p>The study during which the present dataset was collected was approved by the Aristotle University of Thessaloniki Bioethics Committee of Medical School (approval no. 359/3.4.17), Thessaloniki, Greece. Informed consent, including permission for third-party access to pseudo-anonymised data, was obtained from all subjects prior to their engagement with the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 690494 - i-PROGNOSIS: Intelligent Parkinson early detection guiding novel supportive interventions (<a href="http://www.i-prognosis.eu">i-prognosis.eu</a>).</p> <p> </p> <p><strong>CORRESPONDANCE</strong></p> <p>Any inquiries regarding this dataset should be adressed to:</p> <p>Mr. Dimitrios Iakovakis (Electrical & Computer Engineer, PhD candidate)</p> <p>Signal Processing & Biomedical Technology Unit<br> Department of Electrical & Computer Engineering<br> Aristotle University of Thessaloniki<br> University Campus, Building D, 6th floor<br> Thessaloniki, Greece, GR54124</p> <p>Tel: +30 2310 996319<br> Fax: +30 2310 996312<br> E-mail: dimiiako12@gmail.com</p> <p> </p> <p><strong>LICENSE</strong></p> <p>This is an open access dataset, licensed under Creative Commons Attribution 4.0 International (<a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>).</p> <p> </p> <p><strong>WARRANTY</strong></p> <p>This dataset comes without any warranty. Administrators of this dataset can not be held accountable for any damage (physical, financial or otherwise) caused by the use of this dataset. </p>
Expansion of coccidioidomycosis (Valley fever) endemic regions in the United States in response to climate change: projections of disease incidence
<p>This file contains estimations of coccidioidomycosis (Valley fever) incidence data in cases per 100,000 population per year for the contemporary time period and projections throughout the 21st century in response to RCP4.5 and RCP8.5 climate scenarios, associated with the publication:</p> <p>Gorris, M. E., Treseder, K. K., Zender, C. S., and Randerson, J. T. (2019). Expansion of coccidioidomycosis endemic regions in the United States in response to climate change. <em>GeoHealth</em>. </p> <p>The data is reported for each county in the conterminous US with its associated FIPS code (Column 1), county name (Column 2), state FIPS code (Column 3), and state name (Column 4). Column 5 contains the estimation of mean annual Valley fever incidence averaged from 2000-2015. Column 6-8 contain the estimations of mean annual Valley fever incidence for the 11-year averages surrounding years 2035, 2065, and 2095 for RCP4.5 climate scenario. Likewise, Columns 9-11 contain the estimations of mean annual Valley fever incidence for the 11-year averages surrounding years 2035, 2065, and 2095 for the RCP8.5 climate scenario. </p> <p>Details about how the incidence data was calculated may be read in the Methods subsection of the paper under "Modeling of current and future mean annual Valley fever incidence". The data provided here was used to create Figure 7 and Supporting Information Figure S5. Counties that have non-zero incidence are considered endemic by our climate-constrained niche model, so this data may also be used to create portions of Figures 3, 4, and S3. </p>
Data and code for 'Connectivity over a disease gradient enables recovery of rainforest frogs'
<p>This repository provides all data and R code from the analysis presented in the following paper:</p> <p>Bell, S.C., Heard, G.W., Berger, L. and Skerratt, L.F. (in review). Connectivity over a disease risk gradient enables recovery of rainforest frogs. </p> <p>The data are provided as a series of .csv files. R code is provided separately for each of the following components:</p> <p>1. A script to collate nightly air temperature data from stream-side temperature loggers, plus daily maximum air temperature data from the nearest Australian Bureau of Meteorology recording station to each site.</p> <p>2. A script to fit a hierarchical linear model to the stream-side air temperature, with the aid of the R2OpenBUGS package.</p> <p>3. A script to fit a hierarchical logistic regression model to the chytrid infection data, with the aid of the R2OpenBUGS package.</p> <p>4. A script to fit logistic regression models to the frog occupancy data, with the aid of the R2OpenBUGS package. </p> <p>These files allow all analyses from the paper to be reproduced. </p>
Data and code for 'Connectivity over a disease risk gradient enables recovery of rainforest frogs'
<p>This repository provides all data and R code from the analysis presented in the following paper:</p> <p>Bell, S.C., Heard, G.W., Berger, L. and Skerratt, L.F. (in review). Connectivity over a disease risk gradient enables recovery of rainforest frogs. </p> <p>The data are provided as a series of .csv files. R code is provided separately for each of the following components:</p> <p>1. A script to collate nightly air temperature data from stream-side temperature loggers, plus daily maximum air temperature data from the nearest Australian Bureau of Meteorology recording station to each site.</p> <p>2. A script to fit a hierarchical linear model to the stream-side air temperature, with the aid of the R2OpenBUGS package.</p> <p>3. A script to fit a hierarchical logistic regression model to the chytrid infection data, with the aid of the R2OpenBUGS package.</p> <p>4. A script to fit logistic regression models to the frog occupancy data, with the aid of the R2OpenBUGS package. </p> <p>These files allow all analyses from the paper to be reproduced. </p>
MeSDiCon - Medical Spanish Disease and symptom name Collection lexicon (unfiltered initial version)
<p>The MeSDiCon - (Medical Spanish Disease and symptom name Collection lexicon) consists of a list or gazetteer of candidate names of diseases and symptoms mentioned in Spanish clinical texts. Thus MeSDiCon serves as a lexical resource or dictionary for automatic detection of disease/symptom mentions, as well as indexing or classification of medical texts with such concept types.</p> <p>This collection was generated in a five step procedure:</p> <ol> <li>Automatic detection of mentions of disease/symptom terms in biomedical texts in English (including mapping/normalization to MeSH terms or OMIM identifiers).</li> <li>Generation of a unique name list from the detected concept mentions.</li> <li>Basic filtering of non- disease/symptom names or highly ambiguous mentions-abbreviations using basic characteristics like name morphology and length criteria.</li> <li>Automatic translation of name lists form English to Spanish using a medical machine translation system (see Soares, F. and Krallinger, M. BSC Participation in the WMT Translation of Biomedical Abstracts. In <em>Proceedings of the Fourth Conference on Machine Translation, Volume 3: Shared Task Papers, </em>pp. 175-178 2019; https://zenodo.org/record/3346802)</li> <li>Automatic mention lookup of translated names in a collection of 20 million Spanish clinical notes (primary care and pediatrics).</li> </ol> <p>Every term in MeSDiCon is identified by a text span (in Spanish), a target terminology namespace to which it was automatically mapped (MeSH or OMIM) and its corresponding concept identifier in that target terminology. Moreover, we provide for every text span the absolute term frequency, i.e. the number of matches in the corpus of 20 million clinical notes and the number of documents or notes in which it was automatically.</p> <p>Important note: no manual filtering of the MeSDiCon was carried out, implying that some entries might comprise errors, either due to the initial name recognition and concept mapping in English or due to wrong automatic translations into Spanish.</p> <p>The MeSDiCon resource is provided in two formats:</p> <ul> <li>TSV. Data is separated by tabs (\t). Every row of the file has the following fields:</li> </ul> <pre><code>terminology identifier translatedTerm termCount documentCount</code></pre> <ul> <li>JSON. Records are stored as a list of JSON objects. They have the following fields:</li> </ul> <pre><code>{ "terminology":"MESH", "identifier":"D025861", "translatedTerm":"Trastornos de la coagulación", "termFrequency":9, "documentFrequency":9 }</code></pre> <p> </p> <p>Copyright (c) 2019 Secretaría de Estado para el Avance Digital</p>
Dataset for: Infectious disease responses to human climate change adaptations
<p>Original and derived data products referenced in the original manuscript are provided in the data package.</p> <h3>Description of the data and file structure</h3> <p><em>Original data:</em></p> <p><code>Table_1_source_papers.csv</code>: Papers that met review criteria and which are summarized in Table 1 of the manuscript.</p> <ol> <li><strong>ID</strong>: The paper identification number</li> <li><strong>Topic</strong>: The broad topic (i.e., each row of Table 1)</li> <li><strong>Authors:</strong> The names of the authors of the paper</li> <li><strong>Article Title</strong>: The title of the paper</li> <li><strong>Source Title</strong>: The name of the journal in which the paper was published</li> <li><strong>Abstract</strong>: The paper's abstract, retrieved from the Web of Science search</li> <li><strong>study_type:</strong> Classification of the study methodology/approach. "A" = a designed study that shows effect ,"B" = a pre/post study, "C" = a comparison of health outcomes or pathogen risk relative to a 'control/comparison' area, "D" = some quantitative effect but no control, "E" = qualitative comments but little supporting evidence, and/or a qualitative review.</li> <li><strong>pathogen_broad</strong>: Broad classification of the type of pathogen discussed in the paper.</li> <li><strong>transmission_type</strong>: Categorization of indirect, direct, sexual, vector, or other transmission modes.</li> <li><strong>pathogen_type</strong>: Categorization of bacteria, helminth, virus, protozoa, fungi, or other pathogen types.</li> <li><strong>country:</strong> Country in which the study was performed or results discussed. When countries were not available, regions were used. NA values indicate papers in which a geographic region was not relevant to the study (i.e., a methods-based study).</li> </ol> <p><em>Derived data:</em></p> <p><code>change_livestock_country.csv:</code> A dataframe containing values used to generate Figure 4a in the manuscript.</p> <ol> <li><strong>County Name</strong>: The name of the county in Kenya</li> <li><strong>Sheep and goats 1980</strong>: The estimated number of sheep and goats in 1980</li> <li><strong>Sheep and goats 2016</strong>: The estimated number of sheep and goats in 2016</li> <li><strong>pct_change_shoat</strong>: The percent change in sheep and goat numbers from 1980 to 2016</li> <li><strong>Cattle 1980</strong>: The estimated number of cattle in 1980</li> <li><strong>Cattle 2016</strong>: The estimated number of cattle in 2016</li> <li><strong>pct_change_cattle</strong>: The percent change in cattle numbers from 1980 to 2016</li> <li><strong>Camel 1980</strong>: The estimated number of camels in 1980</li> <li><strong>Camel 2016</strong>: The estimated number of camels in 2016</li> <li><strong>pct_change_camel</strong>: The percent change in camel numbers from 1980 to 2016</li> <li><strong>human_pop 1980</strong>: The estimated human population in the county in 1980</li> <li><strong>human_pop 2016</strong>: The estimated human population in the county in 1980</li> <li><strong>pct_change_human</strong>: The percent change in the human population from 1980 to 2016</li> <li><strong>area_sq_km</strong>: The land area of the county</li> <li><strong>change_ind_per_sq_km_shoat:</strong> Absolute change in number of sheep and goats from 1980 to 2016</li> <li><strong>change_ind_per_sq_km_cattle:</strong> Absolute change in number of cattle from 1980 to 2016</li> <li><strong>change_ind_per_sq_km_camel:</strong> Absolute change in number of camels from 1980 to 2016</li> </ol> <p><code>country_avg_schist_wormy_world.csv</code>: A dataframe containing values used to generate Figure 3 in the manuscript.</p> <ul> <li><strong>Country:</strong> The country in which the schistosome prevalence studies were performed.</li> <li><strong>Latitude:</strong> The latitute in decimal degrees</li> <li><strong>Longitude:</strong> The longitute in decimal degrees</li> <li><strong>Maximum.prevalence:</strong> The mean maximum schistosomiasis prevalence of studies conducted within each country.</li> </ul> <p><code>kenya_precip_change_1951_2020.csv</code>: A dataframe containing values used to generate Figure 4b in the manuscript.</p> <ul> <li><strong>Precipitation (mm):</strong> Binned annual precipitation values</li> <li><strong>1951-1980:</strong> The density of observations for each annual precipitation value for the 1951-1980 period</li> <li><strong>1971-2000:</strong> The density of observations for each annual precipitation value for the 1971-2000 period</li> <li><strong>1991-2020:</strong> The density of observations for each annual precipitation value for the 1991-2020 period</li> </ul> <h3>Sharing/Access information</h3> <p>Data were derived from the following sources:</p> <ul> <li> <p>Ogutu, J. O., Piepho, H.-P., Said, M. Y., Ojwang, G. O., Njino, L. W., Kifugo, S. C., & Wargute, P. W. (2016). Extreme wildlife declines and concurrent increase in livestock numbers in Kenya: What are the causes? <em>PloS ONE</em>, <em>11</em>(9), e0163249. https://doi.org/10.1371/journal.pone.0163249</p> </li> <li> <p>London Applied & Spatial Epidemiology Research Group (LASER). (2023). <em>Global Atlas of Helminth Infections: STH and Schistosomiasis</em> [dataset]. London School of Hygiene and Tropical Medicine. https://lshtm.maps.arcgis.com/apps/webappviewer/index.html?id=2e1bc70731114537a8504e3260b6fbc0</p> </li> <li> <p>World Bank Group. (2023). <em>Climate Data & Projections—Kenya</em>. Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org/country/kenya/climate-data-projections</p> </li> </ul>
Data archive for "Modified rice bran arabinoxylan as a nutraceutical in health and disease — A scoping review with bibliometric analysis"
<p>v1.0.0 Release with the publication of the paper on PLoS One</p> <p>Ooi, S. L., Micalos, P. S., & Pak, S. C. (2023). Modified rice bran arabinoxylan as a nutraceutical in health and disease—A scoping review with bibliometric analysis. PLOS ONE, 18(8), e0290314. https://doi.org/10.1371/journal.pone.0290314</p> <p><strong>Full Changelog</strong>: https://github.com/sooi10/RBACScoping/commits/NetworkAnalysis</p>
Video Series: Integrated Pest Management focusing on disease control in cereals
<p>Welcome to this video series on IPM, focusing on disease control in cereals. </p> <p>The challenge of IPM is to make the control methods we use appropriate to the circumstances, and to balance between the productivity of the crop and minimising the impact on the environment. The control decisions we make on one field or in one season may not be appropriate in another set of circumstances – there is no ‘blue print’. In these videos we delve into the physiology of the crop, the epidemiology of the diseases and how different control methods work. By understanding the biology of the systems we're trying to control, we're better equipped to make appropriate decisions. Going into depth means we can’t cover all aspects of IPM. In practice, decisions about disease control are being made alongside decisions about invertebrate pests and weeds, and in the wider context of integrated crop management. In the UK, information on those topics is available from organisations such as LEAF, AHDB and the Voluntary Initiative. Nevertheless, disease control is still a big topic, so we have broken it down into bite size chunks - although each video is still a pretty substantial bite and will need some digesting.</p> <p>The videos can be viewed in any order that interests you, but they'll make most logical sense viewed in the order in the menu.</p> <p>Links to the videos can be found in the summary document. </p> <p>PDF versions of the video presentations are provided. </p>
PhasAGE Training School 1 - Phase separation in diseases - LECTURE
<p>The Training School 1 <strong>“Computational Methods to Study Protein Phase Separation”</strong> is the first edition of a series of PhasAGE training activities.</p> <p>The goal of this course is to provide participants with the basic knowledge to understand the phenomenon of <strong>Phase Separation</strong>, its role in biological processes and diseases. In addition, the course will provide <strong>an overview of the available computational resources</strong> to navigate this knowledge. Participants will have <strong>hands-on training</strong> in tools and resources available for life sciences, to collect information from the literature on biomolecular phase transitions, identify features triggering phase transitions, mutations associated with diseases, known or predicted PTMs and molecular interaction sites.</p>
Building Large-Scale Gene-Disease Association Datasets for Biomedical Relation Extraction
<p>This repository contains the GDAb and GDAt datasets. GDAb and GDAt are large-scale, distantly supervised, and manually enhanced datasets for Gene-Disease Association (GDA) extraction. Each dataset consists of three text files, corresponding to train, validation, and test sets, plus an additional JSON file containing the mapping between relation names and IDs. Each record in train, validation, or test files corresponds to a single GDA extracted from a sentence. Records are represented as JSON objects with the following structure:</p> <ul> <li><strong>text:</strong> sentence from which the GDA was extracted.</li> <li><strong>relation:</strong> relation name associated to the given GDA.</li> <li><strong>h: </strong>JSON object representing the gene entity, composed of: <ul> <li><strong>id: </strong>UMLS CUI associated to the gene entity.</li> <li><strong>name:</strong> UMLS preferred name associated to the gene entity.</li> <li><strong>pos: </strong>list consisting of starting position and length of the gene mention within text.</li> </ul> </li> <li><strong>t:</strong> JSON object representing the disease entity, composed of: <ul> <li><strong>id: </strong>UMLS CUI associated to the disease entity.</li> <li><strong>name:</strong> UMLS preferred name associated to the disease entity.</li> <li><strong>pos:</strong> list consisting of starting position and length of the disease mention within text.</li> </ul> </li> </ul> <p>Both datasets contain over 2,500,000 sentences and 500,000 bags.<br> The zip file consists of two folders, GDAb and GDAt, containing the files corresponding to the two datasets, respectively.</p> <p> </p>
Database of non-communicable disease reports contributing to UN high-level meeting process (2000-2020)
<p>Database of the key non-communicable disease reports, policy papers, strategies and journal series published 2000-2020 that fed into the UN high-level meeting process</p>
Valuation of heat related mortality risk and tick-borne diseases
<p>Monetary impacts of premature mortality due to heat waves</p> <p>Preferences for public programmes against spread of ticks due to climate change and a new vaccine against Lyme disease, prevalence of tick-borne diseases and exposure to ticks</p>
Visual perturbation of balance suggests impaired motor control but intact visuomotor processing in Parkinson's disease, J Neurophysiol (2021): Data
<p>Data set accompanying the publication:</p> <p>Engel, D., Student, J., Schwenk, J., Morris, A. P., Waldthaler, J., Timmermann, L., & Bremmer, F. (2021). Visual perturbation of balance suggests impaired motor control but intact visuomotor processing in Parkinson's disease. <em>Journal of neurophysiology</em>, <em>126</em>(4), 1076–1089. https://doi.org/10.1152/jn.00183.2021</p>
Experience of COVID-19 disease and fear of the SARS-CoV-2 virus among Polish students
<p>The deposited files contain a database related to the study of the fear of COVID-19 among Polish students and a code book. It is connected with the article titled <em>Experience of COVID-19 disease and fear of the SARS-CoV-2 virus among Polish students</em></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.