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Predictors of depression, anxiety and stress indicators in a cohort of women with chronic pelvic pain data set
<p>Data set for submission of paper 'Predictors of depression, anxiety and stress indicators in a cohort of women with chronic pelvic pain'</p>
Predictors of depression, anxiety and stress indicators in a cohort of women with chronic pelvic pain - data set 2
<p>Data set for PPSA study - 2. A second data set was created for the PPSA study publication to isolate variables of interest to make the statistics manageable, due to the sheer number of variables in the initial data set </p>
Mediterranean Sea Climatic Indices - Areal density of OHC/OSC
<p>Climatic indices for the Mediterranean Sea (6.25W-36.5E, 30N-46N) from 1950 to 2015. The file<br> "Med_Climatic_Indices_ArealDensity.tar.gz" contains : a) an ascii file named "inventory_ArealDensity.txt" which lists all the available indices, and b) a directory named "ArealDensity" with the indices under the following structure:</p> <ul> <li>ArealDensity/Annual_Decadal/Variable_decade_allmonths_z1z2.nc</li> <li>ArealDensity/Seasonal_Decadal/ Variable_decade_season_z1z2.nc</li> </ul> <p>Other naming:</p> <ul> <li>decade: 19501959 to 20062015</li> <li>allmonths: 0112 (all months from January to December)</li> <li>season: 0103 for winter, 0406 for spring, 0709for summer, 1012 for autumn</li> <li>z1z2: vertical layer between z1 and z2 depths</li> </ul> <p> </p> <p>Example:</p> <ul> <li>OHCad_19501959_0112_5150.nc, is the areal density of the Ocean Heat Content anomaly for the decade 19501959 between 5 and 150 m</li> </ul>
Mediterranean Sea Climatic Indices - Differences of two 30-years averages of T/S, OHC/OSC Anomalies
<p>Climatic indices for the Mediterranean Sea (6.25W-36.5E, 30N-46N) from 1950 to 2015. The file<br> "Med_Climatic_Indices_Climashift_30yrs.tar.gz" contains : a) an ascii file named "inventory_Climashift_30yrs.txt" which lists all the available indices, and b) a directory named "Climashift_30yrs" with the indices under the following structure:</p> <ul> <li>Climashift_30yrs/Anomalies/Annual/Variable_decade2_decade1_allmonths_z1_z2.nc</li> <li>Climashift_30yrs/Anomalies/Seasonal/ Variable_decade2_decade1_season_z1_z2.nc</li> <li>Climashift_30yrs/Vertical_Averages/Annual/Variable_decade2_decade1_allmonths_z1z2.nc</li> <li>Climashift_30yrs/Vertical_Averages/Seasonal/Variable_decade2_decade1_season_z1z2.nc</li> </ul> <p>The Variable naming is:</p> <ul> <li> Tanomclimashift: Temperature anomaly difference between decade2 and decade1</li> <li> Sanomclimashift: Temperature anomaly difference between decade2 and decade1</li> <li> Tvavgclimashift: Vertically averaged temperature anomaly difference between decade2 and decade1</li> <li> Svavgclimashift: Vertically averaged salinity anomaly difference between decade2 and decade1</li> <li> OHCclimashift: Ocean Heat Content anomaly difference between decade2 and decade1</li> <li> OSCclimashift: Ocean Salt Content anomaly difference between decade2 and decade1</li> </ul> <p> <br> Other naming:</p> <ul> <li>decade1: stands for 19501979</li> <li>decade2: stands for 19802015</li> <li>allmonths: 0112 (all months from January to December)</li> <li>season: 0103 for winter, 0406 for spring, 0709for summer, 1012 for autumn</li> <li>z1z2: vertical layer between z1 and z2 depths</li> <li>z1, z2: standard depth levels</li> </ul> <p>Example:</p> <ul> <li>Sanomclimashift_19802015_19501979_0112_5_4000.nc, is the annual salinity anomaly difference between 1980-2015 and 1950-1970 from 5 to 4000 m.</li> </ul> <p><br> </p>
Mediterranean Sea Climatic Indices - T/S Anomalies
<p>Climatic indices for the Mediterranean Sea (6.25W-36.5E, 30N-46N) from 1950 to 2015. The file<br> "Med_Climatic_Indices_Anomalies.tar.gz" contains : a) an ascii file named "inventory_Anomalies.txt" which lists all the available indices, and b) a directory named "Anomalies" with the indices under the following structure:</p> <ul> <li>Anomalies/Annual_Decadal/Variable_decade_allmonths_z1_z2.nc</li> <li>Anomalies/Seasonal_Decadal/Variable_decade_season_z1_z2.nc</li> </ul> <p>The Variable naming is:</p> <ul> <li>Tanom: Temperature anomaly</li> <li>Sanom: Salinity anomaly</li> </ul> <p>Other naming:</p> <ul> <li>decade: 19501959 to 20062015</li> <li>allmonths: 0112 (all months from January to December)</li> <li>season: 0103 for winter, 0406 for spring, 0709for summer, 1012 for autumn</li> <li>z1, z2: standard depth levels</li> </ul> <p>Example:</p> <ul> <li>Sanom_19501959_0112_5_4000.nc, is the Salinity anomaly for the decade 19501959 from 5 to 4000 m</li> </ul> <p> </p> <p> </p> <p> </p> <p> </p>
Mediterranean Sea Climatic Indices - Linear trends of T/S, OHC/OSC Anomalies
<p>Climatic indices for the Mediterranean Sea (6.25W-36.5E, 30N-46N) from 1950 to 2015. The file<br> "Med_Climatic_Indices_LinearTrends.tar.gz" contains : a) an ascii file named "inventory_LinearTrends.txt" which lists all the available indices, and b) a directory named "LinearTrends" with the indices under the following structure:</p> <ul> <li>LinearTrends/Annual/Variable_period_allmonths_z1z2.nc</li> <li>LinearTrends/Seasonal/ Variable_period_season_z1z2.nc</li> </ul> <p>The Variable naming is:</p> <ul> <li>Tlineartrend: Vertically averaged temperature anomaly linear trend</li> <li>Slineartrend: Vertically averaged salinity anomaly linear trend</li> <li>OHClineartrends: Ocean Heat Content anomaly linear trend</li> <li>OSClineartrends: Ocean Salt Content anomaly linear trend</li> </ul> <p> </p> <p>Other naming:</p> <ul> <li>period: stands for 19502015</li> <li>allmonths: 0112 (all months from January to December)</li> <li>season: 0103 for winter, 0406 for spring, 0709for summer, 1012 for autumn</li> <li>z1z2: vertical layer between z1 and z2 depths</li> </ul> <p>Example:</p> <ul> <li>Tlineartrend_19502015_0112_6004000.nc, is the annual vertically temperature anomaly linear trend, for the period 19502015, between 600 and 4000 m</li> </ul>
Mediterranean Sea Climatic Indices - T/S Vertical Averages
<p>Climatic indices for the Mediterranean Sea (6.25W-36.5E, 30N-46N) from 1950 to 2015. The file<br> "Med_Climatic_Indices_VerticalAverages.tar.gz" contains : a) an ascii file named "inventory_VerticalAverages.txt" which lists all the available indices, and b) a directory named "VerticalAverages" with the indices under the following structure:</p> <ul> <li>VerticalAverages/Annual_Decadal/Variable_decade_allmonths_z1z2.nc</li> <li>VerticalAverages/Seasonal_Decadal/ Variable_decade_season_z1z2.nc</li> </ul> <p>The Variable naming is:</p> <ul> <li>Tanomvavg: Vertically averaged temperature anomaly</li> <li>Sanomvavg: Vertically averaged salinity anomaly</li> </ul> <p>Other naming:</p> <ul> <li>allmonths: 0112 (all months from January to December)</li> <li>season: 0103 for winter, 0406 for spring, 0709for summer, 1012 for autumn</li> <li>decade: 19501959 to 20062015</li> <li>z1z2: vertical layer between z1 and z2 depths</li> </ul> <p>Example:</p> <ul> <li>Tanomvavg_20062015_1012_6004000.nc, is the autumn vertically averaged temperature anomaly, for the decade 2006-2015, between 600 and 4000 m</li> </ul>
Mediterranean Sea Climatic Indices - Time Series of T/S, OHC/OSC Anomalies
<p>Climatic indices for the Mediterranean Sea (6.25W-36.5E, 30N-46N) from 1950 to 2015. The file<br> "Med_Climatic_Indices_TimesSeries.tar.gz" contains a directory named "TimeSeries" with the following 5 indices:</p> <ul> <li>TimeSeries_Annual.nc: annual time series for Temperature (T), Salinity (S), Ocean Heat Content (OHC) and Ocean Salt Content (OSC) anomalies for 57 running decades (from decade 1950-1959 (Y1) to decade 20062015) and four layers from d1-d2 m (surface 5-150 m, intermediate 150 - 600 m, deep 600 - 4000 m and the whole column (5 - 4000 m).</li> <li>TimeSeries_Seasonal_0103.nc: winter time series for Temperature (T), Salinity (S), Ocean Heat Content (OHC) and Ocean Salt Content (OSC) anomalies for 57 running decades (from decade 1950-1959 (Y1) to decade 20062015) and four layers from d1-d2 m (surface 5-150 m, intermediate 150 - 600 m, deep 600 - 4000 m and the whole column (5 - 4000 m).</li> <li>TimeSeries_Seasonal_0406.nc: spring time series for Temperature (T), Salinity (S), Ocean Heat Content (OHC) and Ocean Salt Content (OSC) anomalies for 57 running decades (from decade 1950-1959 (Y1) to decade 20062015) and four layers from d1-d2 m (surface 5-150 m, intermediate 150 - 600 m, deep 600 - 4000 m and the whole column (5 - 4000 m).</li> <li>TimeSeries_Seasonal_0709.nc: summer time series for Temperature (T), Salinity (S), Ocean Heat Content (OHC) and Ocean Salt Content (OSC) anomalies for 57 running decades (from decade 1950-1959 (Y1) to decade 20062015) and four layers from d1-d2 m (surface 5-150 m, intermediate 150 - 600 m, deep 600 - 4000 m and the whole column (5 - 4000 m).</li> <li>TimeSeries_Seasonal_1012.nc: autumn time series for Temperature (T), Salinity (S), Ocean Heat Content (OHC) and Ocean Salt Content (OSC) anomalies for 57 running decades (from decade 1950-1959 (Y1) to decade 20062015) and four layers from d1-d2 m (surface 5-150 m, intermediate 150 - 600 m, deep 600 - 4000 m and the whole column (5 - 4000 m).</li> </ul>
FIGURE 2. Neurocranium. Yellow color indicates the naked areas around the snout plates. Corumbataia britskii, LBP 9590, a in Two new species of Corumbataia (Hypoptopomatinae: Loricariidae) from Rio Corrente, upper Rio Paraná basin, Brazil
FIGURE 2. Neurocranium. Yellow color indicates the naked areas around the snout plates. Corumbataia britskii, LBP 9590, a) dorsal view, b) lateral view. Corumbataia lucianoi, LBP 25543., paratype, c) dorsal view, d) lateral view. Corumbataia liliai, LBP 25544., paratype, e) dorsal view, f) lateral view.
Table for Paper: Late Pleistocene-Holocene Paleoseismology of Mingle-Damaying Fault and the Indication for the Crustal Shortening Mechanism of East Qilian Shan, Northeast Tibetan Plateau
<p>The material for sampling is charred material and they are analyzed in Beta Analytic Inc., USA and AMS <sup>14</sup>C dating laboratory of Peking University. Both of the laboratories provide conventional age, as well as calibrated age using INTCAL13 (Reimer et al., 2013).</p>
Data for paper: Late Pleistocene-Holocene Paleoseismology of Mingle-Damaying Fault and the Indication for the Crustal Shortening Mechanism of East Qilian Shan, Northeast Tibetan Plateau
<p>All of the data are in format of JPG</p>
Casa do Adolescente (SP) - Statistical Indicators (07/08/2018 - 03/09/2018)
<p>Survey conducted between 07 / august / 2018 - 03 / september / 2018, with the objective of gathering<br> information in order to start a database in order to serve in the construction of statistical indicators related to the mapping<br> socioeconomic mapping and rates of teenage pregnancies of participating adolescents<br> of the House of the Teenager project.</p> <p>The data were collected exclusively in the asa do Adolescente building and<br> always conducted by professionals in the areas of Health and / or Education.<br> The participation of adolescents occurred during the routine activities of the<br> Casa do Adolescente project, the respondents were informed about<br> the nature of the questions, their objectives and then invited to participate.<br> At no time were the responders identified, preserving<br> identity, secrecy and security with respect to the information collected.<br> The cities corresponding to the Casa do Adolescente that participated in the survey were:</p> <p>Cruzeiro-SP, Peruíbe-SP, São Paulo (Pinheiros) -SP, Capão Bonito-SP, São Roque-SP,<br> São Paulo (Heliópolis) -SP, Bragança-SP Paulista-SP, Jaguariúna-SP, Tatuí-SP, Ivaiporã-SP,<br> Votuporanga-SP, Ribeirão Branco-SP, Mirassol-SP, São Vicente-SP, Mococa-SP,<br> Carapicuíba-SP, Ilha Comprida-SP.<br> <br> The total of participants was 343 adolescents and the questions and the corresponding answers are in the database.<br> </p>
FIGURE 7. The North Island, New Zealand with the entomological regions indicated with a in The genus Mecodema Blanchard 1853 (Coleoptera: Carabidae: Broscini) from the North Island, New Zealand
FIGURE 7. The North Island, New Zealand with the entomological regions indicated with a two-letter code as per Crosby et al. (1976). Note: For the purpose of the species distributions, the Three Kings Islands are included within the Northland (ND) region. The presence of each North Island Mecodema species within a region are indicated by the box with an arrow to that region. The species names in bold font are endemic to that region, i.e. the Auckland region has eight species of which six are endemic (bold font).
Charge regions indicated by LMA lightning flashes in Hokuriku's winter thunderstorms
<p>The attached is the dataset assoicated with the paper titled "Charge regions indicated by LMA lightning flashes in Hokuriku’s winter thunderstorms" which was submitted to Journal of Geophysical Research: Atmospheres.</p> <p>The charge distribution of some cells in three winter thunderstorms in the Hokuriku region of Japan is investigated based on Lightning Mapping Array (LMA) flash data. The vertical arrangements of charge regions involved in lightning discharges suggest diverse charge patterns, including quad-polar, tripole, positive dipole, inverted dipole, and inverted tripole. The riming electrification between graupel and ice crystals or their aggregations are thought to be responsible for the electrification of most cells. The charging process between snow/aggregates and ice crystals may be responsible for some inverted charge structure occurred above 0 ℃ isotherm and accompanying with weak radar echoes. Convection indicated by the vertical development of radar reflectivity appears crucial to shaping the diverse charge distribution patterns by determining which charging mechanisms occur and where; it also influences changes in height or even the disappearance of the charge regions. The charged cores are distributed from 0.7 to 5.3 km heights and 2 to –31 ℃ temperatures, while the distances between adjacent charged cores with opposite polarities change between 0.2 and 3.4 km, with a mean of 1.3 km. The mean flash duration, and horizontal distance are 425.0 ms and 19.8 km, respectively. The average height, temperature, and power of flash initiations are 2.8 km, −11.9 ℃ and 15.6 dBW, respectively.</p>
lil, left ilium;; lti, left tibia; pd, pedal digits; and ub, unidentified bony element. The white box indicates the position from which the sample was taken for histological analysis. Scale bars, 10 mm (a, c, d), 20 mm (b). in A new Jurassic scansoriopterygid and the loss of membranous wings in theropod dinosaurs
lil, left ilium;; lti, left tibia; pd, pedal digits; and ub, unidentified bony element. The white box indicates the position from which the sample was taken for histological analysis. Scale bars, 10 mm (a, c, d), 20 mm (b).
STORM Climate Indices Maps
<p>These data sets contains climate indices (for definitions please see README file) determined with the Climate Data Operator (cdo) software calculated based on EURO-CORDEX regional climate model projections using RCP4.5 and RCP8.5 for the period 2036-2065 as well as the time period 1971-2000 (baseline climate). The indices are based on an ensemble of EURO-CORDEX runs (see README file) after determining the indices based on each run individually, and are available for an area around the STORM pilot sites (located in Mellor, UK; Troia, Portugal; Rome, Italy; Rethymno, Greece; and Ephesus, Turkey). For more information and terms of use please consult the README file.</p>
Development and Implementation of Sustainable Transportation Resilience Indicators
<p>Communities are complex systems subject to a variety of hazards that can result in significant disruption to critical functions. Community resilience assessment is gaining popularity as a means to help communities better prepare for, respond to, and recover from disruption. Sustainable resilience, a recently developed concept, requires communities to assess system-wide capability to maintain desired performance levels, while simultaneously evaluating impacts to resilience due to changes in hazards and vulnerability over extended periods of time.</p> <p>In an earlier work, the authors developed a classification scheme to aid in identification, selection and application of community sustainable resilience indicators that can be tailored to a community’s needs in operationalizing the assessment process. These indicators were characterized according to whether they aligned with social, economic or environmental systems that are necessary for a community to achieve a sustainable resilience domain of survival, well-being, or full preparedness.</p> <p>Of the critical infrastructure systems that support these systems and domains, transportation is arguably the most important. This is based on the premise that transportation is a means to an end, providing the mobility that enables a community to establish and maintain a social, economic and environmental fabric. Whether it involves an educational, medical, recreational, religious, work or other purpose, absent a safe and reliable transportation system, none of these activities can be satisfactorily pursued. Moreover, in times of crisis, transportation serves as a vital artery for enabling access to and egress from impacted areas.</p> <p>The objective of this project was to establish and demonstrate a method for evaluating a community’s transportation resilience, such that if deficiencies exist, attention can be focused on mitigating those concerns. This approach was designed around the scenario of a river valley community exposed to the threat of a significant flood event, with the expectation that the methodology has the potential to be extended to assess community resilience to other natural and manmade hazards.</p>
FIGURES 21–22. Haplodrassus invalidus. 21 Epigyne. 22 Vulva. Arrow indicates posterior extensions, g in Ground spiders (Araneae: Gnaphosidae, Liocranidae, Prodidomidae) from the Greek islands Rodos, Symi and Karpathos, with the description of new species
FIGURES 21–22. Haplodrassus invalidus. 21 Epigyne. 22 Vulva. Arrow indicates posterior extensions, g: glandular heads.
Dataset of Indication of Quantitative Multiple Disease Resistance to Foliar Pathogens in Pinus radiata D.Don in New Zealand
<p>The file contains the phenotypic data and pedigree information used to produce the manuscript <strong>Indication of quantitative cross-resistance to foliar pathogens in </strong><strong><em>Pinus radiata </em></strong><strong>D.Don</strong></p>
text-fig. 61. Theropod radiations in Laurasia and Gondwana. Column thickness indicates number of taxa. For the Gondwanan columns, some generically indeterminate material has been included for stages for which no generically diagnostic material is known. in The interrelationships and evolution of basal theropod dinosaurs
text-fig. 61. Theropod radiations in Laurasia and Gondwana. Column thickness indicates number of taxa. For the Gondwanan columns, some generically indeterminate material has been included for stages for which no generically diagnostic material is known.
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.