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3,172 results for “Publications”
SPARCS_WP4_Leipzig_City_Percentage of people going to work using public transport
<p>The percentage of people in the city of Leipzig who commute to and from their workplace by public transport; data captured annually covering the period from 2016 to 2022</p>
SPARCS-WP3_Espoo_City_Number of (semi)public EV charging points in Espoo area
<p>Number of (semi)public EV charging points in Espoo area.</p>
SPARCS-WP3_Espoo_City_Citizens using public transportation to go to work
<p>Percentage of people using public transportation for commuting.</p>
Data supporting the publication of "Interactive effects of climate change and land-use change on mammal range retraction in Great Britain"
<p><strong>Table S1 (Species records) provided as a separate .xlsx file in Supporting Information. </strong>List of species included in the sample with corresponding attributes, number of records and rates of change over time.</p> <p>Column A (Scientific name): species’ accepted scientific name (n = 43 species).</p> <p>Column B (Common name): species’ common name in Great Britain (n = 43 names).</p> <p>Column C (Order): species’ taxonomical Order (n = 6 Orders).</p> <p>Column D (Family): species’ taxonomical Family (n = 14 Families).</p> <p>Column E (Guild): species’ sampling guild (n = 3 Guilds, either Bats, Midlarge, or Small</p> <p>Column F (Distribution): species’ distribution status in Great Britain (n = 3 Statuses, either Native, Naturalised, or Non-Native).</p> <p>Column G (Habitat): species’ habitat preference (n = 2 Habitats, either Terrestrial or Freshwater).</p> <p>Column H (Records): total number of records per species from 1960 to 2016 (average = 10,931).</p> <p>Column I (1960s): total number of records per species from 1960 to 1969 (average = 420).</p> <p>Column J (1970s): total number of records per species from 1970 to 1979 (average = 457).</p> <p>Column K (1980s): total number of records per species from 1980 to 1989 (average = 423).</p> <p>Column L (1990s): total number of records per species from 1990 to 1999 (average = 641).</p> <p>Column M (2000s): total number of records per species from 2000 to 2010 (average = 943).</p> <p>Column N (2010s): total number of records per species from 2011 to 2016 (average = 870).</p> <p>Column O (Hectads TP1): number of hectads where the species has been recorded in Time Period 1, from 1960 to 1992 (average = 892).</p> <p>Column P (Hectads TP2): number of hectads where the species has been recorded in Time Period 2, from 2000 to 2016 (average = 1,117).</p> <p>Column Q (Hectads Total): number of hectads where the species has been recorder from 1960 to 2016 (average = 1,315).</p> <p>Column R (Extirpation rate): species’ extirpation rate, calculated as the ratio of extirpations over the sum of extirpations and persistences (average = 0.24). The sum of extirpation and persistence rates is always equal to 1.</p> <p>Column S (Persistence rate): species’ persistence rate, calculated as the ratio of persistences over the sum of extirpations and persistences (average = 0.76). The sum of persistence and extirpation rates is always equal to 1.</p> <p>Column T (Occupancy TP1): species’ occupancy estimate in Time Period 1, from 1960 to 1992, as calculated in Frescalo (average = 0.395).</p> <p>Column U (Occupancy TP2): species’ occupancy estimate in Time Period 2, from 2000 to 2016, as calculated in Frescalo (average = 0.403).</p> <p>Column V (Occupancy change): change in the species’ occupancy estimates between Time Periods 1 and 2, as calculated in Frescalo (average = 0.076).</p> <p>Column W (Occupancy change slope): average yearly change in the species’ occupancy estimates from 1960 to 2016, as calculated in Frescalo (average = -0.001).</p> <p>Column X (Frequency TP1): adjusted frequency of occurrence in Time Period 1, from 1960 to 1992, as calculated in Frescalo (average = 0.527).</p> <p>Column Y (Frequency TP2): adjusted frequency of occurrence in Time Period 2, from 2000 to 2016, as calculated in Frescalo (average = 0.461).</p> <p>Column Z (Frequency change): change in the adjusted frequency of occurrence between Time Periods 1 and 2, as calculated in Frescalo (average = -0.066).</p>
Data gathered from grabbed samples on DWSPs (drinking water service public - houses of water) for CS#2
<p>Data gathered from grabbed samples on DWSPs used to compare data obtained from online sensor installed on DWSPs</p>
CS#2 Data from online sensors on DWSP (drinking water service public – houses of water)
<div> <p>Data gathered from online sensor installed on DWSPs used to obtain infographics for monitoring water quality</p> </div>
EARLY DETECTION OF PREECLAMPSIA IN LOW-RESOURCE SETTING: EXAMINING RISK FACTORS, MEAN ARTERIAL BLOOD PRESSURE (MAP), BODY MASS INDEX (BMI), AND URINE PROTEIN AT PUBLIC PRIMARY HEALTH CARE
<p>Master data for EARLY DETECTION OF PREECLAMPSIA IN LOW-RESOURCE SETTING: EXAMINING RISK FACTORS, MEAN ARTERIAL BLOOD PRESSURE (MAP), BODY MASS INDEX (BMI), AND URINE PROTEIN AT PUBLIC PRIMARY HEALTH CARE</p>
Public release of data associated with WINTER/LIGO BNS kilonova simulations
<p>Public data release associated with the paper "An Infrared Search for Kilonovae with the WINTER Telescope. I. Binary Neutron Star Mergers." This includes the PyCBC Live results, Bayestar skymaps, Bilby skymaps and posteriors, as well as a Jupyter notebook to help decide which GW triggers to follow-up electromagnetically. See README for more details.</p>
Dataset publication pending
<p>Dataset publication pending.</p>
Dataset for the publication "Measuring the Photoelectron Emission Delay in the Molecular Frame"
<p>The data used to create the figures in the publication "Measuring the Photoelectron Emission Delay in the Molecular Frame".</p>
Figure 5 from: Blagoderov V, Brake I, Georgiev T, Penev L, Roberts D, Ryrcroft S, Scott B, Agosti D, Catapano T, Smith V (2010) Streamlining taxonomic publication: a working example with Scratchpads and ZooKeys. ZooKeys 50: 17-28. https://doi.org/10.3897/zookeys.50.539
Figure 5 - Selecting images to be included in the manuscript.
Figure 2 from: Blagoderov V, Brake I, Georgiev T, Penev L, Roberts D, Ryrcroft S, Scott B, Agosti D, Catapano T, Smith V (2010) Streamlining taxonomic publication: a working example with Scratchpads and ZooKeys. ZooKeys 50: 17-28. https://doi.org/10.3897/zookeys.50.539
Figure 2 - Selecting images to be included in the manuscript.
Figure 2 from: Nasu T, Kitagawa K, Karasawa S (2018) Species compositions of terrestrial isopods in public parks of a commuter town in Japan. In: Hornung E, Taiti S, Szlavecz K (Eds) Isopods in a Changing World. ZooKeys 801: 389-399. https://doi.org/10.3897/zookeys.801.21875
Figure 2 Frequencies of species richness of native, exotic, and total species in parks
Data used in the publication: Study on The Error Structure of Radar Reflectivity Using The Symmetric Rainrate Predictor
<p>These data support the results presented in the manuscript titled "Study on The Error Structure of Radar Reflectivity Using The Symmetric Rainrate Predictor". They consist of Observation minus Background data and rainrate data that are used to build the symmetric error model of reflectivity. We uploaded two data sets that support both original and revised manuscripts.</p>
single-cell RNAseq data (data set 13) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset13) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor11 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a></p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 10) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset10) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor8 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 6) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset6) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor4 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a></p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 2) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset2) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from normal mucosa samples downloaded from the GEO website (<strong>GSE81861). </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
Content Analysis of Publications in Experimental domains
<p>This dataset support the proposal of manual content analysis as an approach to streamline the data curator workflow. We have performed manual context analysis over publications from three experimental domains (sustainable chemistry; photovoltaic generation; nanoparticle synthesis) to extract domain-specific concepts to describe data in these domains. Our approach consisted in the identification of relevant snippets of text from publications sections reporting the experimental set up. The read me provides information on how to read the dataset.</p>
Source Data for the publication: Multi-omics and machine learning reveal context-specific gene regulatory activities of PML::RARA in Acute Promyelocytic Leukemia
<p>Source Data for the publication: Multi-omics and machine learning reveal context-specific gene regulatory activities of PML::RARA in Acute Promyelocytic Leukemia</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.