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11,177 results for “identify”
Integrating differential expression and weighted correlation network analysis for identifying genes controlling shoot development in Sorghum bicolor
<p>Supplementery materials of journal article "Integrating differential expression and weighted correlation network analysis for identifying genes controlling shoot development in <em>Sorghum bicolor</em>"</p>
Genome-wide analysis identified candidate variants and genes associated with heat stress adaptation in Egyptian sheep breeds
<p>The current study was conducted from 2009 to 2019 in three hot and dry agroecological zones in Egypt: Western Desert coastal zone, New Valley desert oasis, and hot-dry Upper Egypt. Within these zones, three local sheep breeds were studied: Barki (83 ewes), Wahati (55 ewes) and Saidi (68 ewes). During the study period, the animals exercised under natural heat stress (simulating summer grazing on poor pasture). Meteorological and physiological parameters were measured and recorded. The heat tolerance index of the animals was calculated to identify animals with high and low heat tolerance based on the animals' response to the five main physiological parameters (scale from 0 to 5). DNA samples were extracted for genomic analysis. The genetic diversity measurements showed a significant influence of breed and location on the populations. The influence of breed is more significant than that of location. The inbreeding analysis shows that the desert breeds (Wahati and Barki) have lower values than the urban breed (Saidi). The high rate of sub-clustering indicates the process of sub-population through inbreeding pressure. Wahati and Barki are very distinct breeds with strong identification, while Saidi breed has crosses with other breeds. The most significant SNPs associated with heat tolerance were found in MYO5A, PRKG1, GSTCD, and RTN1 genes (P < 0.0001). MYO5A had an effect of 0.74 on the trait heat tolerance in the studied population. It produces a protein that is widely distributed in the melanin-producing neural crest of the skin. Genetic association between genetic and phenotypic variations showed that OAR1 18300122.1, located in ST3GAL3, had the greatest positive effect on heat tolerance. GWAS analysis identified SNPs associated with heat tolerance in the PLCB1, STEAP3, KSR2, UNC13C , PEBP4, and GPAT2 genes.</p>
Identifying Priorities for the Protection of Deep Mediterranean Sea Ecosystems Through an Integrated Approach
<p>Priority deep-sea areas for conservation in the deep Mediterranean Sea from Fanelli et al., 2021. Identifying Priorities for the Protection of Deep Mediterranean Sea Ecosystems Through an Integrated Approach. Frontiers in Marine Science, 8, 698890. <a href="https://doi.org/10.3389/fmars.2021.698890">https://doi.org/10.3389/fmars.2021.698890</a></p>
Development and evaluation of a method to identify potential release areas of snow avalanches based on watershed delineation - source data
<p>Full data files for the paper:</p> <p>Duvillier, C., Eckert, N., Evin, G., and Deschâtres, M.: Development and evaluation of a method to identify potential release areas of snow avalanches based on watershed delineation, Nat. Hazards Earth Syst. Sci., 23, 1383–1408, https://doi.org/10.5194/nhess-23-1383-2023, 2023.</p> <p>Can be used to reproduce all the results of the paper and for further benchmarking of snow avalanche potential release area detection methods.</p>
BioMates - Bio-oil from ablative fast pyrolysis: Identifiers for WP3- samples and blends
<p>Ablative Fast Pyrolysis (AFP) is the first step in the BioMates-concept to convert herbaceous biomass into co-feed with reliable properties for conventional refineries (www.biomates.eu). The document provides the coding behind the identifiers used for samples and sample blends produced by RISE via AFP within the H2020-project BioMates.</p>
Ooh Na Na Alternative Identifiers
<p>A dump of the alternative identifiers extracted from the OBO Foundry as a three column TSV - prefixes, primary identifiers, and alt identifiers.</p> <p>Generated with the following code:</p> <pre><code class="language-bash">pip install pyobo pyobo database alts</code></pre> <p> </p>
Population genomics and sexual signals identify reproductive interference in Uperoleia
<p>When closely related species come into contact via range expansion, both may experience reduced fitness as a result of the interaction. Selection is expected to favor traits that minimize costly interspecies reproductive interactions (such as mismating) via a phenomenon called reproductive character displacement (RCD). Research on RCD frequently assumes secondary contact between species, but the geographic history of species interactions is often unknown. Landscape genomic data allows tests of geographic hypotheses about species origins and secondary contact through range expansion. We used landscape genomic data from single nucleotide polymorphisms (SNPs), mitochondrial sequence data, advertisement call data, and morphological data to investigate a species complex of toadlets (<em>Uperoleia borealis, U. crassa, U. inundata</em>) from northern Australia. Although the three species of frogs were morphologically indistinguishable in our analysis, we determined that <em>U. crassa</em> and <em>U. inundata</em> form a single species (synonymized here) based on an absence of genomic divergence. SNP data identified the phylogeographic origin of <em>U. crassa </em>as the Top End, with subsequent westward invasion into the range of <em>U. borealis</em> in the Kimberley. We identified six F1 hybrids, all of which had the <em>U. borealis</em> mitochondrial haplotype, suggesting unidirectional hybridization. Consistent with the RCD hypothesis, <em>U. borealis</em> and <em>U. crassa</em> sexual signals differ more in sympatry than in allopatry. Hybrid males have intermediate calls, which likely reduces attractiveness to females. Integrating landscape genomic data, mitochondrial sequencing, morphology, and behavioral approaches supplies us an unusually detailed collection of evidence for reproductive character displacement following range expansion and secondary contact.</p>
VAMDC extraction with identifier = 579afd3a-9506-447c-9d76-f294c5b9bd38
<div>This is a dataset extracted from http://tipbase.obspm.fr/12.07/vamdc/tap/ VAMDC node.</div><div>Query originating this dataset: query=select * where ( atomsymbol = 'ne' and ioncharge <= 6 and ioncharge >= 4 ); </div><div> Data source version: 2017-01-30</div><div>Data format: XSAMS 12.07</div><div>Query uuid in VAMDC query store: 579afd3a-9506-447c-9d76-f294c5b9bd38</div>
VAMDC extraction with identifier = 579afd3a-9506-447c-9d76-f294c5b9bd38
<div>This is a dataset extracted from http://tipbase.obspm.fr/12.07/vamdc/tap/ VAMDC node.</div><div>Query originating this dataset: query=select * where ( atomsymbol = 'ne' and ioncharge <= 6 and ioncharge >= 4 ); </div><div> Data source version: 2017-01-30</div><div>Data format: XSAMS 12.07</div><div>Query uuid in VAMDC query store: 579afd3a-9506-447c-9d76-f294c5b9bd38</div>
Maps of ecosystem multifunctionality and ecological connectivity for identifying Green Infrastructure networks in the European Alps
<p>High resolution raster datasets (20 meters) containing the results of an ecological connectivity and an ecosystem multifunctionality assessment for identifying Green Infrastructure networks in 10 pilot regions of the European Alps, modelled as part of the LUIGI Interreg Alpine Space project. Pilot regions include: department of Isère (FR), departments of Savoie and Haute-Savoie (FR), Munich Metropolitan Region (DE), Central Area of Salzburg (AT), South Burgenland (AT), Goriška region (SI), South Tyrol (IT), canton of Grisons (CH), Metropolitan City of Milan (IT), and Metropolitan City of Turin (IT). For a preview of the data and the results available for each pilot region <a href="https://www.alpine-space.org/projects/luigi/en/project-results/d.t1.2.1-pilot-regions-policy-briefs">click here</a></p> <p>Further information on the LUIGI project is available at: <a href="https://www.alpine-space.org/projects/luigi/en/home">https://www.alpine-space.org/projects/luigi/en/home</a></p> <p><a href="https://webassets.eurac.edu/31538/1661510408-luigi-wp1-technical-annex-mapping-a-green-infrastructure-network-in-the-alpine-space.pdf">https://webassets.eurac.edu/31538/1661510408-luigi-wp1-technical-annex-mapping-a-green-infrastructure-network-in-the-alpine-space.pdf </a></p> <p>The datasets include:</p> <ul> <li>a map for ecosystem service-based multifunctionality calculated out of the average of 11 standardized ecosystem service indicators: water provision, crop potential, timber production, fodder provision, pollination potential, carbon sequestration, nitrogen retention, natural hazard mitigation, runoff retention, outdoor recreation, and landscape aesthetics.</li> <li>a map of the modelled Ecological Network composed of core areas and ecological corridors. Corridors are modelled for medium-large forest mammal species and represent least-cost pathways connecting core areas. Different classes indicate areas with different levels of current ecological connectivity starting from core areas to areas in cities or anthropized land with no connectivity. Modeled corridors are presented in two classes to mirror different levels of prioritization and management actions.</li> <li>a map of the resistance of the landscape to the movement of forest mammal species. The landscape resistance raster has been developed by reclassifying and aggregating a high resolution (5m) land use and land cover map. Resistance values have been determined in relation to the naturalness of different land use and land cover classes. In this context, land use or landscape resistance is intended as the opposite of habitat suitability.</li> </ul>
Derby database for mapping secondary to primary HMDB identifiers
<p>The data (hmdb_metabolites, released on 17/11/2021) used to create this ID mapping database was downloaded from HMDB (<em>Human Metabolome Database, </em>website URL: https://hmdb.ca/). </p> <p>This database was used for the <a href="https://github.com/tabbassidaloii/BridgeDbDemoBioSB2022">BridgeDb demo at BioSB 2022</a> conference.</p> <p>The scripts used to create this database based on HGNC: https://github.com/tabbassidaloii/create-bridgedb-secondary2primary</p> <p>This work was funded by the <a href="https://fairplus-project.eu/">FAIRplus project</a> (grant agreement no 802750) and <a href="https://www.nwo.nl/en/researchprogrammes/open-science/open-science-fund/open-science-fund-2021-awarded-grants">NWO Open Science Fund</a> (grant no <a href="https://www.nwo.nl/en/projects/203001121">203.001.121</a>).</p>
Comparison of methods to identify and monitor mold damages in buildings
<p>Molds thrive in indoor environments challenging the stability of building materials and occupants’ health. Diverse sampling and analytical techniques can be applied in microbiology of buildings with specific benefits and drawbacks. We evaluated the use of two methods, microscopy of visible mold growth (tape lifts) and DNA metabarcoding of mold and dust samples (swabs), for mapping mold-damage indicator fungi in buildings in Oslo. Overall, both methods provided consistent results for mold samples, where nearly 80% of the microscopy-identified taxa were confirmed by DNA analysis. <em>Aspergillus </em>was the most abundant genus colonizing all materials, while some taxa were associated with different substrates: <em>Acremonium </em>with gypsum board, <em>Chaetomium</em><em> </em>with chipboard, <em>Stachybotrys </em>with gypsum board and wood, and <em>Trichoderma</em> with wood. Based on DNA data, community composition was clearly different between mold and dust with a much higher alpha diversity in dust. Most genera identified in mold were also detected with a low abundance in dust from the same apartments. Their spatial distribution indicated some local spread from the mold growth to other areas, but there was no clear correlation between relative abundances and the distance to the damages. To study mold damages, different microbiological analyses (microscopy, cultivation, DNA and chemistry) should be combined with a thorough inspection of buildings. The interpretation of such datasets requires the collaboration of skilled mycologists and building consultants.</p>
Subtyping of common complex diseases and disorders by integrating heterogeneous data. Identifying clusters among women with lower urinary tract symptoms in the LURN study
<p>We present a methodology for subtyping of persons with a common clinical symptom complex by integrating heterogeneous continuous and categorical data. We illustrate it by clustering women with lower urinary tract symptoms (LUTS), who represent a heterogeneous cohort with overlapping symptoms and multifactorial etiology. Data collected in the Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN), a multi-center observational study, included self-reported urinary and non-urinary symptoms, bladder diaries, and physical examination data for 545 women. Heterogeneity in these multidimensional data required thorough and non-trivial preprocessing, including scaling by controls and weighting to mitigate data redundancy, while the various data types (continuous and categorical) required novel methodology using a weighted Tanimoto indices approach. Data domains only available on a subset of the cohort were integrated using a semi-supervised clustering approach. Novel contrast criterion for determination of the optimal number of clusters in consensus clustering was introduced and compared with existing criteria. Distinctiveness of the clusters was confirmed by using multiple criteria for cluster quality, and by testing for significantly different variables in pairwise comparisons of the clusters. Cluster dynamics were explored by analyzing longitudinal data at 3- and 12-month follow-up. Five clusters of women with LUTS were identified using the developed methodology. None of the clusters could be characterized by a single symptom, but rather by a distinct combination of symptoms with various levels of severity. Targeted proteomics of serum samples demonstrated that differentially abundant proteins and affected pathways are different across the clusters. The clinical relevance of the identified clusters is discussed and compared with the current conventional approaches to the evaluation of LUTS patients. The rationale and thought process are described for the selection of procedures for data preprocessing, clustering, and cluster evaluation. Suggestions are provided for minimum reporting requirements in publications utilizing clustering methodology with multiple heterogeneous data domains.</p>
A comparison of different methods of identifying publications related to the United Nations Sustainable Development Goals: Case Study of SDG 13: Climate Action
<p>This data set pertains to the following research article: Purnell, P.J. (2022) <em>A comparison of different methods of identifying publications related to the United Nations Sustainable Development Goals: Case Study of SDG 13 – Climate Action</em>. arXiv:2201.02006</p>
GENeSYS-MOD Transport Sensitivities: Data and model code for Hainsch (preprint): Identifying policy areas for the transition of the transportation sector
<p>This dataset contains all GENeSYS-MOD input data for Hainsch (preprint): Identifying policy areas for the transition of the transportation sector. doi: 10.5281/zenodo.6919452.</p> <p>With the input data files and the GAMS files, the model results presented in the preprint can be replicated.</p> <p>Furthermore, the output folder contains the result files for the base case and all sensitivities as well as the Tableau files which were used to generate the result figures.</p>
IDENTIFYING AND MITIGATING CONGESTION ONSET (Project J3)
<p>These data were used in the Sacramento and Tampa case studies as described in the final report of the STRIDE J3 project.</p>
Text-fig. 6. Stratigraphic and phylogenetic placement inferred for fossil Fraxinus fruits. Only Fraxinus fossil fruits identified on the section level are included. The black color represents selected fossil fruits from published literature (excluding some Eocene North American occurrences not assigned to section), the red color represents the fossil fruits from the Lühe flora, Yunnan, Southwest China. The phylogenetic relationships are based on Hinsinger et al. (2013). in Fraxinus L. (Oleaceae) Fruits From The Early Oligocene Of Southwest China And Their Biogeographic Implications
Text-fig. 6. Stratigraphic and phylogenetic placement inferred for fossil Fraxinus fruits. Only Fraxinus fossil fruits identified on the section level are included. The black color represents selected fossil fruits from published literature (excluding some Eocene North American occurrences not assigned to section), the red color represents the fossil fruits from the Lühe flora, Yunnan, Southwest China. The phylogenetic relationships are based on Hinsinger et al. (2013).
Text-fig. 5. Mastixiopsis nyssoides KIRCHH. a, b, g–n: Organic preservation. a, b: Lignitic, unpermineralized, early Eocene Dorset Pipe clays at Arne, V. 40762. a: Ventral view (original illustration from pl. 18, fig. 1 of Chandler 1962). b: Transverse fracture, somewhat distorted by compression. c–f: Pyrite permineralization. c: Ventral view, V. 22963(1) from Sheppey, originally listed as Mastixia cantiensis. d: Lateral view, V. 22969 from Sheppey (identified as Mastixia grandis by Reid and Chandler 1933: pl. 25, fig. 8). e: Equatorial transverse physical section from (c). f: Equatorial transverse physical section from (d). g: Detail of pericarp from (e), showing endocarp formed of dense fibrous tissue, surrounded by mesocarp of anticlinally oriented larger cells. h: Detail of pericarp from (f). i–n: Type material from Eocene of Riestadt, Germany, MNB. i: Ventral view. j, k: Ventral and apical views of holotype. l: View of the transversely fractured surface from (j) showing horseshoe shaped locule. m: Equatorial transverse physical cut of the specimen in (i); note yellow resin cavity (arrow). n: Scanning electron microscopy of pericarp from (l) with locule lining at lower edge of image. Note dense endocarp tissue composed of small cells (fibres and sclereids), extending about 3/5 of distance to periphery, surrounded by mesocarp of larger, anticlinally oriented cells. Scale bars 1 cm in (a–f), (i–k), 1 mm in (g), 2 mm in (h), 3 mm in (l), m, 250 Μm in (n). Bar in (d) applies also to (c). Bar in (l) also applies to (m). Bar in (i) also applies to (j) and (k). in Mastixioid Fruits (Cornales) From The Early Eocene London Clay Flora: Morphology, Anatomy And Nomenclatural Revision
Text-fig. 5. Mastixiopsis nyssoides KIRCHH. a, b, g–n: Organic preservation. a, b: Lignitic, unpermineralized, early Eocene Dorset Pipe clays at Arne, V. 40762. a: Ventral view (original illustration from pl. 18, fig. 1 of Chandler 1962). b: Transverse fracture, somewhat distorted by compression. c–f: Pyrite permineralization. c: Ventral view, V. 22963(1) from Sheppey, originally listed as Mastixia cantiensis. d: Lateral view, V. 22969 from Sheppey (identified as Mastixia grandis by Reid and Chandler 1933: pl. 25, fig. 8). e: Equatorial transverse physical section from (c). f: Equatorial transverse physical section from (d). g: Detail of pericarp from (e), showing endocarp formed of dense fibrous tissue, surrounded by mesocarp of anticlinally oriented larger cells. h: Detail of pericarp from (f). i–n: Type material from Eocene of Riestadt, Germany, MNB. i: Ventral view. j, k: Ventral and apical views of holotype. l: View of the transversely fractured surface from (j) showing horseshoe shaped locule. m: Equatorial transverse physical cut of the specimen in (i); note yellow resin cavity (arrow). n: Scanning electron microscopy of pericarp from (l) with locule lining at lower edge of image. Note dense endocarp tissue composed of small cells (fibres and sclereids), extending about 3/5 of distance to periphery, surrounded by mesocarp of larger, anticlinally oriented cells. Scale bars 1 cm in (a–f), (i–k), 1 mm in (g), 2 mm in (h), 3 mm in (l), m, 250 Μm in (n). Bar in (d) applies also to (c). Bar in (l) also applies to (m). Bar in (i) also applies to (j) and (k).
Dataset for the paper "A Dataset and Baseline Approach for Identifying Usage States from Non-Intrusive Power Sensing With MiDAS IoT-based Sensors"
<p>The state identification problem seeks to identify power usage patterns of any system, like buildings or factories, of interest. In this challenge paper, we make power usage dataset available from 8 institutions in manufacturing, education and medical institutions from the US and India, and an initial unsupervised machine learning based solution as a baseline for the community to accelerate research in this area.</p> <p>Additional data for more days (from January-August 2022) for the same locations presented in our paper can be requested for research purposes by contacting the authors.</p> <p>Our GitHub repository - https://github.com/ai4society/PowerIoT-State-Identification</p> <p>If you are using this data, please cite,</p> <blockquote> <pre>@inproceedings{midas-state-id, author = {Bharath C Muppasani and C J Anand and Chinmayi Appajigowda and Biplav Srivastava and Lokesh Johri}, title = {A Dataset and Baseline Approach for Identifying Usage States from Non-Intrusive Power Sensing With MiDAS IoT-based Sensors}, booktitle = {Proc. Thirty-Fifth Annual Conference on Innovative Applications of Artificial Intelligence (AAAI/IAAI-23)}, year = {2023}, keywords = {Signal Processing (eess.SP), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Computer and information sciences}, copyright = {Creative Commons Attribution Non Commercial No Derivatives 4.0 International} }</pre> </blockquote>
Data from: Andriollo T., Ruedi M. (2018). Novel molecular tools to identify Plecotus bats in sympatry and a review of their distribution in Switzerland. Revue suisse de Zoologie 125(1)
<p><strong>Supporting data for:</strong> Andriollo T., Ruedi M. (2018). Novel molecular tools to identify <em>Plecotus</em> bats in sympatry. Revue suisse de Zoologie 125(1): 61-72. https://doi.org/10.5281/zenodo.1196013</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.