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30,334 results for “Responsiveness”
Site-Specific MCER Response Spectra for Los Angeles Region based on 3-D Numerical Simulations and the NGA West2 Equations
<p><strong>ABSTRACT</strong></p> <p>The Utilization of Ground Motion Simulation (UGMS) committee of the Southern California Earthquake Center (SCEC) developed site-specific, risk-targeted Maximum Considered Earthquake (MCER) response spectra for the Los Angeles region. The long period (T ≥ 2-sec) MCER response spectra were computed as the weighted average of MCER spectral accelerations derived from (1) 3-D numerical ground-motion simulations using the CyberShake computational platform, and (2) empirical ground-motion prediction equations (GMPEs) from the Pacific Earthquake Engineering Research (PEER) Center NGAWest2 project. The short period (T < 2- sec) MCER response spectra were computed exclusively from the NGAWest2 GMPEs. A web-based lookup tool was also developed so users can obtain the MCER response spectrum for a specified latitude and longitude and for a specified site class or 30-m average shear-wave velocity, VS30. The tool provides acceleration ordinates of the MCER response spectrum at 21 natural periods in the 0 to 10-sec band.</p> <p>This dataset includes a Java application to run queries. It serves as the backend data source for the web-based tool that can be found at: <a href="https://data2.scec.org/ugms-mcerGM-tool_v18.4/">https://data2.scec.org/ugms-mcerGM-tool_v18.4/</a>.</p> <p>For more information, please see <a href="https://www.scec.org/research/ugms">https://www.scec.org/research/ugms</a>.</p> <p><strong>DISCLAIMER</strong></p> <p>The UGMS MCER Tool is provided "as is" and without warranties of any kind. While SCEC and the UGMS Committee have made every effort to provide data from reliable sources or methodologies, SCEC and the UGMS Committee do not make any representations or warranties as to the accuracy, completeness, reliability, currency, or quality of any data provided herein. SCEC and the UGMS Committee do not intend the results provided by this tool to replace the sound judgment of a competent professional, who has knowledge and experience in the appropriate field(s) of practice. By using this tool, you accept to release SCEC and the UGMS Committee of any and all liability.</p> <p>Please note: The site-specific, design response spectral acceleration, Sa, returned by this tool for user-specified inputs, must be compared to the minimum Sa requirement described in Section 21.3 of ASCE 7-16 (second and third paragraphs). This minimum Sa is computed as 80% of the design response spectrum derived from the SDS, SD1, and TL values obtained from the ASCE tool at https://asce7hazardtool.online/. The larger of the site-specific Sa and the 80% minimum Sa at each period, T, is the final design response spectral acceleration. This final Sa x 1.5 is the final MCER response spectral acceleration.</p>
Bone metabolism gene variation and response to bisphosphonate treatment in women with postmenopausal osteoporosis
<p>This repository contains the raw and source data for the manuscript "Bone metabolism gene polymorphism and response to bisphosphonate treatment in women with postmenopausal osteoporosis" submitted to PLOS ONE.</p> <p><strong>Abstract: </strong></p> <p><em>Introduction:</em> Long-term treatment is used in patients with osteoporosis, and bisphosphonates (BPs) are the most commonly prescribed medications. However, in some patients this therapy is not effective, cause different side effects and complications. Unfortunately, at least one year is needed to identify and confirm an ineffectiveness of BPs therapy on bone mineral density (BMD). Among other factors, a response to BPs therapy may also be explained by genetic factors. The aim of this study was to analyze the influence of <em>SOST, PTH, FGF2, FDPS, GGPS1, </em>and<em> LRP5</em> gene polymorphisms on the response to treatment with BPs.</p> <p><em>Materials and methods: </em>Women with postmenopausal osteoporosis were included to this study if they used bisphosphonates for at least 12 months. Exclusion criteria were: persistence on BPs therapy less than 80%, bone metabolic diseases, diseases deemed to affect bone metabolism, malignant tumours, using of any medications influencing BMD. The study protocol was approved by the local ethics committee. The BMD at the lumbar spine and femoral neck were measured using dual x-ray absorptiometry (GE Lunar) before and at least 12 months after treatment with BPs. According to BMD change, patients were divided in two groups – responders and non-responders to BPs terapy. Polymorphic variants in <em>SOST, PTH, FGF2, FDPS, GGPS1, </em>and<em> LRP5</em> genes were determined using PCR analysis with TaqMan probes (Thermo Scientific).</p> <p><em>Results:</em> In total, 201 women with BPs therapy were included in the study. No statistically significant differences were observed in age, age at menopause, weight, height, BMI and baseline BMD levels between responders (122 subjects) and non-responders (79 subjects).</p> <p>As single markers, the <em>SOST </em>rs1234612 T/T (OR=2.3; P=0.02), <em>PTH</em> rs7125774 T/T (OR=2.8, P=0.0009), <em>FDPS</em> rs2297480 G/G (OR=29.3, P=2.2×10<sup>-7</sup>), and <em>GGPS1</em> rs10925503 C/C+C/T (OR=2.9; P=0.003) gene variants were over-represented in non-responders group. No significant association between <em>FGF2</em> rs6854081 and <em>LRP5</em> rs3736228 gene variants and response to BPs treatment was observed. The carriers of T-T-G-C allelic combination (constructed from rs1234612, rs7125774, rs2297480, and rs10925503) were predisposed to negative response to BPs treatment (OR = 4.9, 95% CI 1.7–14.6, P=0.005). The C-C-T-C combination was significantly over-represented in responders (OR = 0.1, 95% CI 0.1–0.5, P=0.006).</p> <p><em>Conclusions:</em> Our findings highlight the importance of identified single gene variants and their allelic combinations for pharmacogenetics of BPs therapy of osteoporosis. Complex screening of these genetic markers could be used as a new strategy for personalized antiresorptive therapy.</p>
Modeling Autonomic Pupillary Responses from External Stimuli Using Machine Learning - Dataset
<p>This page contains the data collected for the paper: <em><strong>Modeling Autonomic Pupillary Responses from External Stimuli Using Machine Learning </strong></em>(<a href="https://doi.org/10.26717/BJSTR.2019.20.003446">DOI:10.26717/BJSTR.2019.20.003446</a>). The dataset consists of spectral and pupillometric data collected during three outdoor/indoor walks. The folders “raw”, “merged”, and “cleaned” contain data collected by the Konica Minolta CL-500A Illuminance Spectrophotometer and Tobii Pro Glasses 2 at three different stages in the data preparation process. The “raw” folder contains uncleaned and unsynchronized .csv/.json files. The “merged” folder contains uncleaned, but synchronized light and ocular data in .csv format. The “cleaned” folder contains a single .csv of cleaned and synchronized data with the derived variables: average pupil diameter and pupil diameter difference. </p> <p>The best choice of data files will depend on desired analysis. More guidance on how to handle this data can be found in the readMe files located in each subsequent folder. More information on the sensing devices used here can be found at the Minolta and Tobii information links below. </p> <p><strong>Minolta Information</strong>: <a href="https://sensing.konicaminolta.us/uploads/cl-500a_instruction217a_eng-250cl60686.pdf">https://sensing.konicaminolta.us/uploads/cl-500a_instruction217a_eng-250cl60686.pdf</a></p> <p><strong>Tobii Information</strong>: <a href="https://www.tobiipro.com/siteassets/tobii-pro/user-manuals/tobii-pro-glasses-2-user-manual.pdf/?v=1.1.3">https://www.tobiipro.com/siteassets/tobii-pro/user-manuals/tobii-pro-glasses-2-user-manual.pdf/?v=1.1.3</a></p> <p>The codes used to prepare, analyze, and visualize this data is available in the LightOcular GitHub Repository linked below. </p> <p><strong>LightOcular GitHub Repo</strong>: <a href="https://github.com/mi3nts/LightOcular">https://github.com/mi3nts/LightOcular</a></p>
Expansion of coccidioidomycosis (Valley fever) endemic regions in the United States in response to climate change: projections of disease incidence
<p>This file contains estimations of coccidioidomycosis (Valley fever) incidence data in cases per 100,000 population per year for the contemporary time period and projections throughout the 21st century in response to RCP4.5 and RCP8.5 climate scenarios, associated with the publication:</p> <p>Gorris, M. E., Treseder, K. K., Zender, C. S., and Randerson, J. T. (2019). Expansion of coccidioidomycosis endemic regions in the United States in response to climate change. <em>GeoHealth</em>. </p> <p>The data is reported for each county in the conterminous US with its associated FIPS code (Column 1), county name (Column 2), state FIPS code (Column 3), and state name (Column 4). Column 5 contains the estimation of mean annual Valley fever incidence averaged from 2000-2015. Column 6-8 contain the estimations of mean annual Valley fever incidence for the 11-year averages surrounding years 2035, 2065, and 2095 for RCP4.5 climate scenario. Likewise, Columns 9-11 contain the estimations of mean annual Valley fever incidence for the 11-year averages surrounding years 2035, 2065, and 2095 for the RCP8.5 climate scenario. </p> <p>Details about how the incidence data was calculated may be read in the Methods subsection of the paper under "Modeling of current and future mean annual Valley fever incidence". The data provided here was used to create Figure 7 and Supporting Information Figure S5. Counties that have non-zero incidence are considered endemic by our climate-constrained niche model, so this data may also be used to create portions of Figures 3, 4, and S3. </p>
DATASET : Thresholds of fire response to moisture and fuel load differ between tropical savannas and grasslands across continents
<p><strong>Abstract </strong></p> <p><strong>Aim:</strong> An emerging framework for tropical ecosystems states that fire activity is either ‘<em>fuel build-up limited</em>’ or ‘<em>fuel moisture limited</em>’ i.e. as you move up along rainfall gradients, the major control on fire occurrence switches from being the amount of fuel, to the moisture content of the fuel. Here we used remotely sensed datasets to assess whether interannual variability of burned area is better explained by annual rainfall totals driving fuel build-up, or by dry season rainfall driving fuel moisture.</p> <p><strong>Location:</strong> Pantropical savannas and grasslands</p> <p><strong>Time period:</strong> 2002-2016</p> <p><strong>Methods:</strong> We explored the response of annual burned area to interannual variability in rainfall. We compared several linear models to understand how <em>fuel moisture </em>and <em>fuel build-up effect </em>(accumulated rainfall during 6 and 24 months prior to the end of the burning season respectively) determine the interannual variability of burned area and explore if tree cover, dry season duration and human activity modified these relationships.</p> <p><strong>Results:</strong> Fuel and moisture controls on fire occurrence in tropical savannas varied across continents. Only 24% of South American savannas were <em>fuel build-up limited</em> against 61% of Australian savannas and 47% of African savannas. On average, South America switched from fuel limited to moisture limited at 500 mm yr<sup>-1</sup>, Africa at 800 mm yr<sup>-1</sup> and Australia at 1000 mm yr<sup>-1 </sup>of mean annual rainfall.</p> <p><strong>Main conclusions:</strong> In 42% of tropical savannas (accounting for 41% of current area burned) increased drought and higher temperatures will not increase fire, but there are savannas, particularly in South America, that are likely to become more flammable with increasing temperatures. These findings highlight that we cannot transfer knowledge of fire responses to global change across ecosystems/regions – local solutions to local fire management issues are required, and different tropical savanna regions may show contrasting responses to the same drivers of global change.</p>
Automated Classification of Conversation Valence and Arousal using Autonomic Nervous System Responses
<p>This repository contains the supplementary file for our study "Automated Classification of Conversation Valence and Arousal using Autonomic Nervous System Responses". The MS Excel file contains all physiological features (individual features and synchrony features) for all valid dyads and all intervals together with self-report ratings of the conversation (Self-Assessment Manikin) and personality trait data (CES-D, BFNES, QCAE). Synchrony features were calculated using code from a previous Zenodo submission (https://zenodo.org/record/7140829).</p>
Dataset for: Infectious disease responses to human climate change adaptations
<p>Original and derived data products referenced in the original manuscript are provided in the data package.</p> <h3>Description of the data and file structure</h3> <p><em>Original data:</em></p> <p><code>Table_1_source_papers.csv</code>: Papers that met review criteria and which are summarized in Table 1 of the manuscript.</p> <ol> <li><strong>ID</strong>: The paper identification number</li> <li><strong>Topic</strong>: The broad topic (i.e., each row of Table 1)</li> <li><strong>Authors:</strong> The names of the authors of the paper</li> <li><strong>Article Title</strong>: The title of the paper</li> <li><strong>Source Title</strong>: The name of the journal in which the paper was published</li> <li><strong>Abstract</strong>: The paper's abstract, retrieved from the Web of Science search</li> <li><strong>study_type:</strong> Classification of the study methodology/approach. "A" = a designed study that shows effect ,"B" = a pre/post study, "C" = a comparison of health outcomes or pathogen risk relative to a 'control/comparison' area, "D" = some quantitative effect but no control, "E" = qualitative comments but little supporting evidence, and/or a qualitative review.</li> <li><strong>pathogen_broad</strong>: Broad classification of the type of pathogen discussed in the paper.</li> <li><strong>transmission_type</strong>: Categorization of indirect, direct, sexual, vector, or other transmission modes.</li> <li><strong>pathogen_type</strong>: Categorization of bacteria, helminth, virus, protozoa, fungi, or other pathogen types.</li> <li><strong>country:</strong> Country in which the study was performed or results discussed. When countries were not available, regions were used. NA values indicate papers in which a geographic region was not relevant to the study (i.e., a methods-based study).</li> </ol> <p><em>Derived data:</em></p> <p><code>change_livestock_country.csv:</code> A dataframe containing values used to generate Figure 4a in the manuscript.</p> <ol> <li><strong>County Name</strong>: The name of the county in Kenya</li> <li><strong>Sheep and goats 1980</strong>: The estimated number of sheep and goats in 1980</li> <li><strong>Sheep and goats 2016</strong>: The estimated number of sheep and goats in 2016</li> <li><strong>pct_change_shoat</strong>: The percent change in sheep and goat numbers from 1980 to 2016</li> <li><strong>Cattle 1980</strong>: The estimated number of cattle in 1980</li> <li><strong>Cattle 2016</strong>: The estimated number of cattle in 2016</li> <li><strong>pct_change_cattle</strong>: The percent change in cattle numbers from 1980 to 2016</li> <li><strong>Camel 1980</strong>: The estimated number of camels in 1980</li> <li><strong>Camel 2016</strong>: The estimated number of camels in 2016</li> <li><strong>pct_change_camel</strong>: The percent change in camel numbers from 1980 to 2016</li> <li><strong>human_pop 1980</strong>: The estimated human population in the county in 1980</li> <li><strong>human_pop 2016</strong>: The estimated human population in the county in 1980</li> <li><strong>pct_change_human</strong>: The percent change in the human population from 1980 to 2016</li> <li><strong>area_sq_km</strong>: The land area of the county</li> <li><strong>change_ind_per_sq_km_shoat:</strong> Absolute change in number of sheep and goats from 1980 to 2016</li> <li><strong>change_ind_per_sq_km_cattle:</strong> Absolute change in number of cattle from 1980 to 2016</li> <li><strong>change_ind_per_sq_km_camel:</strong> Absolute change in number of camels from 1980 to 2016</li> </ol> <p><code>country_avg_schist_wormy_world.csv</code>: A dataframe containing values used to generate Figure 3 in the manuscript.</p> <ul> <li><strong>Country:</strong> The country in which the schistosome prevalence studies were performed.</li> <li><strong>Latitude:</strong> The latitute in decimal degrees</li> <li><strong>Longitude:</strong> The longitute in decimal degrees</li> <li><strong>Maximum.prevalence:</strong> The mean maximum schistosomiasis prevalence of studies conducted within each country.</li> </ul> <p><code>kenya_precip_change_1951_2020.csv</code>: A dataframe containing values used to generate Figure 4b in the manuscript.</p> <ul> <li><strong>Precipitation (mm):</strong> Binned annual precipitation values</li> <li><strong>1951-1980:</strong> The density of observations for each annual precipitation value for the 1951-1980 period</li> <li><strong>1971-2000:</strong> The density of observations for each annual precipitation value for the 1971-2000 period</li> <li><strong>1991-2020:</strong> The density of observations for each annual precipitation value for the 1991-2020 period</li> </ul> <h3>Sharing/Access information</h3> <p>Data were derived from the following sources:</p> <ul> <li> <p>Ogutu, J. O., Piepho, H.-P., Said, M. Y., Ojwang, G. O., Njino, L. W., Kifugo, S. C., & Wargute, P. W. (2016). Extreme wildlife declines and concurrent increase in livestock numbers in Kenya: What are the causes? <em>PloS ONE</em>, <em>11</em>(9), e0163249. https://doi.org/10.1371/journal.pone.0163249</p> </li> <li> <p>London Applied & Spatial Epidemiology Research Group (LASER). (2023). <em>Global Atlas of Helminth Infections: STH and Schistosomiasis</em> [dataset]. London School of Hygiene and Tropical Medicine. https://lshtm.maps.arcgis.com/apps/webappviewer/index.html?id=2e1bc70731114537a8504e3260b6fbc0</p> </li> <li> <p>World Bank Group. (2023). <em>Climate Data & Projections—Kenya</em>. Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org/country/kenya/climate-data-projections</p> </li> </ul>
Data on respiration, substrate incorporation, and soil compound concentration in response to simulated root exudation
<p>In this study we used reverse microdialysis to release a mixture of <sup>13</sup>C-labeled substrates into intact meadow and forest soil cores (6-hour long) to simulate root exudation. We utilized three different artificial root exudates: sugars (glucose, fructose), organic acids (acetate, succinate), and a combination of sugars and organic acids (glucose, fructose, acetate, succinate); alongside a water-only control for comparison.</p> <p>We collected compounds from soil solutions and measured respiration. Due to <sup>13</sup>C-labeled substrate we could differentiate between substrate-derived respiration and SOM-derived respiration. Additionally, we extracted lipid fatty acids from soil and measured their <sup>13</sup>C incorporation.<br><br></p>
DATA: As-deposited and dewetted Cu layers on plasma treated glass: adhesion study and its effect on biological response
<p>Dataset contains data related to improving the adhesion of nanosized copper films to a glass substrate. </p>
Regional scale shear wave velocity profiles for ground response analyses and uncertainties evaluations – the Piedmont Region (NW Italy) Database
<p>The databases provide detailed information for the Piedmont region in Northwest Italy, offering a view of its geological and geophysical characteristics:</p> <ul> <li><strong>Geological-Geomorphological Database</strong>: Includes 13 distinct Geological-Geomorphological Domains (GGD) in shapefile format. It supports spatial analysis and visualization, based on data from the Geological Map of the Piedmont Region at a 1:250,000 scale.</li> <li><strong>Geotechnical Database</strong>: Contains geotechnical data on bedrock depth and texture attributes derived from available logs in CSV format. Georeferenced using UTM coordinates (WGS84 UTM32N), it includes depth values and texture codes (C for clay, G for gravel, S for sand, R for rock, X for not available).</li> <li><strong>Geophysical Database</strong>: Provides data on shear wave velocity (Vs) profiles in CSV format. Georeferenced with UTM coordinates (WGS84 UTM32N), it includes layer interface depth and shear wave velocity above each layer.</li> </ul>
Shape, membrane morphology, and morphodynamic response of metabolically active human mitochondria revealed by scanning ion conductance microscopy
<p>This contains the hole data set as well as all analysed data for the paper published in Beilstein Journal of Nanotechnology "Shape, membrane morphology and morphodynamic response of metabolically active human mitochondria revealed by Scanning Ion Conductance Microscopy".</p> <p>Most of the images were taken with the SICM. These uncompressed tiff files can be read and processed with the Gwyddion software or other scanning probe image processing software.</p>
SARS-CoV-2 mRNA vaccines induce persistent human germinal centre responses
<p>These are the<strong> processed</strong> BCR repertoire bulk sequencing data described in <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., Nature, 2021</a> (Fig 3b-d; Extended Data Fig 3; Extended Data Table 6). The corresponding <strong>raw</strong> sequencing reads are available on SRA under <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA731610">BioProject PRJNA731610</a>.</p> <p><strong>Summary</strong>: Bulk-sorted total plasmablasts from PBMCs and germinal centre B cells at 4 weeks after primary immunization from 3 vaccinees who had no prior history of infection with SARS-CoV-2. </p> <p><strong>Code: </strong>Code along with Docker container for reproducing the NGS data-based figures and analyses in the published paper can be <a href="https://github.com/julianqz/wustl_published/tree/main/nature_2021">found on GitHub</a>.</p> <p><strong>Metadata file</strong>: WU368_turner_et_al_nature_2021_meta.tsv</p> <p>Abbreviations:</p> <ul> <li>LN = lymph node</li> <li>PB = plasmablast</li> <li>GC = germinal centre</li> <li>mAb = monoclonal antibody</li> </ul> <p><strong>BCR data file</strong>: WU368_turner_et_al_nature_2021_bcr.tsv.gz</p> <p>In addition to the processed bulk sequences, also included are the heavy chains of 37 mAbs that had been validated to be spike-binding and that were used together with the bulk sequences for clonal lineage inference. The mAbs are annotated as "mab" in the "seq_type" column.</p> <p><strong>BCR data column descriptions</strong></p> <p>The columns largely follow the <a href="https://changeo.readthedocs.io/en/stable/standard.html">AIRR-C Rearrangement format</a>. The main deviation is that CDR3s are used, as opposed to IMGT-defined "junctions". Non-standard columns are noted below.</p> <ul> <li>v_call_genotyped: V gene annotation reassigned after individualized genotyping by <a href="https://tigger.readthedocs.io/en/stable/">TIgGER</a></li> <li>germline_[vdj]_call: clonal consensus germline sequence reconstructed via <a href="https://changeo.readthedocs.io/en/stable/methods/germlines.html">`CreateGermlines.py --cloned` using Change-O</a></li> <li>isotype: IGH[ADEGM]</li> <li>cdr3: CDR3 nucleotide sequence</li> <li>cdr3_length: CDR3 nucleotide sequence length</li> <li>cdr3_aa: CDR3 amino acid sequence</li> <li>collapse_count: number of duplicate IMGT-aligned V(D)J sequences that were collapsed by <a href="https://alakazam.readthedocs.io/en/stable/topics/collapseDuplicates/">`alakazam::collapseDuplicates`</a></li> <li>donor: vaccinee</li> <li>sample: sample ID (arbitrary)</li> <li>timepoint: time point at which sample was collected</li> <li>tissue: tissue from which sample was collected</li> <li>sorting: FACS sorting</li> <li>seq_type: sequence type (mAb or bulk)</li> <li>nuc_RS_19_312: number of replacement and silent mutations between IMGT-numbered nucleotide positions 19-312 along IGHV sequences, calculated by <a href="https://shazam.readthedocs.io/en/stable/topics/calcObservedMutations/">`shazam::calcObservedMutations`</a></li> <li>nuc_denom_19_312: number of informative nucleotide positions for counting mutations, excluding non-A/T/G/C positions (such as "N", "-", ".")</li> <li>nuc_RS_freq_19_312: nucleotide-level mutation frequency (= nuc_RS_19_312 / nuc_denom_19_312)</li> </ul>
Monitoring knowledge, risk perceptions, preventive behaviours and trust to inform pandemic outbreak response.
<p>The study is part of the large project promoted by WHO Regional Office for Europe called “<em>Monitoring knowledge, risk perceptions, preventive behaviours and trust to inform pandemic outbreak response</em>” and carried out in over 30 countries of the WHO European Region (Registered ISRCTN on 11/05/2021, ID: ISRCTN26200758). In Italy, the survey was conducted administering an online questionnaire developed <em>ad hoc</em> by the WHO in four waves (January-May 2021) to a sample of 10.000 individuals aged 18-70 years. A detailed sampling plan was developed to obtain a representative sample of the Italian adult population. The following variables were taken into account for stratification of the participants: gender by age (four age groups: 18-34 years, 35-44 years, 45-54 years, 55-70 years); geographical area (four areas: North West, North East, Centre, South and Islands); size of living centers (two classes: above and below 100,000 inhabitants); level of education (up to lower middle school, beyond lower middle school); and employment situation (employed, not employed). At the end of each survey’s wave, a weighting procedure has been applied to accurately restore the proportionality of the total sample examined with the reference population, according to the most recent data of the Italian Statistics Institute (ISTAT, 12/31/2019). In particular, data have been weighted for the main socio-demographic and geographic variables (e.g., sex by age by geographical area, occupation, educational qualification, geographical area by size of living centers). The sample size made it possible to maintain a sampling error of less than 2% (at the significance level of 95%) and to control the error of estimates within groups or subgroups of interest. The interviews were conducted by Doxa S.p.a. and carried out with the CAWI technique (Computer Assisted Web Interviewing) on an online panel and on the Confirmit software platform used by Doxa S.p.a. The average administration time was about 18-20 minutes. This study was approved by the Ethics Committee of the IRCCS San John of God Fatebenefratelli of Brescia (n° 72-2020), and all participants provided written informed consent.</p> <p>The primary objectives are to:</p> <p>● Monitor variables that are critical for population behaviour to control transmission of the novel coronavirus, including risk perceptions, knowledge, self-efficacy, confidence in institutions, behaviours, rumours, affect, worry, resilience, trust in/use of information sources and more.<br> ● Document changes over time in these factors to understand the effect of the pandemic process, new developments, events or measures taken.<br> ● Monitor possible issues, e.g. related to misinformation or distrust, as they emerge, to allow early response.<br> ● Identify relationships between variables to identify levers for effective and appropriate responses.<br> ● Explore the relationship of psychological variables (e.g. worry, resilience, trust, affect) with the epidemiological situation and the events and measures taken.<br> ● Identify gaps between perceived and actual knowledge.<br> ● Evaluate the effectiveness of pandemic response measures, and the acceptance and effectiveness of policies and restrictions implemented, including the easing of such restrictions.<br> The secondary objectives are to:<br> ● Contribute to post-outbreak evaluation, thereby contributing to the continued regional/global efforts to better understand mechanisms of crisis response.<br> ● If additional research capacity is available, the data can be triangulated with data on media reporting, COVID-19 cases and other.● If additional research capacity is available, the data can be triangulated with data on media reporting, imported or confirmed cases, etc.: The relationship between psychological variables and characteristics of the outbreak situation can be explored (i.e. how closely the perceived risk mirrors reported cases, relative import risk, media reports).<br> This approach allows a citizen-centred approach where insights into population perceptions and behaviours inform COVID-19 actions, alongside epidemiological data and considerations of economic, cultural, ethical, structural political nature and other.</p> <p>The WHO questionnaire includes 21 different thematic areas noteworthy for the investigation of COVID-19 experience. The questionnaire was translated into specific country language by each recruiting site, following the WHO’s guidelines for translations of tools into other languages. The process included the following steps: forward translation, panel experts, back-translation, pre-test and cognitive interviews and, finally, development of the final version. Variables being surveyed include the following:<br> • Socio-demography;<br> • COVID-19 personal experience;<br> • Health literacy;<br> • COVID-19 risk perception;<br> • Probability and Severity;<br> • Preparedness and Perceived self-efficacy;<br> • Prevention – own behaviours;<br> • Affect;<br> • Trust in sources of information;<br> • Use of sources of information;<br> • Frequency of Information;<br> • Trust in institutions (perceptions);<br> • Policies, interventions (perceptions);<br> • Conspiracies (perceptions);<br> • Resilience (perceptions);<br> • Testing and tracing;<br> • Fairness (perceptions);<br> • Lifting restrictions (pandemic transition phase);<br> • Unwanted behaviour;<br> • Wellbeing;<br> • COVID-19 vaccine.</p> <p> </p>
First In-Situ Measurements of Travelling Ionospheric Disturbances at 420 km Altitude by the Scintillation Observations and Response of The Ionosphere to Electrodynamics (SORTIE) CubeSat
<p>Companion dataset to the paper entitled "First In-Situ Measurements of Travelling Ionospheric Disturbances at 420 km Altitude by the Scintillation Observations and Response of The Ionosphere to Electrodynamics (SORTIE) CubeSat". The dataset includes the SORTIE CubeSat IVM Level 2 ion density and GPS TEC data used in the study along with the WRF simulation results.</p>
Taste responsiveness and liking of model food samples
<p>This data sheet consisted of children's taste intensity perception and liking to model food samples of grapefruit juice and vegetable broth. In addition, children's responses regarding PROP responsiveness, food familiarity, stated liking, food choice, and neophobia were also reported in this data set. </p>
Inflammatory Responses in the Placenta upon SARS-CoV-2 Infection Late in Pregnancy - IHC data
<p>SARS-CoV-2 infection during pregnancy does not affect the large majority of neonates but presents an increased risk for adverse pregnancy outcome. The effects of SARS-CoV-2 of its recently identified variants on placental function are not well understood. In this study, we investigated the impact of late gestational SARS-CoV-2 infection on the placenta.</p> <p>This dataset of is comprised of 897 images of classic immunohistochemistry for 3 markers in placenta from COVID-19 patients and controls.</p> <p><strong>A full description of the tissues, markers, and donors is available in the metadata.csv file.</strong></p>
Roles and responsibilities of stakeholders in the RDM lifecycle
<p>In efforts to better articulate the roles and responsibilities of key stakeholders (not exhaustive) involved in data management and stewardship throughout the research data management (RDM) lifecycle, this infographic was developed as part of RDM training. The infographic is an integrative adaptation of the Data Asset Framework Implementation Guide (JISC et al., 2009, page 3) and Research Data Governance & Materials Handling Policy (UNSW, 2019, page 10).</p>
Supplementary Information to: "Living on the edge: Response of rudist bivalves (Hippuritida) to hot and highly seasonal climate in the low-latitude Saiwan site, Oman"
<p>This dataset contains supplementary information required to understand and reproduce the study detailed in our manuscript titled "<em>Living on the edge: Response of rudist bivalves (Hippuritida) to hot and highly seasonal climate in the low-latitude Saiwan site, Oman</em>" which was submitted for publication to Palaeogeography, Palaeoclimatology, Palaeoecology.</p>
Segmentation Labels for Emergency Response Imagery from Hurricane Barry, Delta, Dorian, Florence, Isaias, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon
<p>The zip file here contains 1,179 pairs of human-generated segmentation labels and images from Emergency Response Imagery collected by US National Oceanic and Atmospheric Administration (NOAA) after Hurricane Barry, Delta, Dorian, Florence, Ida, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon. A total of 1,054 unique images were labeled. 946 images were annotated by a single labeler. 95 images were annotated by two labelers. 11 images were annotated by three labelers. 2 images were annotated by five labelers. All authors contributed to labeling, and all labeling was done with an open-source labeling tool (Buscombe et al., 2022).</p> <p>All pixels in each image are labeled with one of four classes: 0 (water), 1 (bare sand), 2 (vegetation - both sparse and dense), 4 (the built environment - buildings, roads, parking lots, boats, etc.)</p> <p>The csv file provided here is a list of each image file name (which includes the anonymized labeler ID), the name of the image without the labeler ID, the name of the corresponding NOAA jpg, the NOAA flight name, the storm name, the latitude and longitude of the image, and a column stating if the image has been labeled multiple times. </p> <p>Images labeled here correspond to multiple NOAA flights — all listed in the csv file for each jpeg image. These jpeg images can be downloaded directly from NOAA (https://storms.ngs.noaa.gov/) or using Moretz et al. (2020a, 2020b). The images included in this data release correspond to original NOAA images that have been resized and then split into quadrants (using ImageMagick). The naming convention corresponds to the image quarter — the *-0.jpg is upper left, *-1.jpg is upper right, *-2.jpg is lower left, and *-3.jpg is the lower right.</p> <p><br> The resize command used was:</p> <p><br> #to resize and then quarter<br> #Dir structure is:<br> # --Desktop<br> # |- originals<br> # |- resized<br> # |- quarters</p> <p>`cd originals`<br> `mogrify -resize 2000x2000 -path ../resized *.jpg`</p> <p>#then quarter them<br> `cd ..`<br> `cd resized`</p> <p>`mogrify -crop 2x2@ +repage -path ../quarters *.jpg`</p> <p>For full size images, please download the jpegs directly from NOAA.</p>
Data from: Transcriptomic meta-analysis reveals unannotated long non-coding RNAs related to the immune response in sheep
<p>This dataset contains additional files from the manuscript: "Transcriptomic meta-analysis reveals unannotated long non-coding RNAs related to the immune response in sheep".</p> <p>The files included are:</p> <p>- All novel lncRNA transcript annotation GTF file ( lncrnas.gtf )</p> <p>- High-confidence lncRNA gene annotation GTF file ( lncrnas_evidence.gtf )</p> <p>- All novel lncRNA transcript annotation GTF file remapped to the ARS-UI_Ramb_v2.0 genome ( lncrnas_remapped_v2.gtf )</p> <p>- Raw count estimates of the extended annotation ( rawcounts.csv )</p> <p>- TPM values of the extended annotation ( tpmcounts.csv )</p> <p>- Supplementary data to the published article (.xlsx, .pdf)</p> <p> </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.