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1,283 results for “Copying”
FuTRES (Functional Trait Resource for Environmental Studies) data store archival copy - 5/21/2022
<p> </p> <p>The Functional Trait Resource for Environmental Studies (FuTRES) project is a collaborative project among four universities (University of Oregon, University of Arizona, University of Florida, and Howard University). The key deliverables of FuTRES are a workflow for assembling functional trait data measured at the specimen level, a database to serve that data, and scientific publications demonstrating the utility of the assembled data. This dataset represents the FuTRES datastore as of 5/21/2022, providing an archive that is timestamped and providing all data that is not currently embargoed by providers. The column headers for FuTRES data are: basisOfRecord,catalogNumber,class,collectionCode,country,decimalLatitude,decimalLongitude,diagnosticID,eventID,family,genus,individualID,institutionCode,lifeStage,locality,mapped_project,materialSampleID,maximumChronometricAge,maximumChronometricAgeReferenceSystem,maximumElevationInMeters,measurementMethod,measurementSide,measurementType,measurementUnit,measurementValue,minimumChronometricAge,minimumChronometricAgeReferenceSystem,minimumElevationInMeters,observationID,occurrenceID,occurrenceRemarks,order,reproductiveCondition,samplingProtocol,scientificName,sex,specificEpithet,stateProvince,verbatimElevation,verbatimEventDate,verbatimLatitude,verbatimLocality,verbatimLongitude,verbatimMeasurementUnit,yearCollected,projectID,inferred_traits. The traits available and number of records for each trait: </p> <ul> <li><a href="https://futres-data-interface.netlify.app/">length (1,790,883)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tail length (520,281)</a></li> <li><a href="https://futres-data-interface.netlify.app/">body length (456,165)</a></li> <li><a href="https://futres-data-interface.netlify.app/">pes length (413,668)</a></li> <li><a href="https://futres-data-interface.netlify.app/">ear length to notch (397,073)</a></li> <li><a href="https://futres-data-interface.netlify.app/">external ear length (397,073)</a></li> <li><a href="https://futres-data-interface.netlify.app/">body mass (373,949)</a></li> <li><a href="https://futres-data-interface.netlify.app/">weight (373,949)</a></li> <li><a href="https://futres-data-interface.netlify.app/">width (7,429)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus width (1,705)</a></li> <li><a href="https://futres-data-interface.netlify.app/">metacarpal bone of digit 3 proximal articular breadth (783)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 1 occlusal surface width (782)</a></li> <li><a href="https://futres-data-interface.netlify.app/">metacarpal bone of digit 3 breadth (716)</a></li> <li><a href="https://futres-data-interface.netlify.app/">metacarpal bone of digit 3 depth (706)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus length (649)</a></li> <li><a href="https://futres-data-interface.netlify.app/">long bone length (637)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary molar tooth 1 occlusal surface width (605)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus trochlea breadth (596)</a></li> <li><a href="https://futres-data-interface.netlify.app/">epiphysis width (581)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus breadth (560)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus medial depth (549)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tooth row length (498)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower tooth row length (425)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia distal width (413)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 1 occlusal surface length (402)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 4 occlusal surface width (361)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 3 occlusal surface length (343)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur width (301)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus length (297)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus width (293)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 2 occlusal surface width (261)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 2 occlusal surface length (260)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia diaphysis width (260)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 2 occlusal surface length (242)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia length (228)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia distal breadth (208)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia distal depth (201)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 2 occlusal surface width (197)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 4 occlusal surface length (187)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 4 occlusal surface width (185)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 1 occlusal surface length (184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 1 occlusal surface width (184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 3 occlusal surface width (184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary canine tooth to premolar tooth 3 length (184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 3 occlusal surface length (184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 3 occlusal surface width (184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary molar tooth 1 occlusal surface length (180)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary molar tooth 1 occlusal surface length (177)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary molar tooth 1 occlusal surface width (177)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 4 occlusal surface length (176)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 4 occlusal surface width (176)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia proximal width (162)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 3 occlusal surface length (159)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 2 occlusal surface length (155)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 2 occlusal surface width (155)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia diaphysis breadth (145)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary molar tooth 2 occlusal surface length (120)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary molar tooth 2 occlusal surface width (119)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia diaphysis depth (111)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 2 occlusal surface width (106)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 2 occlusal surface length (105)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper premolar tooth 1 occlusal surface length (105)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 1 occlusal surface length (105)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 1 occlusal surface width (105)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia proximal breadth (85)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 3 occlusal surface length (81)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 1 occlusal surface length (79)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 1 occlusal surface width (79)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary molar tooth 2 occlusal surface length (78)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary molar tooth 2 occlusal surface width (78)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus trochlea breadth (76)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus medial trochlear height (74)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus trochlear height at sagittal crest (74)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus trochlear sulcus height (74)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia proximal depth (73)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper molar tooth 1-2 length (73)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper tooth row length (73)</a></li> <li><a href="https://futres-data-interface.netlify.app/">anterior tibial tuberosity length (70)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus distal depth (69)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur length (67)</a></li> <li><a href="https://futres-data-interface.netlify.app/">ulna width (67)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia medial length (63)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus diaphysis breadth (57)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus diaphysis depth (54)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary molar tooth 3 occlusal surface length (51)</a></li> <li><a href="https://futres-data-interface.netlify.app/">trochlea tali length (49)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur diaphysis breadth (45)</a></li> <li><a href="https://futres-data-interface.netlify.app/">forelimb zeugopod bone length (45)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur distal breadth (44)</a></li> <li><a href="https://futres-data-interface.netlify.app/">ulna length (42)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur proximal breadth (40)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus length from trochlea to caput (38)</a></li> <li><a href="https://futres-data-interface.netlify.app/">calcaneus length (37)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur caput depth (37)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus length from trochlea to ventral tubercle (37)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus proximal breadth (37)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur diaphysis depth (36)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur distal depth (36)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur trochlea breadth (32)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur proximal depth (31)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 3 occlusal surface width (30)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus lateral length (30)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper molar tooth 3 occlusal surface length (30)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper molar tooth 3 occlusal surface width (30)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur length from caput to lateral condyle (28)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur length from greater trochanter to medial condyle (28)</a></li> <li><a href="https://futres-data-interface.netlify.app/">body length with tail (25)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower molar tooth 1 occlusal surface length (23)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower molar tooth 2 occlusal surface length (23)</a></li> <li><a href="https://futres-data-interface.netlify.app/">ulna depth across the process anaconaeus (23)</a></li> <li><a href="https://futres-data-interface.netlify.app/">ulna proximal articular breadth (23)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper molar tooth 1 occlusal surface length (22)</a></li> <li><a href="https://futres-data-interface.netlify.app/">olecranon depth (21)</a></li> <li><a href="https://futres-data-interface.netlify.app/">olecranon length (21)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper molar tooth 2 occlusal surface length (21)</a></li> <li><a href="https://futres-data-interface.netlify.app/">breadth of calcaneal body (18)</a></li> <li><a href="https://futres-data-interface.netlify.app/">calcaneus width (18)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia lateral length (14)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur length from caput to medial condyle (5)</a></li> <li><a href="https://futres-data-interface.netlify.app/">radius distal width (3)</a></li> <li><a href="https://futres-data-interface.netlify.app/">radius length (3)</a></li> <li><a href="https://futres-data-interface.netlify.app/">radius proximal articular width (3)</a></li> <li><a href="https://futres-data-interface.netlify.app/">radius proximal width (3)</a></li> <li><a href="https://futres-data-interface.netlify.app/">radius width (3)</a></li> <li><a href="https://futres-data-interface.netlify.app/">body height (1)</a></li> <li><a href="https://futres-data-interface.netlify.app/">height (1)</a></li> </ul>
Parish church (Parochiekerk Sint-Pieter). Triptych. Descent of the Cross ("Edelheeretriptiek"). Copy after Rogier Van der Weyden. 1443.
<u>File Name</u>: PM_141417_B_Leuven <br><u>Sublocation</u>: Parochiekerk Sint-Pieter <br><u>Location</u>: Leuven <br><u>Province</u>: Vlaams Brabant <br><u>Country</u>: Belgium <br><u>Header</u>: Triptiek "Edelheeretriptiek", school Rogier Van der Weyden, 1443 <br><u>Description</u>: Parish church (Parochiekerk Sint-Pieter). Triptych. Descent of the Cross ("Edelheeretriptiek"). Copy after Rogier Van der Weyden. 1443. <br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Author Mail</u>: PMRMaeyaert@gmail.com <br><u>Copyright</u>: © Paul M.R. Maeyaert; pmrmaeyaert@gmail.com <br><u>Keywords</u>: Europe|Belgium; Europe|Belgium|Vlaams-Brabant; Europe|Belgium|Vlaams-Brabant|Leuven; Cultural heritage <br><u>Date of Generation</u>: 2021-10-03T14:02:31+02:00
Chemical composition, soil water content and 16S rRNA and ITS gene copy numbers of soil aggregates and bulk soil samples
<p>This repository contains all data to reproduce the analyses presented in "Distinct microbial communities are linked to organic matter properties in millimetre-sized soil aggregates", Simon et al 2024, <em>The ISME Journal </em>(DOI: 10.1093/ismejo/wrae156).</p>
Raw read counts and phased SNP counts for every single cell in the sequencing datasets of the breast cancer patient S1 from "Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL"
<p>This dataset contains the raw read counts and phased SNP counts for every single cell in the sequencing datasets of breast cancer patient S1 from “Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL” [Zaccaria & Raphael, 2020]. These data enable the full reproduction of all the results in the related manuscript for breast cancer patient S1. Specifically, the data are provided in two files for every dataset <em>DAT</em> of patient S1 with the following format:</p> <ol> <li><em>DAT.raw</em>_<em>read</em>_<em>counts.bed.gz </em>is a multi-cell BED file containing the raw read counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>START: the starting genomic position of a genomic bin in the chromosome</li> <li>END: the ending genomic position of the genomic bin in the chromosome</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>NORMAL: the raw read count for the specified bin from a matched-normal sample</li> <li>COUNT: the raw read count for the specified bin in the specified cell</li> <li>RDR: the estimated read-depth ratio for the specified bin in the specified cell</li> </ul> </li> <li><em>DAT.phased</em>_<em>snps</em>_<em>counts.pos.gz </em>is a multi-cell POS file containing the phased SNP counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>POS: the genomic position in the chromosome of a germline SNP</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>COUNT_HAPLOTYPE_A: the count of reads that cover the SNP and that belong to haplotype A in the specified cell</li> <li>COUNT_HAPLOTYPE_B: the count of reads that cover the SNP and that belong to haplotype B in the specified cell</li> </ul> </li> </ol> <p>All the files have been compressed using standard <em>gzip</em>.</p>
Raw read counts and phased SNP counts for every single cell in the sequencing datasets of the breast cancer patient S0 from "Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL"
<p>This dataset contains the raw read counts and phased SNP counts for every single cell in the sequencing datasets of breast cancer patient S0 from “Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL” [Zaccaria & Raphael, 2020]. These data enable the full reproduction of all the results in the related manuscript for breast cancer patient S0. Specifically, the data are provided in two files for every dataset <em>DAT</em> of patient S0 with the following format:</p> <ol> <li><em>DAT.raw</em>_<em>read</em>_<em>counts.bed.gz </em>is a multi-cell BED file containing the raw read counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>START: the starting genomic position of a genomic bin in the chromosome</li> <li>END: the ending genomic position of the genomic bin in the chromosome</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>NORMAL: the raw read count for the specified bin from a matched-normal sample</li> <li>COUNT: the raw read count for the specified bin in the specified cell</li> <li>RDR: the estimated read-depth ratio for the specified bin in the specified cell</li> </ul> </li> <li><em>DAT.phased</em>_<em>snps</em>_<em>counts.pos.gz </em>is a multi-cell POS file containing the phased SNP counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>POS: the genomic position in the chromosome of a germline SNP</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>COUNT_HAPLOTYPE_A: the count of reads that cover the SNP and that belong to haplotype A in the specified cell</li> <li>COUNT_HAPLOTYPE_B: the count of reads that cover the SNP and that belong to haplotype B in the specified cell</li> </ul> </li> </ol> <p>All the files have been compressed using standard <em>gzip</em>.</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>
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>
High MHC gene copy number maintains diversity despite homozygosity in a Critically Endangered single-island endemic bird, but no evidence of MHC-based mate choice
<p>Raw sequence data from two amplicon libraries of MHC class I exon 3 of Raso Lark <em>Alauda razae</em>, sequenced on an Illumina Miseq. The two different libraries (two different Illumina runs) are collected in separat tar archive (.tar). Within each of those are individual sequence reads as gzipped fastq files (.fastq.gz). Each sample has two files, one for read 1 (R1) and one for read 2 (R2), with file names structured as follows. Delimited by underscore (_) are:</p> <ol> <li>sample name as referred to in the data and paper (“RingNo” in the Supporting data table);</li> <li>formal ID (also referred to in data table, often corresponding to full ring number);</li> <li>Illumina sample number (i.e. based on the order that samples are listed in the sample sheet);</li> <li>Illumina lane number (static as L001, as Miseq instruments have a single lane on their flow cells);</li> <li>read number (R1 [forward] or R2 [reverse]);</li> <li>static identifier from Illumina (001).</li> </ol> <p>Thus, the file 83304_TJ83304_S163_L001_R2_001.fastq.gz is the reverse (read 2) MHC class I exon 3 sequence of individual 83304 (ring number TJ83304).</p>
Analysis of copy number variation in dogs implicates genomic structural variation in the development of anterior cruciate ligament rupture
<p>Anterior cruciate ligament (ACL) rupture is an important condition of the human knee. Second ruptures are common and societal costs are substantial. Canine cranial cruciate ligament (CCL) rupture closely models the human disease. CCL rupture is common in the Labrador Retriever (5.79% prevalence), ~100-fold more prevalent than in humans. Labrador Retriever CCL rupture is a polygenic complex disease, based on genome-wide association study (GWAS) of single nucleotide polymorphism (SNP) markers. Dissection of genetic variation in complex traits can be enhanced by studying structural variation, including copy number variants (CNVs). Dogs are an ideal model for CNV research because of reduced genetic variability within breeds and extensive phenotypic diversity across breeds. We studied the genetic etiology of CCL rupture by association analysis of CNV regions (CNVRs) using 110 case and 164 control Labrador Retrievers. CNVs were called from SNPs using three different programs (PennCNV, CNVPartition, and QuantiSNP). After quality control, CNV calls were combined to create CNVRs using ParseCNV and an association analysis was performed. We found no strong effect CNVRs but found 46 small effect (max(T) permutation P<0.05) CCL rupture associated CNVRs in 22 autosomes; 25 were deletions and 21 were duplications. Of the 46 CCL rupture associated CNVRs, we identified 39 unique regions. Thirty four were identified by a single calling algorithm, 3 were identified by two calling algorithms, and 2 were identified by all three algorithms. For 42 of the associated CNVRs, frequency in the population was <10% while 4 occurred at a frequency in the population ranging from 10-25%. Average CNVR length was 198,872bp and CNVRs covered 0.11 to 0.15% of the genome. All CNVRs were associated with case status. CNVRs did not overlap previous canine CCL rupture risk loci identified by GWAS. Associated CNVRs contained 152 annotated genes; 12 CNVRs did not have genes mapped to CanFam3.1. Using pathway analysis, a cluster of 19 homeobox domain transcript regulator genes was associated with CCL rupture (P=6.6E-13). This gene cluster influences cranial-caudal body pattern formation during embryonic limb development. Clustered genes were found in 3 CNVRs on chromosome 14 (HoxA), 28 (NKX6-2), and 36 (HoxD). When analysis was limited to deletion CNVRs, the association was strengthened (P=8.7E-16). This study suggests a component of the polygenic risk of CCL rupture in Labrador Retrievers is associated with small effect CNVs and may include aspects of stifle morphology regulated by homeobox domain transcript regulator genes.</p>
First copy costs of scientific articles
<p>This markdown file contians information from studies and reports on the first copy costs (or pure production costs) of a scientific article. It also available in GitHub (https://github.com/scinoptica/article_costs). Please feel free to improve/ update the data or add new information in the GitHub Version.</p>
Digitized Copies of the Zurich Overnight Visitor Logs ("Nachtzedel") after 1780
<div> <div>This dataset contains digital copies - image files and metadata - of the "Zürcher Nachtzedel" (overnight visitor logs = Fremdenliste, 1780 to 1784).</div> </div>
CONGA: Copy number variation genotyping in ancient genomes and low-coverage sequencing data
<p>To date, ancient genome analyses have been largely confined to the study of single nucleotide polymorphisms (SNPs). Copy number variants (CNVs) are a major contributor of disease and of evolutionary adaptation, but identifying CNVs in ancient shotgun-sequenced genomes is hampered by (i) most published genomes being <1x coverage, (ii) ancient DNA fragments being typically <80 bps. These characteristics preclude state-of-the-art CNV detection software to be effectively applied to ancient genomes. Here we present CONGA, an algorithm tailored for genotyping deletion and duplication events in genomes with low depths of coverage. Simulations and down-sampling experiments show that CONGA can genotype deletions >1 kbps with F-scores >0.75 at >=1x, and distinguish between heterozygous and homozygous states. Using CONGA, we analyse deletion events at 10,018 loci in 56 ancient human genomes spanning the last 50,000 years, with coverages 0.4x-26x. We show that inter-individual genetic diversity measured using deletions and SNPs are highly correlated, as in modern-day genomes, confirming that deletion frequencies broadly reflect demographic history. We also identify signatures of strong purifying selection on deletions in ancient-genomes, such as an excess of singletons compared to those in SNPs. CONGA paves the way for systematic studies of drift, mutation load, and adaptation in ancient and modern-day gene pools through the lens of CNVs.</p>
Bacterial Community Weighted rrn Operon Copy Numbers and Enzyme Activity in Costa Rica and Alaska Soils.
<p>Datasets of bacterial community weighted rrn operon copy number and enzyme activity in Alaska and Costa Rica soils. The Alaska soils were collected from across four elevational terraces in a tidal wetland on the western coast. The Costa Rica soils were collected from plots subjected to precipitation manipulation treatments in La Selva Biological Research Station. </p> <p>The variable descriptions for the Alaska dataset are as follows: simple_id: an identifier variable. id: an identifier variable that includes terrace identity. transect_id: a variable that identifies collection transect location. BG_umol_g_h: the activity rate of beta-glucosiadase in micromoles per gram soil per hour. NAG_umol_g_h: the activity rate of N-acetyl-glucosaminidase in micromoles per gram soil per hour. LAP_umol_g_h: activity rate of leucine-aminopeptidase in micromoles per grams soil per hour. AP_umol_g_h: the activity rate of acid phosphatase in micromoles per gram soil per hour. BG_g_C: the activity rate of beta-glucosiadase in micromoles per gram soil carbon per hour. NAG-g_N: activity rate of N-acetyl-glucosaminidase in micromoles per gram soil nitrogen per hour. LAP_g_N: activity rate of leucine-aminopeptidase in micromoles per gram soil nitrogen per hour. AP_mg_P: activity rate of acid-phosphatase in micromoles per gram soil phosphorus per hour. P_mg_kg: soil phosphorus concentration in milligrams phosphorus per kilogram soil. K_mg_kg: soil potassium concentration in milligrams potassium per kilogram soil. C_pct: soil carbon concentration in percent. N_pct: soil nitrogen concentration in percent. pH: soil pH. cn_ratio: the carbon-to-nitrogen ratio of soil. cp_ratio: the carbon-to-phosphorus ratio of soil. np_ratio: the nitrogen to phosphorus ratio of soil. weighted_copy_number: the bacterial community weighted rrn operon copy number.</p> <p>The variable descriptions for the Costa Rica dataset are as follows: ID: a variable that identifies the soil core collected. Plot: a variable that identifies the experimental plot from which soils were collected. Precip: a variable that describes the precipitation manipulation treatment applied. Core: a variable that identifies whether soil cores were enclosed in mesh or not. Moisture: the soil moisture of the collected core in grams water per grams dry soil. weighted copy_number: the bacterial community weighted rrn operon copy number. AP: activity rate of acid-phosphatase in micromoles per gram soil per hour. BG: the activity rate of beta-glucosiadase in micromoles per gram soil per hour.</p> <p>Also included are files of the code used to analyze the data in the R Statistical Computing Environment.</p>
Data from: A social learning primacy trend in mate-copying; an experiment in Drosophila melanogaster
<p>Social learning is learning from the observation of how others interact with the environment. However, in nature, individuals often need to process serial social information and may either favour the most recent information (recency bias), constantly updating knowledge to match the environment, or the information that appeared first in the series (primacy bias), which may slow down adjustment to environmental change. Mate-copying is a widespread form of social learning in a mate choice context related to conformity in mate choice, and where a naïve individual develops a preference for a given mate (or mate phenotype) seen being chosen by conspecifics. Mate-copying is documented in most vertebrate taxa and in the fruit fly <em>Drosophila melanogaster</em>. Here, we tested experimentally whether female fruit flies show a primacy or a recency bias by presenting pictures of a female copulating with one of two contrastingly coloured male phenotypes. We found that after two sequential contradictory demonstrations, females show a tendency to prefer males of the phenotype preferred in the first demonstration, suggesting that mate-copying in <em>D. melanogaster</em> is not based on the most recently observed mating and may be influenced by a form of primacy bias.</p>
Rare Genomic Copy Number Variants Implicate New Candidate Genes for Bicuspid Aortic Valve
<p>Whole genome genotyping data in dbGAP format and copy number variant calls in dbVar format.</p> <p>dbVar data includes CNV calls from cases with early onset bicuspid aortic valve disease (EBAV), cases from the International BAV Consortium (BAVCon), and controls from the dbGAP Wisconsin Longitudinal Study on Aging dataset (WLS).</p> <p>dbGAP files are divided into 12 batches of genotypes from EBAV subjects (EBAV1-12):</p> <p>1) PLINK output files (.map and .ped)</p> <p>2) GenomeStudio Final Report files</p> <p>3) One master pedigree file</p> <p>4) dbGAP subject mapping files</p> <p> </p>
Supporting data for RCANE: A Deep Learning Algorithm for Whole-genome Pan-Cancer Somatic Copy Number Aberration Prediction using RNA-seq Data.
<p>This is the data repository for <em>RCANE: A Deep Learning Algorithm for Whole-genome Pan-Cancer Somatic Copy Number Aberration Prediction using RNA-seq Data</em>. To use this dataset, please refer to <a href="https://github.com/HowardGech/RCANE" target="_blank" rel="noopener">https://github.com/HowardGech/RCANE</a>.</p>
Analyzing the Impact of Copying-and-Pasting Vulnerable Solidity Code Snippets from Question-and-Answer Websites
<p>This data comprises all input and output, including intermediate results for the evaluation of the tool cpg-contract-checker(CCC) and the crawled data and results for the study published under the name "Analyzing the Impact of Copying-and-Pasting Vulnerable Solidity Code Snippets from Question-and-Answer Websites".<br>We conducted a study on the impact of vulnerable code reuse from Q&A websites during the development of smart contracts and provided tools uniquely fit to detect vulnerable code patterns in complete and incomplete Smart Contract code. The paper proposes a pattern-based vulnerability detection tool that is able to analyze code snippets (i.e., incomplete code) as well as full smart contracts based on the concept of code property graphs. We also propose a methodology that leverages fuzzy hashing to quickly detect code clones of vulnerable snippets among deployed smart contracts. Our results show that our vulnerability search, as well as our code clone detection, are comparable to state-of-the-art while being applicable to code snippets. The tools are used to realize a study pipeline for which the dataset and (intermediate) results are contained in this archive.</p>
Supplementary material to "Habitat detection, habitat choice copying, or mating benefits: what drives conspecific attraction in a nomadic songbird?"
<p><strong>Abstract</strong></p> <ol> <li>Conspecific attraction during habitat selection is common among animals, but the ultimate (i.e., fitness-related) reasons for this behavior often remain enigmatic.</li> <li>We aimed to evaluate the following three hypotheses for conspecific attraction during the breeding season in male Wood Warblers (<em>Phylloscopus</em> <em>sibilatrix</em>): the habitat detection hypothesis, the habitat choice copying hypothesis, and the female preference hypothesis. These hypotheses make different predictions with respect to the relative importance of social and non-social information during habitat assessment, and whether benefits accrue as a consequence of aggregation.</li> <li>We tested the above hypotheses using a combination of a two-year playback experiment, spatial statistics and mate choice models.</li> <li>The habitat detection hypothesis was the most likely explanation for conspecific attraction and aggregation in male Wood Warblers, based on the following results: 1) males were attracted to conspecific song playbacks, but fine-scale habitat heterogeneity was the better predictor of spatial patterns in the density of settling males; 2) male pairing success did not increase, but instead slightly decreased, as connectivity with other males (i.e., the number and proximity of neighboring males) increased.</li> <li>Our study highlights how consideration of the process by which animals detect and assess habitat, together with the potential fitness consequences of resulting aggregations, are important for understanding conspecific attraction and spatially clustered distributions.</li> </ol>
Dataset for: Ace and ace-like genes of invasive redlegged earth mite: Copy number variation, target-site mutations, and their associations with organophosphate insensitivity
<p class="MsoNormal">This repository contains the scripts and data required to replicate the analyses in Thia et al.'s, "Evolution of an acetylcholinesterase<em> </em>gene complex and its contribution toward organophosphate insensitivity in an invasive mite pest", submitted to <em>Pest Management Science</em>.</p> <p class="MsoNormal">In this work, Thia et al. use a combination of experimental selection and pool-seq genomic analyses to understand the genetic mechanisms underpinning organophosphate insensitivity in the redlegged earth mite, <em>Halotydeus destructor</em>. There is a special emphasis on disentangling the roles of copy number variation and target-site mutations in the acetylcholinesterase genes, <em>ace,</em> and radiated <em>ace</em>-like genes<span>.</span></p> <p class="MsoNormal">There are three major analyses: (1) an F<sub>ST</sub> genome scan to identify outlier loci between alive (insensitive) and dead (sensitive) mites; (2) an analysis of <em>ace </em>copy number variation between alive and dead mites; and (3) an analysis of candidate target-site mutations in the <em>ace</em> gene.</p>
CNETML: maximum likelihood inference of phylogeny from copy number profiles of multiple samples
<p>This folder includes simulated and real data used in validating CNETML, a new maximum likelihood method designed to reconstruct the evolutionary history of multiple samples of a single patient which may be taken at different locations and/or times, which can take as input (relative) total integer copy numbers called from shallow whole genome sequencing data.</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.