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30 results for “Collation”
Collation and orthology-based identification of hormone-related genes in bread wheat
<p>Plant hormones coordinate a plethora of developmental processes in plants, including responses to abiotic and biotic stressors. Here, we collate the findings of previous studies identifying bread wheat (<em>Triticum aestivum</em>) genes related to hormonal processes (<strong>biosynthesis</strong>, <strong>transport</strong>, <strong>signalling</strong>, and <strong>catabolism</strong>) and collect wheat orthologues from hundreds of additional hormone-related genes utilising the Ensembl Plants Compara database. We have initially conducted this procedure for <strong>abscisic acid</strong>, <strong>auxins </strong>(IAA and IBA), <strong>brassinosteroids</strong>, <strong>cytokinins</strong>, <strong>ethylene</strong>, <strong>gibberellins</strong>, and <strong>strigolactone</strong>, yielding a total of over 1,700 putative wheat orthologues. We aim to provide a community resource to aid gene annotation and subsequent analyses. We warmly welcome feedback from the community.</p> <p>Please refer to the file <strong>README.pdf</strong> for further details, including methods and references.</p>
Collated set of all gravitational wave observations (to current date) from Wikipedia
<p>This is a dataset I needed but couldn't find. So, with the help of Chat GPT (and several hours ot time), I compiled this easy to access html file.</p> <p>It's not perfect but here it is for all of you to enjoy. </p>
Population size, HIV prevalence, and antiretroviral therapy coverage among key populations in sub-Saharan Africa: collation and synthesis of survey data 2010-2023
<p>This dataset contains surveillance study estimates for population size, HIV prevalence, and ART coverage among female sex workers (FSW), men who have sex with men (MSM), people who inject drugs (PWID), and transgender men and women (TGM/W) from 2010-2023. It was created to support the UNAIDS Estimates Key Population Workbook for use by HIV estimates teams in sub-Saharan Africa. Key population surveillance reports, including Ministry of Health-led biobehavioural surveys, mapping studies, and academic studies were used to populate the database.</p> <p>The dataset was populated using existing key population size estimate databases including:</p> <ul> <li>UNAIDS Key Population Atlas</li> <li>US Centers for Disease Control and Prevention surveillance database</li> <li>Global Fund against HIV/AIDS, TB, and Malaria surveillance database</li> <li>Global.HIV database</li> <li>Systematic review databases among MSM (<a href="https://pubmed.ncbi.nlm.nih.gov/31601542/" target="_blank" rel="noopener">Stannah et al, 2019</a> and <a href="https://pubmed.ncbi.nlm.nih.gov/37453439/" target="_blank" rel="noopener">Stannah et al., 2023</a>) and PWID (<a href="https://pubmed.ncbi.nlm.nih.gov/36996857/" target="_blank" rel="noopener">Degenhardt et al., 2023</a>)</li> </ul> <p><br>and was additionally supplemented by a literature review of peer-reviewed and grey literature sources.</p> <p>The data can be <a href="https://shiny.dide.ic.ac.uk/kp-data/" target="_blank" rel="noopener">explored in this web application</a> and the <a href="https://www.medrxiv.org/content/10.1101/2022.07.27.22278071v3" target="_blank" rel="noopener">accompanying manuscript can be found here</a></p>
Wordlist files of lexical data from Papua New Guinea and western Solomons Oceanic languages collated for Ross's 1986 PhD thesis and 1988 publication thereof
<p>It occurs to me that the files containing Western Oceanic lexical data that I collected in the late 70s/early 80s for my PhD (Ross 1988) might be useful to someone. They are also used in the volumes of <em>The lexicon of Proto Oceanic </em>(Ross, Pawley & Osmond 1998, 2003, 2011, 2016, 2023). In any case, it is right that they be made publicly available, something that wasn't so easy back then. Most of the material is from wordlists that I collected during fieldwork in Papua New Guinea from around 1978 to 1982. The file cor06 is omitted because it contains SE Solomonic data (outside Western Oceanic) drawn from Tryon & Hackman 1983.</p> <p>I keyed the data into text files in a format such that each line was the entry for a single word, and each field within an entry was marked by a backslash code (I adapted this format from SIL's conventions at the time), then arranged them in cognate sets, each set separated from the next by an empty line. This work was done between 1983 and 1985, when text files were the best way to store data. They were entered on a terminal connected to a mainframe computer at the ANU. I have converted the ASCII symbols used in the original files into UTF-8 here in the interests of readability. The conversion was largely automatic, and I have not done a full check of each file, so there may be glitches.</p> <p>Each file contains languages from a region, as listed below (and the regions sometimes cut across subgroups determined by the comparative method). Three-letter abbreviations are used for language names, and two key files are also provided, one (COR-abbrevs) ordered by regions (determined by the numerals that start each line), the other by alphabetical order of language name (COR-abbrevs-alph). Some three-letter codes are followed by a hyphen and an extra letter. These are dialects. For example, MUM stands for Mumeng and MUM-P for the Patep dialect of Mumeng.</p> <p>Data files are labelled with COR (for 'correspondence sets') plus a numeral. The numerals are: 1-3 New Ireland; 4 Willaumez Peninsula (New Britain) area; 5 NW Solomonic; 7+8 Papuan Tip; 9 Vitiaz Strait area and NG north coast; 10 Huon Gulf and Markham Valley; 11 South and west New Britain. 7+8 are partial only. When I keyed the files, I had to rely on a mainframe's nightly back-up onto tape spools. One night the system failed, and so did the restore, and I lost some data.</p> <p>The backslash codes in the data files are: \l language; \p protolanguage; \w word; \g gloss; \n note; \s source. The formatting of these files is a little odd, since they served as input to routines I wrote to pull out sound correspondences. Anything after '%' is the elicited form: what immediately precedes '%' has had something 'undone', e.g. metathesis.</p> <p>The orthography of the files is phonemic and largely obvious. The conventions are set out in the introductions to the volumes of <em>The lexicon of Proto Oceanic.</em></p> <p>Finally, the files also contain reconstructions at various interstages at the top of a cognate set. These were inserted for heuristic reasons during my research. Many of them did not survive into my PhD thesis, and they should preferably be ignored. The reader who is interested in current Oceanic reconstructions should turn to the volumes of <em>The lexicon of Proto Oceanic.</em></p>
The Marginal Latin (LaM) readings of 2 Samuel - The collation file
<p>The collected cases of Marginal Latin (LaM) readings in 2 Samuel. Made at the time of sending the draft of the article to a publisher for an evaluation.</p>
དབའ་བཞེད་ collated with རྦ་བཞེད : version 1.0
<p>Collation of DBA’ (2000) and RBA (2011.1): version 1.0 [with some translation notes inserted as a guide]</p>
British Library fragment Or.8210/S.9498 collated with དབའབཞེད་ (version 1.1)
<p>Comparison of BL S.9498+S.13683 with the DBA' BZHED MS (=DBA' 2000 in our referencing system). The purpose of the comparison is three fold: a) to tentatively reconstruct the disposition of lines and content across the folio from which the fragment came, b) to determine if there is enough space in the reconstructed folio to accommodate the names of the three ministers sent to investigate Śāntarakṣita, and c) to determine the likely position of ན་ visible as a tail in the top missing line.</p>
In search for Old Greek readings: The Old Latin manuscript Palimpsestus Vindobonensis (La115) in 2 Samuel - the collation file for SBL 2021
<p>Collected cases for my SBL 2021 paper "In search for Old Greek readings: The Old Latin manuscript Palimpsestus Vindobonensis (La115) in 2 Samuel"</p>
Quedlinburg Itala (La116) - collation file of characteristics readings
<p>The collated cases of the whole fragmentary Old Latin manuscript Quedlinburg Itala (La116)</p>
Demonstration of computer assisted scholarly collation
<p>Demonstration of computer assisted scholarly collation, using the collation of General Prologue 488 in the Textual Communities interface. The software here used is a customization of the Collation Editor, written by Cat Smith of the Institute for Textual Scholarship and Electronic Editing at the University of Birmingham, with assistance from Troy Griffitts, itself an extension of the CollateX program, written by Ronald Dekkers and others.</p>
Restoration efforts of the seagrass Posidonia oceanica: a collated evidence review dataset
<p><span>Seagrass meadows are important shallow coastal ecosystems due to their contribution to enhancing biodiversity, nutrient cycling, carbon burial, and sediment stabilisation, but the maintenance of their integrity has been threatened by several anthropogenic disturbances. Active restoration is considered a reliable strategy to enhance recovery of seagrass ecosystems, and decision making for correct seagrass restoration management requires relying on valuable information regarding the effectiveness of past restoration actions and experimental efforts. </span>Previous experimental efforts and human-mediated active restoration actions of the slow growing seagrass <em>Posidonia oceanica</em> have been collated here by combining a literature systematic review and questionnaires consulting seagrass ecology experts. Overall, the poor consistency of the available information on <em>P. oceanica</em> restoration may be due to the wide portfolio of practices and methodologies used in different conditions, that supports the need of further field manipulative experiments in various environmental contexts to fill the identified knowledge gaps. The current situation requires an international, collaborative effort from scientists and stakeholders to jointly design the future strategy forward in identifying the best practices that lead to efficient restorations of <em>P. oceanica</em> habitat and functioning.</p>
LSTbase - a collation of Laacher See tephra occurrences
<p>LSTbase is a collation - from the literature - of locations at which Laacher See ejecta has been identified.</p> <p>The data are presented 'as is' with information taken from the published literature. The focus has been strictly on point-like occurrences in the medial and distal fields. For proximal mappings of tephra blankets and PDCs please consult the many publications by the late Hans-Ulrich Schmincke and collaborators. In addition, German soil maps also contain information on such blanket deposits (https://geoportal.bgr.de/mapapps/resources/apps/geoportal/index.html?lang=en#/geoviewer).</p> <p> </p> <p>Laacher See tephra occurrences are by default assumed to be the result of airfall; in some case - chiefly in relation to deposits along the River Rhine - they are classed as fluvially deposited, however. Please consult the original publications for further information.</p> <p>Whenever the information is provided in the original publications, section or core labels are given.</p> <p> </p> <p>Coordinates are provided in WGS84; not all locations are precise.</p> <p> </p> <p>For additional datapoints or corrections, please contact f.riede@cas.au.dk</p> <p> </p> <p>An earlier version of this dataset was published in Tephrabase (https://www.tephrabase.org/laacherSee.html) and reported in Riede, Felix, Oliver Bazely, Anthony J. Newton, and Christine S. Lane. “A Laacher See-Eruption Supplement to Tephrabase: Investigating Distal Tephra Fallout Dynamics.” Quaternary International 246, no. 1–2 (2011): 134–44. https://doi.org/doi: 10.1016/j.quaint.2011.06.029.</p>
Restoration efforts of the seagrass Posidonia oceanica: a collated evidence review dataset
Open the record for dataset details and reuse information.
Collated reference lichen genomes
<p>Reference genomes concatenated into files for each lichen family - sourced from NCBI and JGI. <br>Details on how databases were created fully available on the main <a href="https://github.com/Kamouyiaraki/DEFRALichens/tree/main/databases">project github</a></p>
Dataset for a cross-sectional study collated from June 2019 to July 2022 among patients visiting Kalkaal Hospital, Mogadishu, Somalia
<p>This is a dataset for a cross-sectional study collated from June 2019 to July 2022 among patients visiting Kalkaal Hospital, Mogadishu, Somalia. All individuals were evaluated for the presence of Hepatitis B surface antigen (HBsAg) within one hour of sample collection using fast chromatographic immunoassays. Using ELISA equipment for quantitative testing, positive individuals were confirmed. Similarly, a Hepatitis C blood test was performed using a rapid and a PCR test to validate the result. The Statistical Package for Social Science (SPSS) version 20 was used as the analysis tool.</p>
OCO-2 Level 1A collated, parsed, science or calibration data, Retrospective Processing V10r (OCO2_L1aIn_Sample) at GES DISC
Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r. Version 8r is the current version of the data set. Version 7r has been superseded by Version 8r. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectral elements, although some are masked out in the L2 retrieval.This product is the input to the Level 1B process. It is depacketized raw data formatted into a standard granularity with calibrated engineering data (for both science and calibration observations), in the Sample Mode of operation.This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.
OCO-2 Level 1A collated, parsed, calibration data V11 (OCO2_L1aIn_Pixel) at GES DISC
Version 11 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectral elements, although some are masked out in the L2 retrieval.This product is the input to the Level 1B process. It is depacketized raw data formatted into a standard granularity with calibrated engineering data (for both science and calibration observations), in the Single-pixel Mode of operation.
OCO-3 Level 1A collated, parsed, science or calibration data V11 (OCO3_L1aIn_Sample) at GES DISC
Version 11 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-3 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectral elements, although some are masked out in the L2 retrieval.This product is the input to the Level 1B process. It is depacketized raw data formatted into a standard granularity with calibrated engineering data (for both science and calibration observations), in the Sample Mode of operation.
OCO-3 Level 1A collated, parsed, science or calibration data, Retrospective Processing V11r (OCO2_L1aIn_Sample) at GES DISC
Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-3 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectral elements, although some are masked out in the L2 retrieval.This product is the input to the Level 1B process. It is depacketized raw data formatted into a standard granularity with calibrated engineering data (for both science and calibration observations), in the Sample Mode of operation.This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.
OCO-3 Level 1A collated, parsed, science or calibration data, Retrospective Processing V11r (OCO3_L1aIn_Pixel) at GES DISC
Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-3 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time to the Level 1B process as Level 1A products. Each band has 1016 spectral elements, although some are masked out in the L2 retrieval.This product is the input to the Level 1B process. It is depacketized raw data formatted into a standard granularity with calibrated engineering data (for both science and calibration observations), in the Sample Mode of operation.This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.
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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.