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3,937 results for “number”
Number of cases of coronavirus disease (COVID-19) in Ireland
<p>Datasets in this publication report the number of diagnoses with coronavirus disease (COVID-19) as reported by the Department of Health in Ireland. This includes new cases diagnosed per day and cumulative cases, hospitalisations, ICU admissions, deaths, number of healthcare workers, number of clusters, gender of cases, age groups of cases, mode of transmission, age groups of those hospitalised, and cases per county. To aid standardisation of age groups and cases per county, the population estimates by age group for 2019 and the actual county population in the 2016 Census from Ireland's Central Statistics Office are also included as separate datasets, to allow expression of cases per million population.</p> <p>These are </p> <ol> <li><em>doh_covid_ie_cases_analysis.csv</em>, where data from Ireland's Health Protection Surveillance Centre is included up to midnight on each included date (currently up to 16-Jun-2020). </li> <li><em>age_population_cso_2019.csv</em></li> <li><em>counties_population_cso_2016.csv</em></li> </ol> <p><em>age_population_cso_2019.csv </em>has been updated to include separate population estimates for those aged 65-74 years, 75-84 years, and 85 years and over. This is in response to the HSPC releasing case and hospitalisation data for these groups rather than a combined 65 years and over group.</p> <p><em>counties_population_cso_2016.csv </em>has been updated to remove trailing spaces in the 'county' column.</p> <p><em>doh_covid_ie_cases_analysis.csv </em>is regularly updated at <a href="https://github.com/frankmoriarty/covid_ie/blob/master/doh_covid_ie_cases_analysis.csv">https://github.com/frankmoriarty/covid_ie/blob/master/doh_covid_ie_cases_analysis.csv</a></p>
Genetic Relationships Between Terminal Shoot Length, Number of Flushes and Height in a Four-Year-old Progeny Test of Pinus brutia Ten.
<p><strong>Description of the data</strong></p> <p>A total of 188 plus trees were selected from eight natural seed stands of <em>Pinus brutia</em> in the Aegean region of Turkey. The number of trees selected per seed stand (provenance) varied between 7 to 53 trees. Open-pollinated seeds were collected from plus trees in 1998 and in 1999. In addition, six checklots consisting of bulk seeds from natural seed stands were included in the study to estimate genetic gain and link the progeny tests across different breeding zones in the Aegean region.</p> <p>Open-pollinated progeny tests were established at three locations in the Aegean region of Turkey (Hisaronu, Izmir, and Kinik) in March 2000. One-year-old bare-root seedlings were used in the study. Randomized complete block design with four-tree row plots was used in all sites. For each plus tree (female parent), about 72 half-sib progenies were planted across three test sites. The Hisaronu site had four blocks, while two other sites had seven blocks each. Each parent tree was represented by 16 half-sib progenies at the Hisaronu site and 28 progenies in the other two sites when the trials were planted. The spacing among seedlings was 2 x 3 m at each site. Each block was split into four sets (sets in replications) to accommodate a large number of trees, with checklots included in every set. In total, about 166 half-sib progenies and checklots were planted in each block.</p> <p>At the end of the first growing year after planting, survival was assessed. It was about 52% at the Hisaronu site. Dead seedlings at the Hisaronu site were replaced with 1083 two-year-old seedlings of the same families, which were grown in a nursery near Marmaris in the Aegean region. The other two sites had 91% (İzmir) and 94% (Kinik) survival. At age four after planting (2004), tree height (cm), terminal shoot length (cm), and the number of flushes were measured. In total, approximately 12100 trees were assessed across the three locations. </p>
Prevalence of Creative Commons licenses in the Directory of Open Access Journals by discipline, author fees, number of journals per country and publisher
<p>An analysis on the prevalence of Creative Commons licenses in the Directory of Open Access Journals by discipline, author fees, country and publisher according to the number of journals.</p>
Supplement 1: Full list of ICD10 codes and number of gene-disease links (tab-separated-value file); Supplement 2: Mapping (tab-separated-value file)
<p>Supplements to BioMedBridges deliverable 10.2 A prototype linking ICD10/SNOMED CT concepts to Ensembl gene identifiers:</p> <p><strong>Supplement 1</strong>: Full list of ICD10 codes and number of gene-disease links: table_icd10_gene_count_descr.tsv</p> <p><strong>Supplement 2</strong>: Mapping of disease terms: <em>ICD10_to_doid.tsv</em></p>
Numbers and shares of Open Access Journals in Sociology using Creative Commons Licenses, June 2014
<p>a) The data for the year 2014 was retrieved as a CSV-file (doaj_2014-05-07_1330_utf8.csv) from the Directory of Open Access Journals (DOAJ) homepage at 2014-06-08.<br> b) A subset of journals assigned to the subject category Sociology was generated (n=109).<br> c) I manually checked the information on CC-licenses for each of the 109 journals<br> d) Where necessary I added correct information on licenses, see column k in the CSV-file for the updated information. Column l marks entries that were updated.</p> <p> </p>
Numbers and shares of Open Access Journals from all disciplines and from the discipline Sociology using Creative Commons Licenses as listed by the Directory of Open Access Journals (2014-06-08)
<p>These files contains the data on frequencies and shares of Open Access journals using Creative Commons Licenses (comparing journals from all disciplines and sociologial journals). Date of data collection: 2014-06-08.</p>
Numbers and shares of Open Access Journals in Sociology charging publication fees (article processing charges APCs)
<p>On the eleventh of June 2014 the Directory of Open Access Journals DOAJ listed 109 sociology journals. For these journals the information on publication charges was wrong in more than 10 % of the cases. Focusing on the eleven journals that - according to the DOAJ - used (always or conditionally) APCs the situation is even worse: 10 of these journals (90,9 %) were categorized wrongly.</p> <p>In fact only three out of these 109 journals (2,75 %) are charging their authors. The files contain corrected information for the 109 journals including the sums charged by the journals.</p>
AVATAR HIGH REYNOLDS NUMBER TESTS ON AIRFOIL DU00-W-212
<p>Within EU FP7 AVATAR project (AdVanced Aerodynamic Tools of lArge Rotors), a high Reynolds number and low Mach number wind tunnel test has been performed with the aim to obtain reliable data that can be used to validate existing aerodynamic models for this operating range. The test has been performed at the DNW High Pressure Wind Tunnel in Göttingen (HDG).</p> <p> </p>
Number of bovine animals tested by age group, reporting country and target group, 2022
<p>The tables contain the number of bovine animals tested by age group, reporting country and target group, 2020.</p>
Dataset for publication "Efficient magnetic switching in a correlated spin glass", Nature Communications volume 14, Article number: 6127 (2023).
<p>Dataset for publication "Efficient magnetic switching in a correlated spin glass", Nature Communications volume 14, Article number: 6127 (2023), DOI 10.1038/s41467-023-41718-4, include images, data used for generate that images, input files, converged potential files used for the calculations on SPR-KKR package 8.6. and raw data files.</p>
A trace of microservice time, call rate, and number of replicas
<p>This is part of the runtime traces of the Alibaba Cloud clusters, which record rows of different microservice times, call rates, and number replicas over a 30-second interval.<br><br>In this dataset, starting from zero, the fifth and sixth columns show the microservice time and call rates (with the microservice ID in column 2 and the container ID in column 3).<br>Moreover, column 4 contains the number of replicas from the microservice, denoted in column 2.</p> <p>Alibaba's original microservice trace has 24 parts, from which we extracted ten and filtered based on message queue and similarity in microservice and container IDs.<br><br>Please consider that the first two columns are timestamps.</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>
Dataset for Number agreement and dependency length in Finnish dialects
<p>This material contains the dataset and the scripts from the <a href="https://version.helsinki.fi/gramadapt/depling2021-number-agreement">gitlab repository</a> of the following article. Please cite the article when using the data.</p> <p>Sinnemäki, Kaius & Akira Takaki 2021. Number agreement, dependency length, and word order in Finnish traditional dialects. In <em>Proceedings of the Sixth International Conference on Dependency Linguistics (Depling, SyntaxFest 2021)</em>. Stroudsburg, PA: The Association for Computational Linguistics.</p> <p> </p>
Dataset for configurational entropy of a finite number of dumbbells close to a wall
<p><strong>Introduction:</strong> Dataset from numerical simulations to quantify the reduction in configurational entropy of dumbbells due to the presence of a wall, as developed and described in detail in the paper</p> <ul> <li>Markus Hütter: Configurational entropy of a finite number of dumbbells close to a wall. Eur. Phys. J. E, 45(1): 6 (19 pages), 2022. DOI: 10.1140/epje/s10189-022-00160-y WWW: https://doi.org/10.1140/epje/s10189-022-00160-y</li> </ul> <p>which should be cited whenever this dataset is used. The data compiled here is the basis for figures 5, 6, and 9 in that paper.</p> <p><strong>Format</strong>: The files are provided in plain-text format (ascii).</p> <p><strong>Filenames</strong>: The nomenclature for the filenames follows the following scheme:</p> <ul> <li>data-normal-Lone{L1}-Ltwo{L2}-N{N}-{method}-{timestamp}.txt</li> </ul> <p>where (see the original paper for details) {L1} and {L2} specify the confining slab, {N} denotes the number of dumbbells, and {method} is either "SPLIT" (for the data presented in figures 5 and 6) or "WangLandau-MERGED" (for the data presented in figure 9).</p> <p><strong>File content</strong>: Each datafile contains 8 headerlines, in which the values of L1, L2, and N are repeated, and furthermore the following quantities are specified: nsteps is the total number of steps for the random sampling; clow = (4*L1^2)/N; cupp = 4*L2^2.</p> <p>After these headerlines, the data is presented in tab-delimited columns, as<br> follows for the "SPLIT"-files:</p> <ul> <li>column 1: conformation value (mid-bin position)</li> <li>column 2: number of successful placings in that bin (i.e., compatible with wall confinement)</li> <li>column 3: number of attempted placings in that bin</li> <li>column 4: (not used)</li> </ul> <p>(where the ratio of column 2 to column 3 gives the partition coefficient), whereas for the "WangLandau-MERGED"-files the columns represent the following:</p> <ul> <li>column 1: conformation value (mid-bin position)</li> <li>column 2: natural logarithm of the partition coefficient</li> </ul> <p>Details are explained in the paper mentioned above.</p>
Smooth numbers in large prime gaps
<p><strong>Overview</strong></p> <p>This dataset contains numbers from 25 up to 1 quadrillion (10<sup>15</sup>) that are smooth relative to the gap to the preceding prime. More precisely, we list all numbers <em>n</em> so that</p> <p> <em>r</em> + <em>p<sup>a</sup></em> ≤ <em>n</em></p> <p>where <em>r</em> is the largest prime smaller than <em>n</em> - 1, and <em>p<sup>a</sup></em> is the largest prime-power divisor of <em>n</em>. The dataset is the result of a 10 day computation using 15 cores on an Intel Xeon system, running code hosted at <a href="https://github.com/RussWoodroofe/invgen">GitHub</a> (see "Related identifiers"). The GitHub code checks additional conditions when <em>r</em> is <em>n</em> - 2 and <em>n </em>- 1 is a power of 2, but it is easy and quick to check that when (up to 10<sup>15</sup>) <em>n</em> = 2<sup><em>k</em></sup> + 1, the second largest prime <em>r<sub>2</sub></em> satisfies <em>r<sub>2</sub></em> + <em>p<sup>a</sup></em> > n. Thus, this additional check makes no difference in the output.</p> <p>Our motivations for computing this data are described in our paper <em>On invariable generation of alternating groups by elements of prime and prime power order</em> (<a href="http://arxiv.org/abs/2201.12371">arXiv:2201.12371</a>). Any number <em>n</em> in the range which is not of the given form has the associated alternating group <em>A<sub>n</sub></em> generated by any element of order <em>r</em> together with any element having a certain cycle structure (and of order <em>p<sup>a</sup></em>).</p> <p><strong>Description / specification</strong></p> <p>The data is stored as compressed text-based input to a computer algebra system, specifically in gzipped <a href="https://www.gap-system.org/">GAP</a> format. The file out-<em>k</em>.g.gz holds numbers in the range from (<em>k </em>- 1)⋅10<sup>12</sup> to <em>k</em>⋅10<sup>12</sup>. The first line of each file sets the variable invgen_oversmooth_range to be the range (thus, [(<em>k </em>- 1)⋅10<sup>12</sup> .. <em>k</em>⋅10<sup>12</sup>]). The subsequent lines set invgen_oversmooth to a list of pairs of numbers [<em>n</em>, <em>p<sup>a</sup></em>], where <em>n</em> is a smooth number as described above, and <em>p<sup>a</sup></em> is the largest prime-power of <em>n</em>. The largest prime preceding <em>n</em> - 1 is given in a GAP comment.</p> <p>Thus, the first few lines of out-0.g.gz (when uncompressed) appear as</p> <pre><code>invgen_oversmooth_range:=[25..1000000000000]; invgen_oversmooth := [ [ 30, 5 ], # bp 23 [ 60, 5 ], # bp 53 [ 126, 9 ], # bp 113 [ 210, 7 ], # bp 199 [ 252, 9 ], # bp 241 [ 308, 11 ], # bp 293 [ 330, 11 ], # bp 317 [ 420, 7 ], # bp 409 ...</code></pre> <p>where [25 .. 1000000000000] is the range considered, and for example "[ 30, 5 ], # bp 23" represents that 23 is the largest prime preceding 30 - 1, 5 is the largest prime-power divisor of 30, and 23 + 5 ≤ 30.</p> <p>We created the data in GAP files for ease of inputting into a GAP program in our own use of the data. It is easy to convert the GAP files to another format via standard technique such as regular expression-based search and replace. For example, on macOS or Linux, the following command will convert the list in out-0.g.gz to a CSV file, which it will display on the terminal.</p> <pre><code class="language-bash">zcat out_quadrillion/out-0.g.gz | sed -En 's/ \[ ([0-9]+), ([0-9]+) \], # bp ([0-9]+)/\1,\2,\3/gp' | less</code></pre> <p> </p>
Automatic Number Plate Recognition Data from Bristol, England for Apr-Jun 2019, 2020 and 2021
<p>These files contain data derived from Automatic Number Plate Recognition (ANPR) data collected by Bristol City Council as part of their continuous traffic monitoring work. The data was used in a project examining the impact of Covid-19 on travel behaviour in Bristol and the implications for air quality. </p> <p>Number plate data cannot be shared, therefore the files contain anonymised vehicle identifiers. Also, specific information about the locations of the ANPR cameras cannot be provided under the data sharing agreement we have with the data owners. Figures 4 and 19 in the project report (see uploaded pdf file) indicate the approximate locations of the ANPR cameras. </p> <p>The following files have been uploaded: </p> <p>- a metadata file containing descriptions of variables in the vehicle and trip files </p> <p>- a copy of the report which came out of this project. </p> <p>- BCCANPR21_REG_descrip_withID_NOVRM.csv: This file includes vehicle information for all vehicles observed in the trip data. The anonymised identification numbers can be used to merge this data with the trip files. </p> <p>- BCCANPR21_2019AprMayJun_REG_idv2.csv: Three files of this format contain trip data. Each file contains trips in April to June of the year specified in the file name. The number plate data was processed to chain together observations that were part of the same trip and this file only contains the location of the first and last ANPR camera at which this trip was recorded. </p> <p>This project was funded by the TRANSITION Clean Air Network through the first round of its Discovery & Innovation Fund in 2021. TRANSITION is a UK-wide network, led by the University of Birmingham in collaboration with nine universities and over 20 cross-sector partners, aiming to optimise the air quality and health outcomes of transport decarbonisation. The network (NERC ref. NE/V002449/1) is itself funded by UK Research & Innovation through its Clean Air Strategic Priorities Fund, administered by the Natural Environment Research Council. </p> <p>The number plate data used in this research was provided by the Bristol Traffic Control Service at Bristol City Council. The vehicle data was obtained through the DVLA’s Vehicle Enquiry Service API. </p>
Particle number concentration and meteorological measurements at Berlin-Tegel Airport during its closure
<p>Observation data of particle number concentrations (PNC) on the airfield of Berlin-Tegel Airport (TXL) during its closure. PNC was recorded with a Grimm EDM465 UFPC, including meteorological parameters measured with a Lufft WS600-UMB.</p> <ul> <li>observations between 20. October 2020, 14:44 LT and 3 December 2020, 03:03 LT</li> <li>time interval: 5 seconds</li> <li>air inlet of CPC at 1.4 m above ground</li> <li>weather sensor at 1.3 m above the ground.</li> <li>Location: 52,561 North, 13,320 East</li> <li>Variables: <ul> <li>particle number concentration in particles/cm³ (pnc)</li> <li>wind speed in m/s (ws)</li> <li>wind direction in ° (wdir)</li> <li>air temperature in °C (temp)</li> <li>relative humidity in % (rh)</li> <li>air pressure in hPa (pressure)</li> <li>precipitation in mm (pcpn)</li> <li>date as local time</li> <li>phase: whether the airport was still open ("TXL_open") or already closed ("TXL_closed")</li> <li>no data available between between 15.11.2020 02:15:00 and 18.11.2020 11:14:55 due to hardware issues</li> </ul> </li> </ul>
Code for: MHC Heterozygosity Prunes the Numbers of Different T Cell Receptors Expressed in CD4 T Cells
<p>Contains source data file and code for publication "MHC Heterozygosity Prunes the Numbers of Different T Cell Receptors Expressed in CD4 T Cells".<br>Associated FASTQ files are deposited on the NIH SRA under accession: PRJNA1106276</p>
Soil Bacteria Community-Weighted rrn Operon Copy Number Estimation
<p>Datasets and R-Scripts for estimating community-weighted rrn operon copy number for soil bacteria communities collected from the Yukon-Kuskokwim River Delta, AK, USA, and from La Selva Biological Station, Costa Rica. File descriptions follow:</p> <p>"rrnDB_copy_number_database.csv": The Ribosomal RNA Database downloaded from <a href="rrndb.umms.med.umich.edu.">rrndb.umms.med.umich.edu.</a> Citation: </p> <ul> <li>Stoddard S.F, Smith B.J., Hein R., Roller B.R.K. and Schmidt T.M. (2015) <em>rrn</em>DB: improved tools for interpreting rRNA gene abundance in bacteria and archaea and a new foundation for future development. <em>Nucleic Acids Research</em> 2014; doi: 10.1093/nar/gku1201 [<a href="http://www.ncbi.nlm.nih.gov/pubmed/25414355">PMID:25414355</a></li> </ul> <p>"AK_16S_Genus_Abundance.csv": Count of ASVs by taxon (assigned to genus level) present in each soil sample collected in the Yukon_Kuskokwim River Delta, AK, USA.</p> <p>"Costa_Rica_16S_OTU_Abundance": Count of OTUs by taxon present in each soil sample collected in La Selva Biological Station, Costa Rica.</p> <p>"Alaska_rrn_copy_number_estimation_script.R": an R script for processing Alaska ASV count table and estimating community-weighted rrn operon copy numbers for each soil sample.</p> <p>"CostaRica_rrn_copy_number_estimation_script.R": an R script for processing Costa Rica OTU count table and estimating community-weighted rrn operon copy numbers for each soil sample.</p>
Quantifying the basic reproduction number and the under-estimated fraction of mpox cases around the world at the onset of the outbreak: a mathematical modeling and machine learning- based study
<p><span>In 2022, there was a global resurgence of mpox, with different clinico-epidemiological features compared with previous</span><br><span>outbreaks. During this resurgence, sexual contact was hypothesized as the primary transmission route, with the community</span><br><span>of men having sex with men (MSM) being disproportionately affected. Because of the stigma associated with sexually</span><br><span>transmitted infections, especially those impacting MSM, the real burden of mpox could be masked.</span><br><span>We quantified the basic reproduction number (R</span><span>0</span><span>) and the under-estimated fraction of mpox cases in 16 countries, from the</span><br><span>onset of the outbreak until early September 2022, using Bayesian inference and a compartmentalized, risk-structured (high-</span><br><span>and low-risk populations), two-route (sexual and non-sexual transmission) mathematical model. Machine learning (ML) was</span><br><span>leveraged to identify under-estimation determinants.</span><br><span>Estimated R</span><span>0</span><span> </span><span>ranged between 1·37 (Canada) and 3·68 (Germany). The under-estimation rates for the high- and low-risk</span><br><span>populations varied between 25-93% and 65-85%, respectively. The estimated total number of mpox cases, relative to the</span><br><span>reported cases, is highest in Colombia (3·60) and lowest in Canada (1·08). In the ML analysis, two clusters of countries could</span><br><span>be identified, differing in terms of attitudes towards the 2SLGBTQIAP+ community and importance of religion.</span><br><span>Given the substantial mpox under-estimation, surveillance should be enhanced and campaigns against the stigmatization of</span><br><span>MSM should be organized. Countries have different social characteristics, potentially explaining the various degrees of under-</span><br><span>reporting in mpox cases, which should be considered by studies assessing the effectiveness of community-based</span><br><span>interventions.</span></p>
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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.