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30,813 results for “type”
Accepted Artifact for Privacy-Respecting Type Error Telemetry at Scale
<p>This artifact packages the data for the paper: <em>Privacy-Respecting Type Error Telemetry at Scale</em></p> <p>There are two files on Zenodo:</p> <ul> <li>data.tar.gz has the original Luau telemetry data</li> <li>artifact.tar.gz has a result PDF, intermediate data, and scripts for processing the data</li> </ul> <p>The artifact code and the source for the paper are also on GitHub:</p> <ul> <li><a href="https://github.com/bennn/luau-telemetry">https://github.com/bennn/luau-telemetry</a></li> </ul> <p>This artifact is primarily a **dataset**. It shows how we reached the conclusions in the paper.</p> <p>The scripts in this artifact are provided as-is for completeness. They may have bugs. They may not work as advertised.</p>
USENIX'24 Artifact Datasets: With Great Power Come Great Side Channels: Statistical Timing Side-Channel Analyses with Bounded Type-1 Errors
<p>This dataset contains the measurements and analysis results for our USENIX Security '24 paper 'With Great Power Come Great Side Channels: Statistical Timing Side-Channel Analyses with Bounded Type-1 Errors'.</p>
Simulation of the efficiency of a reversed supply chain of wood biomass using different types of transport units (NCN) DEC-2020/39/I/HS4/03533
<p>Data describing simulations related to the standardisation of loading units for the transport of wood biomass. The effectiveness of assumptions relating to the use of different types of packaging were verified from the perspective of the number of vehicles required and their emissions. The relationship between the size and specification of the wood biomass load and the packaging used was indicated. The study was funded by National Science Centre in Poland under agreement National Center of Science (NCN) through grant DEC-2020/39/I/HS4/0353</p>
Global soil type dataset for WRF-ARW model, based on HWSD version 2
<p>Global soil type dataset, based on HWSD ("Harmonized World Soil Database", version 2.0), suitable for meteorological model WRF-ARW.</p> <ul> <li>spatial resolution: 30 arc seconds by 30 arc seconds (about 1km)</li> <li>original data (HWSD 2.0) <ul> <li><a href="https://s3.eu-west-1.amazonaws.com/data.gaezdev.aws.fao.org/HWSD/HWSD2_RASTER.zip">https://s3.eu-west-1.amazonaws.com/data.gaezdev.aws.fao.org/HWSD/HWSD2_RASTER.zip</a></li> <li><a href="https://s3.eu-west-1.amazonaws.com/data.gaezdev.aws.fao.org/HWSD/HWSD2_DB.zip">https://s3.eu-west-1.amazonaws.com/data.gaezdev.aws.fao.org/HWSD/HWSD2_DB.zip</a></li> <li>https://gaez.fao.org/pages/hwsd</li> <li>documentation: Nachtergaele, Freddy, et al. Harmonized world soil database version 2.0. Food and Agriculture Organization of the United Nations, 2023. https://www.fao.org/3/cc3823en/cc3823en.pdf</li> </ul> </li> <li>the original 7 soil layers (0–20 cm, 20–40 cm, 40–60 cm, 60–80 cm, 80–100 cm, 100–150 cm and 150–200 cm) have been remapped to the 2 layers required by WRF (topsoil 0-30 cm, botsoil 30-200 cm)</li> <li>the original Soil Mapping Units (SMU) have been remapped to the 16 soil categories used by WRF: <ul> <li>the depth-weighted averages of the content of clay, silt and sand lead to 12 texture-based categories (Sand, Loamy sand, Sandy loam, Silt loam, Silt, Loam, Sandy clay loam, Silty clay loam, Clay loam, Sandy clay, Silty clay, Clay), as defined by USDA;</li> <li>category "Organic material" is assigned where the average content of organic carbon exceeds the threshold of 25%;</li> <li>where the content of clay, silt and sand is not defined, HWSD special categories are mapped to the WRF last 3 categories, as follows: <ul> <li>"Water bodies" to "Water", </li> <li>"Rock outcrops" and "Rocky sublayers" to "Bedrock", </li> <li>"Land ice and glaciers", "Dunes/shifting sands", "Salt flats", and "Other" to "Other"</li> </ul> </li> </ul> </li> </ul> <p>The dataset is provided in three ways:</p> <ol> <li>two global files (SoilType_depth<T>to<B>cm.tif), one for each layer; format is GeoTIFF, compatible with <a href="https://github.com/openwfm/convert_geotiff" target="_blank" rel="noopener"><em>convert_geotiff</em></a>, a commandline utility for converting data from GeoTIFF to geogrid format used by WRF;</li> <li>16 tiles, 8 for each layer, each covering 90 degrees by 90 degrees (SoilType_depth<T>to<B>cm_lon<W>to<E>deg_lat<S>to<N>deg.tif); format is GeoTIFF;</li> <li>two compressed folders, hwsd_toplayer.zip and hwsd_bottomlayer.zip, each including 648 tiles in binary format and an "index" ASCII file, following the Geogrid data format and naming convention, as described <a href="https://www2.mmm.ucar.edu/wrf/users/tutorial/presentation_pdfs/202101/duda_wps_advanced.pdf">here</a>.</li> </ol> <p>Soil categories are coded as follows</p> <table> <tbody> <tr> <td><strong>code</strong></td> <td><strong>category</strong></td> </tr> <tr> <td>1</td> <td>sand</td> </tr> <tr> <td>2</td> <td>loamy sand</td> </tr> <tr> <td>3</td> <td>sandy loam</td> </tr> <tr> <td>4</td> <td>silt loam</td> </tr> <tr> <td>5</td> <td>silt</td> </tr> <tr> <td>6</td> <td>loam</td> </tr> <tr> <td>7</td> <td>sandy clay loam</td> </tr> <tr> <td>8</td> <td>silty clay loam</td> </tr> <tr> <td>9</td> <td>clay loam</td> </tr> <tr> <td>10</td> <td>sandy clay</td> </tr> <tr> <td>11</td> <td>silty clay</td> </tr> <tr> <td>12</td> <td>clay</td> </tr> <tr> <td>13</td> <td>organic material</td> </tr> <tr> <td>14</td> <td>water</td> </tr> <tr> <td>15</td> <td>bedrock</td> </tr> <tr> <td>16</td> <td>other</td> </tr> </tbody> </table> <p> </p>
Fetal exposure to the Ukraine famine of 1932-1933 and adult Type 2 Diabetes Mellitus (Public data and analytical code)
<p><strong>Abstract</strong></p> <p>The short-term impact of famines on death and disease is well documented but it is difficult to estimate their potential long-term impact. We used the setting of the man-made Ukrainian Holodomor famine of 1932-1933 to examine the relationship between prenatal famine and adult Type 2 diabetes mellitus (T2DM). This ecological study included 128,225 T2DM cases diagnosed between 2000-2008 among 10,186,016 male and female Ukrainians born between 1930 and 1938. Individuals who were born in the first half-year of 1934, and hence exposed in early gestation to the mid-1933 peak famine period, had a larger than two-fold likelihood of T2DM (OR 2.21; 95% CI 2.00-2.45) compared to unexposed controls. There was a dose-response relationship between severity of famine exposure and adult T2DM risk comparing individuals born in regions with severe, very severe, and extreme famine to births in the no-famine region.</p> <p> </p> <p><strong>Description of the data and analytical code</strong></p> <p>In exploratory analyses we first examined whether the odds for T2DM were elevated for any month of birth in the period January 1930 to December 1938 in any of the four regions of varying famine intensity. This was achieved by comparing, within each region, the T2DM odds for births in any month and year of birth relative to the T2DM odds for births in the same month combining all other years of birth. The analysis served to identify potential relations of famine with specific months and years of birth, controlling for month of birth effects. We observed increased T2DM odds ratios for births between January and June 1934 in famine-exposed oblasts, with smaller increases for births in 1935 and 1936 in these months. Our findings suggested that in multivariate modelling statistical control for month of birth effects could be accomplished by adjusting for the January-June period. Our findings are presented in the data file '01 Odds Ratio for T2DM Over Time' and show the odds ratios (ORs) for Type 2 Diabetes Mellitus (T2DM) comparing the region-specific T2DM odds for each birth year and month relative to births in the same months but combining all other years of birth. The R syntax file '01 Odds of T2DM Over Time Figure' provides the code necessary to reproduce the figure.</p> <p> </p> <p>For confirmatory analyses we employed a Difference-in-Differences approach to quantify associations between prenatal exposure to famine and T2DM, taking into account year of birth, half-year of birth (Jan-Jun vs Jul-Dec), region, and their interactions. This analysis was conducted initially for each gender separately and then for both genders combined, adjusting for We carried out sensitivity analyses to assess potential changes in T2DM odds arising from the use of pre-famine births vs post-famine births as controls. Our findings are presented in the data file '02 Ukraine Famine 1932-33 Main Data'. Information on the number of T2DM cases by gender, region of residence, and year and month of birth 1930-1938 in Ukraine was collected by the national Ukraine Diabetes Register (Komisarenko Institute of Endocrinology and Metabolism, Kyiv) between 2000-2008. The number of births in the same subgroups, representing the populations at risk for T2DM, was estimated by demographic population reconstruction methods as reported in the publication. We classified the birth counts by year of birth, the semi-annual birth period (January-June vs. July-December), region of birth, and gender. The SPSS syntax file titled '02 Ukraine Famine 1932-33 Main Analysis' provides the code to replicate our main findings as presented in the publication.</p> <p> </p> <p>In a separate analysis we visualized by a meta-regression approach the relation between famine intensity at the oblast level in 1933 and the odds for adult T2DM. The data required for the replication of our findings are included in the file '03 Odds Ratio for T2DM and Famine Intensity at Oblast Level'. The R syntax file titled '03 Ukraine Famine 1932-33 Meta-regression' provides details on conducting the meta-regression using the R package ‘metafor’.</p> <p> </p> <p><strong>Funding</strong></p> <p>Ukraine State complex program Diabetes Mellitus, project number 0106U000844 (M.K.). Holodomor Research and Education Consortium in Canada (L.H.L., O.W.). NIDI-NIAS Fellowship of the Royal Netherlands Academy of Sciences (L.H.L.). National Institute of Aging R01 AG028593 (L.H.L.). National Institute of Aging R01 AG06687 (L.H.L.).</p> <p> </p> <p><strong>Sharing/Access information</strong></p> <p>Data sharing and use are unrestricted with acknowledgement of the original publication and listing of the funding sources as per the above. Researchers are encouraged to contact the Principal Investigators (PIs) for consultations on data structure and use as needed (L.H. Lumey, <a href="mailto:lumey@columbia.edu">lumey@columbia.edu</a>; Oleh Wolowyna, <a href="mailto:olehw@aol.com">olehw@aol.com</a>).</p>
Proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitor therapy reduces the level of DNA damage in patients with heterozygous familial hypercholesterolemia
<p><span><span>Heterozygous Familial Hypercholesterolaemia (HeFH) is a common autosomal dominant genetic disease (1:300) characterized by elevated LDL-C leading to premature atherosclerosis. Treatment with a PCSK9 inhibitor (iPCSK9) is recommended in high cardiovascular risk FH patients if the treatment goal is not achieved on maximal tolerated statin plus ezetimibe. </span></span><span>The aim of this study was to </span><span><span>examine the changes in DNA damage in HeFH patients associated with iPCSK9 use. </span></span><span>Fifty-seven patients were included: a normolipidemic group (control; n=20) and patients with HeFH (study group; n=36). DNA damage was determined by alkaline comet assay. PCSK9 protein level was assessed by ELISA. </span><span>The levels of Lp(a) in human serum were quantitatively turbidimetrically assay.</span><span> </span><span>PCSK9i treatment was associated with lower DNA damage, Lp(a), PCSK9 and lipid profile than before treatment. However, 20 of 36 patients still had Lp(a) values above 125 nmol/L, and reduced Lp(a) did not correlate with reduced DNA damage. Reduced PCSK9 moderately (r=0.48) correlates with reduced DNA damage</span><span><span>. PCSK9i therapy reduces the level of DNA damage in HeFH patients, regardless of the type of inhibitor. The reduction in DNA damage is not related to the changes in lipid profile or Lp(a) induced by PCSK9i, but it is dependent on PCSK9 level.</span></span></p>
Inhibition of striatal dopamine release by the L-type calcium channel inhibitor isradipine co-varies with risk factors for Parkinson's
<h3><strong>ABSTRACT</strong></h3> <p>Ca<sup>2+</sup> entry into nigrostriatal dopamine (DA) neurons and axons via L-type voltage-gated Ca<sup>2+</sup> channels (LTCCs) contributes respectively to pacemaker activity and DA release, and has long been thought to contribute to vulnerability to degeneration in Parkinson’s disease. LTCC function is greater in DA axons and neurons from substantia nigra pars compacta than from ventral tegmental area, but this is not explained by channel expression level. We tested the hypothesis that LTCC-control of DA release is governed rather by local mechanisms, focussing on candidate biological factors known to operate differently between types of DA neurons and/or be associated with their differing vulnerability to parkinsonism, including biological sex, α-synuclein, DA transporters (DATs), and calbindin-D28k (Calb1). We detected evoked DA release <em>ex vivo </em>in mouse striatal slices using fast-scan cyclic voltammetry, and assessed LTCC support of DA release by detecting the inhibition of DA release by the LTCC inhibitors isradipine or CP8. Using genetic knockouts or pharmacological manipulations we identified that striatal LTCC support of DA release depended on multiple intersecting factors, in a regionally and sexually divergent manner. LTCC function was promoted by factors associated with Parkinsonian risk, including male sex, α-synuclein, DAT, and a dorsolateral co-ordinate, but limited by factors associated with protection i.e. female sex, glucocerebrosidase activity, Calb1, and ventromedial co-ordinate. Together, these data show that LTCC function in DA axons, and isradipine effect, are locally governed and suggest they vary in a manner that in turn might impact on, or reflect, the cellular stress that leads to parkinsonian degeneration.</p> <p> </p> <h3><strong>FILE DESCRIPTIONS</strong></h3> <p>This repository contains the following files:</p> <ul> <li>Key Resources Table (.xlsx) - Table containing details on key lab materials (antibodies, mouse lines, and software), and the persistent identifiers for protocols and code used and generated in this study. </li> <li>Source Data (.xlsx) - Excel spreadsheet containing all tabular datasets plotted in Main Figures 1 to 5 (.xlsx).</li> <li>R_Scritps (.R) - Custom written R scripts to perform a classification tree analysis.</li> </ul>
Genomic Typing, Antimicrobial Resistance Gene, Virulence Factor and Plasmid Replicon Dataset for the Important Pathogenic Bacteria Klebsiella pneumoniae
<p>The infections caused by various bacterial pathogens both in clinical and community settings represent a significant threat to public healthcare worldwide. The growing resistance to antimicrobial drugs acquired by bacterial species causing healthcare-associated infections has already become a life-threatening danger noticed by the World Health Organization. Several groups or lineages of bacterial isolates usually called 'the clones of high risk' often drive the spread of resistance within particular species. </p> <p>Thus, it is vitally important to reveal and track the spread of such clones and the mechanisms by which they acquire antibiotic resistance and enhance their survival skills. Currently, the analysis of whole genome sequences for bacterial isolates of interest is increasingly used for these purposes, including epidemiological surveillance and developing of spread prevention measures. However, the availability and uniformity of the data derived from the genomic sequences often represents a bottleneck for such investigations. </p> <p>In this dataset, we present the results of a genomic epidemiology analysis of 61,857 genomes of a dangerous bacterial pathogen <em>Klebsiella pneumoniae</em> obtained from NCBI Genbank database. Important typing information including multilocus sequence typing (MLST)-based sequence types (STs), capsular (KL) and oligosaccharide (OL) types, CRISPR-Cas systems, and cgMLST profiles are presented, as well as the assignment of particular isolates to clonal groups (CG). The presence of antimicrobial resistance and virulence genes, as well as plasmid replicons, within the genomes is also reported. </p> <p>These data will be useful for researchers in the field of <em>K. pneumoniae</em> genomic epidemiology, resistance analysis and prevention measure development.</p>
Map of standing water habitat types and regionally important biotopes in Flanders
<p>This map is a combination of the <a href="https://zenodo.org/records/13865531">standardized habitat map of Flanders</a> (version habitatmap_stdized_2023_v1) and <a href="https://zenodo.org/records/14203168">the watersurface map of Flanders</a> (version watersurfaces_2024). It contains standing water Natura 2000 habitat types (2190_a and 31xx) and regionally important biotopes (rbbah) in Flanders.</p> <p>The polygons with 2190_a habitat (dune slack ponds) are generated by selecting all watersurface polygons that overlap with dune habitat polygons (21xx) of the standardized habitat map.</p> <p>For each of the other aquatic habitat types (31xx and rbbah) we select the watersurface polygons that overlap with the selected habitat type polygons of the standardized habitat map. We also select polygons of the standardized habitat map containing standing water types but that do not overlap with polygons of the watersurface map.</p> <p>The <code>watersurfaces_hab.gpkg</code> file is a GeoPackage that contains:</p> <ul> <li><code>watersurfaces_hab_polygons</code>: a spatial layer with the selected polygons that contain standing water habitat types or regionally important biotopes. </li> <li><code>watersurfaces_hab_types</code>: a table with information on standing water habitat types and regionally important biotopes in each watersurface polygon.</li> </ul> <p>The R-code for creating the <code>watersurfaces_hab</code> data source can be found in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/58138a8/src/generate_watersurfaces_hab">'n2khab-preprocessing' at commit 58138a8</a>.</p> <p>A reading function to return the data source in a standardized way into the R environment is provided by the R-package <a href="https://github.com/inbo/n2khab">n2khab</a>.</p>
scGPT: End-to-End Protocol for Fine-tuned Retina Cell Type Annotation
<h1>Abstract</h1> <p>Single-cell research faces challenges in accurately annotating cell types at high resolution, especially when dealing with large-scale datasets and rare cell populations. To address this, foundation models like scGPT offer flexible, scalable solutions by leveraging transformer-based architectures. This protocol provides a comprehensive guide to fine-tuning scGPT for cell-type classification in single-cell RNA sequencing (scRNA-seq) data. We demonstrate how to fine-tune scGPT on a custom retina dataset, highlighting the model’s efficiency in handling complex data and improving annotation accuracy achieving 99.5% F1-score. This protocol automates key steps, including data preprocessing, model fine-tuning, and evaluation. This protocol enables researchers to efficiently deploy scGPT for their own datasets. The provided tools, including a command-line script and Jupyter Notebook, simplify the customization and exploration of the model, proposing an accessible workflow for users with minimal Python and Linux knowledge. The protocol offers an off-the-shell solution of high-precision cell-type annotation using scGPT for researchers with intermediate bioinformatics.</p>
Catalog of stool metagenome-assembled genomes from patients with different cancer types
<p><strong>A non-redundant catalog of 3,816 genomes with at least 75% completeness and no more than 15% contamination assembled from metagenomes. Samples of 976 metagenomes were obtained from patients receiving immunotherapy for the treatment of different types of cancers.</strong></p>
T1D-lipidome: Database of lipidomic aberrations during the pathogenesis of type 1 diabetes (T1D)
<p>This is the<strong> living database</strong> of <strong>lipidomic aberrations</strong> during the <strong>pathogenesis of type 1 diabetes</strong> (T1D).</p> <p>The database has been collected from scientific publications that report abnormalities related to the onset of T1D. In practice, this either means:</p> <ol> <li>lipids that are aberrated in blood samples collected from persons, who are later known to have been diagnosed with T1D,</li> <li>lipids that are aberrated in blood samples collected from persons, who are have islet auto-antibodies (IAA-positive), or</li> <li>lipids that are associated with the deterioration of insulin secretion in blood samples collected from persons recently diagnosed with T1D.</li> </ol> <p>This database is described in the following publication. Please cite the publication, if you use the database or related code:</p> <p><strong>Citation</strong></p> <p>Tommi Suvitaival. <strong>Lipidomic Abnormalities During the Pathogenesis of Type 1 Diabetes: a Quantitative Review</strong>. <em>Current Diabetes Reports</em>. 20, 46 (2020). <a href="http://dx.doi.org/10.1007/s11892-020-01326-8">http://dx.doi.org/10.1007/s11892-020-01326-8</a></p> <p><strong>Acknowledgement</strong></p> <p>This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115797 (<a href="https://www.innodia.eu/">INNODIA</a>). This Joint Undertaking receives support from the Union’s Horizon 2020 research and innovation programme and “EFPIA”, ‘JDRF” and “The Leona M. and Harry B. Helmsley Charitable Trust”.</p>
Time required for typing numbers
<p>The goal of this test is to see how much time it is needed for one keystroke of a number on the keyboard. By random principle the program displays a number made of three whole digits and one or two decimals separated by comma. Upon starting the test program displays the first number. When the user finishes pressing all number keys (and a comma that separates whole from decimal digits), user has to press Enter so the next number can be displayed. The process is repeated for 5 different numbers.</p> <p>In addition, the number of errors per ten typed numbers entered for each user is counted. The Damerau–Levenshtein algorithm is used to calculate the number of errors. The Damerau–Levenshtein distance between two words is the minimum number of operations (consisting of insertions, deletions or substitutions of a single character, or transposition two adjacent characters) required to change one word into the other. Since this number is usually too small, we counted the number of errors per 10 typed numbers.</p>
The open D1NAMO dataset: A multi-modal dataset for research on non-invasive type 1 diabetes management
<p>The description of the dataset is available at <a href="https://doi.org/10.1016/j.imu.2018.09.003">https://doi.org/10.1016/j.imu.2018.09.003</a></p> <p>The usage of wearable devices has gained popularity in the latest years, especially for health-care and well being. Recently there has been an increasing interest in using these devices to improve the management of chronic diseases such as diabetes. The quality of data acquired through <a href="https://www.sciencedirect.com/topics/medicine-and-dentistry/wearable-sensor">wearable sensors</a> is generally lower than what medical-grade devices provide, and existing datasets have mainly been acquired in highly controlled clinical conditions. In the context of the <em>D1NAMO</em> project — aiming to detect <a href="https://www.sciencedirect.com/topics/medicine-and-dentistry/glycemic">glycemic</a> events through non-invasive <a href="https://www.sciencedirect.com/topics/medicine-and-dentistry/ecg-abnormality">ECG pattern</a> analysis — we elaborated a dataset that can be used to help developing health-care systems based on wearable devices in non-clinical conditions. This paper describes this dataset, which was acquired on 20 healthy subjects and 9 patients with type-1 diabetes. The acquisition has been made in real-life conditions with the <em>Zephyr BioHarness 3</em> wearable device. The dataset consists of <em>ECG</em>, <em>breathing</em>, and <em><a href="https://www.sciencedirect.com/topics/medicine-and-dentistry/accelerometer">accelerometer</a></em> signals, as well as <em>glucose</em> measurements and annotated <em>food pictures</em>. We open this dataset to the scientific community in order to allow the development and evaluation of diabetes management algorithms.</p>
Type-III Dirac fermions in HfxZr1-xTe2 topological semimetal candidate (data)
<p>This dataset contains the raw data files connected to the figures included in the paper "<em>Type-III Dirac fermions in Hf<sub>x</sub>Zr<sub>1-x</sub>Te<sub>2</sub> topological semimetal candidate</em>" by S. Fragkos et al., Journal of Applied Physics <strong>129</strong>, 075104 (2021); <a href="https://doi.org/10.1063/5.0038799">https://doi.org/10.1063/5.0038799</a></p> <p>An Open Access version of the paper can be found here: <a href="https://zenodo.org/record/4562057#.YaDC4NBBxPY">https://zenodo.org/record/4562057#.YaDC4NBBxPY</a></p>
Inputlog Copy Task Corpus: Exploring and defining typing skills
<p><strong>Context</strong></p> <p>One of the components that is included in the keystroke logging program Inputlog (<a href="https://www.inputlog.net">https://www.inputlog.net</a>) is the Copy Task component. It consists of a multi-layered set of tasks that measure a person's typing skill:</p> <table> <tbody> <tr> <td>Tapping task</td> <td>press the ‘d’ and ‘k’ key alternatively during 15 s</td> </tr> <tr> <td>Sentence</td> <td>copy a sentence during 30 s</td> </tr> <tr> <td>Word combination 1</td> <td>copy a combination of three words seven times</td> </tr> <tr> <td>Word combination 2</td> <td>copy a combination of three words seven times</td> </tr> <tr> <td>Word combination 3</td> <td>copy a combination of three words seven times</td> </tr> <tr> <td>Word combination 4</td> <td>copy a combination of three words seven times</td> </tr> <tr> <td>Consonant groups</td> <td>copy four blocks of six consonants once</td> </tr> </tbody> </table> <p>The task is currently made available in twelve languages. </p> <p>For more information: <a href="https://doi.org/10.5334/jors.234 ">https://doi.org/10.5334/jors.234 </a></p> <p> </p> <p><strong>Interactive Dashboard</strong><br> Visit the webpage with an interactive dashboard to explore, filter, and download the +5K copy task corpus.</p> <p><em><strong>website</strong></em>: <a href="https://www.inputlog.net/copy-task/">https://www.inputlog.net/copy-task/</a><br> <em><strong>dashboard</strong></em>: <a href="https://inputlog-analysis.uantwerpen.be/expert">https://inputlog-analysis.uantwerpen.be/expert</a></p> <p> </p> <p><strong>Corpus</strong></p> <p>We are happy to make a multilingual corpus available (open access) that currently consists of more than 5000 copy tasks. </p> <ul> <li>The + 5K corpus is carefully cleaned and fully anonymized.</li> <li>The Shiny interface allows users to filter the corpus based on about 10 variables.</li> <li>The selection can be downloaded in different formats and levels of aggregation (from raw idfx to synthesized analysis).</li> <li>The selection can be explored using different interactive graph visualizations.</li> <li>Researchers can upload their own corpus (or single copy task file) and compare it to the (selected) corpus.</li> <li>An extra webpage is designed for laypersons wanting to take a copy task to test their typing skills. They get dashboard feedback in a user-friendly and attractive way and can compare their performance with (age-related) participants in the corpus. (Specially designed to further expand the corpus).</li> </ul> <p><strong>Facts and Figures</strong><br> Some facts and figures about the corpus' composition:</p> <p>Languages:</p> <ul> <li>Dutch 3130 files</li> <li>English 1163 files</li> <li>German 281 files</li> <li>French 201 files</li> <li>Other 378 file</li> </ul> <p><strong>Gender</strong></p> <ul> <li>Female: 3495 files</li> <li>Male: 1276 files</li> <li>X or missing 382 files</li> </ul> <p><strong>Age</strong></p> <ul> <li>15- 439 files</li> <li>16-20 1591 files</li> <li>21-25 2427 files</li> <li>26-35 478 files</li> <li>36-45 126 files</li> <li>46+ 230 files</li> </ul> <p>A subset of the total corpus has been uploaded here. The subset contains a dataset of about 500 tests (English | 21-25-year-olds).</p> <p> </p>
Correlation Between Insulation Resistance and Temperature Measurement Error in Type K and Type N Mineral Insulated, Metal Sheathed Thermocouples
<p>Mineral insulated, metal sheathed (MI) Type K and Type N thermocouples are<br> widely used in industry for process monitoring and control. One factor that limits<br> their accuracy is the dramatic decrease in the insulation resistance at temperatures<br> above about 600 °C which results in temperature measurement errors due to electrical<br> shunting. In this work the insulation resistance of a cohort of representative MI<br> thermocouples was characterised at temperatures up to 1160 °C, with simultaneous<br> measurements of the error in indicated temperature by in situ comparison with a reference<br> Type R thermocouple. Intriguingly, there appears to be a systematic relationship<br> between the insulation resistance and the error in the indicated temperature. At<br> a given temperature, as the insulation resistance decreases, there is a corresponding<br> increasingly negative error in the temperature measurement. Although the measurements<br> have a relatively large uncertainty (up to about 1 °C in temperature error and<br> up to about 10 % in insulation resistance measurement), the trend is apparent at all<br> temperatures above 600 °C, which suggests that it is real. Furthermore, the correlation<br> disappears at temperatures below about 600 °C, which is consistent with the<br> well-established diminution of insulation resistance breakdown effects below that<br> temperature. This raises the intriguing possibility of using the as-new MI thermocouple<br> calibration as an indicator of insulation resistance breakdown: large deviations<br> of the electromotive force (emf) in the negative direction could indicate a correspondingly<br> low insulation resistance.</p>
Effects of the type of lunch meal and teaching-learning methods on CRI student engagement in afternoon classes.
<p><br> The post-lunch slump is a natural dip in energy caused not only by our circadian cycles but also as a result of digestive processes in our body after consuming lunch meals. It being a widely known concept, there are ample studies that focus on its effects on academic performance of students. While this dip in energy does affect student engagement to a large extent, there are also studies on how the teaching-learning methods are a factor affecting academic performance. However, there are no relevant studies that aim to look at both these factors in tandem, i.e. the effect of lunch along with the teaching-learning methods in the afternoon classrooms as factors influencing academic performance and student engagement. This paper aims to study this area of topic hypothesising that having a more balanced lunch along with engaging in more interactive classes would positively correlate with student engagement in afternoon classes. The study was conducted with bachelors and masters students at the CRI, University of Paris through surveys that were sent to students at the end of their class day. </p> <p>Due to lack of adequate data, the study was not able to produce significant correlations. However smaller nuances of the relationship between lunch, teaching-learning methods and student engagement were found. While engagement in classrooms did seem to be maximum when it was interactive and only 50% of students' lunch was formed of carbohydrates, no significant correlations were found to confirm the hypotheses.</p>
Spatially resolved metabolic composition in seeds of common bean: comparison of the low phytic acid mutant and the wild type
<p>Common bean (Phaseolus vulgaris L.) seeds are a good source of energy, are rich in proteins and carbohydrates, minerals and vitamins (such as Fe, Zn, B-vitamin), and bioactive compounds, such as polyphenols. However, the presence of some antinutritional compounds, such as phytic acid (PA), which decreases mineral bioavailability, can limit the nutritional value of common beans. Therefore, genotypes with low PA concentrations in common beans have been generated. The increased bioavailability of Fe from LPA mutant seeds compared to the wild type common beans was shown in a stable Fe-isotope absorption study in Swiss women, indicating that the seeds of LPA common bean could be used to help remedy the Fe malnutrition in women. Within this TNA project, we spatially resolved molecular composition in LPA mutant and wild-type common beans, particularly the distribution of PA. In total, three replicates of each genotype were analyzed with MeV-SIMS at RBI. Positive and negative modes were operated for analysis of the samples and of the standard (PA). Best spectra were obtained in negative mode, in which three distinct peaks were observed in the standard (PA): 63 m/z: PO2-, 79 m/z: PO3- and 97 m/z: H2PO4-.</p>
Uncovering major types of deforestation frontiers across the world's tropical dry woodlands
<p>These datasets provide maps of deforestation frontier classsification into three themed typologies (severity, spatio temporal patterns and development stage) and archetypes of major frontier patterns. We do this for tropical dry woodland worldwide, for the period of 2000 to 2020, at ~3-km spatial resolution (Coordinate System: WGS_1984_Mollweide, float format). Datasets used for this analysis are publicly available, forest cover and loss data are available at: https://data.globalforestwatch.org/. Deforestation frontiers metrics were calculated and typologies developed in RStudio 1.3.1056. We share the code used to develop frontier metrics, frontier typologies, and archetypes, together with a sample dataset summarized from the originally publicly available dataset.</p> <p>Further details of the datasets can be found in Buchadas et. al. (2022): https://doi.org/10.1038/s41893-022-00886-9 </p> <p>For further questions or issues with the datasets, please contact Ana Buchadas at ana.buchadas@geo.hu-berlin.de.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.