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16,872 results for “Differences”
X-ray scattering data from Norway spruce at different moisture conditions
<p>This data includes small and wide-angle X-ray scattering (SAXS, WAXS) intensities measured for Norway spruce (<em>Picea abies</em>) wood.</p> <p>The experiments were done in perpendicular transmission geometry, with the wood fiber axis roughly vertical and the radial direction of the wood tissue parallel to the X-ray beam, using a Xenocs Xeuss 3.0 C SAXS/WAXS device and Cu K-alpha radiation (wavelength 1.542 Å). The scattering patterns were recorded using an EIGER2 R 1M detector (pixel size 75 µm). The wood sample was measured first in wet state (saturated with water; "Wet"), and then equilibrated at different relative humidities (RH) in the following order: 95% ("RH95_1st"), 85% ("RH85"), 70% ("RH70"), 50% ("RH50_1st"), 20% ("RH20"), 10% ("RH10"), 50% ("RH50_2nd"), 95% ("RH95_2nd"). The sample-to-detector distance was 0.4139 m in SAXS, and 0.1528 m for the first 4 conditions (until "RH70") and 0.1525 m for the remaining 5 conditions in WAXS. Beam center (in detector pixels) was at x=540.6, y=667.0 (except y=635.0 in "RH70") in SAXS and x=1540, y=1521 in WAXS.</p> <p>For each of the 9 moisture conditions, files corresponding to 3 different processing steps are provided:</p> <ul> <li>"_bgsub_saxs.txt" and "_bgsub_waxs.txt" are ASCII files that contain the normalized and background-subtracted detector images (intensity in units mm^-1) corresponding to SAXS and WAXS, respectively. Pixels to be masked have the value "nan".</li> <li>"_bgsub_saxs_pol90.txt" and "_bgsub_waxs_pol90.txt" contain azimuthally regrouped images (90 bins in azimuthal angle) based on "_bgsub_saxs.txt" and "_bgsub_waxs.txt", respectively. PNG image files "_bgsub_saxs_pol.png" and "_bgsub_waxs_pol.png" are provided for reference.</li> <li>"_bgsub_pol90_ibg.txt" and "_bgsub_waxs_pol90_vert_ibg.txt" contain the equatorial and meridional anisotropic intensities, respectively, which were obtained from the azimuthally regrouped images by subtracting the isotropic scattering from the equatorial or meridional intensity (sector width 25°) at each value of the scattering vector <em>q</em>. The equatorial anisotropic intensities from SAXS and WAXS were merged by scaling the SAXS intensity, and the meridional anisotropic intensity is provided for the WAXS range only. The files contain columns for the magnitude of the scattering vector (q, unit Å<sup>-1</sup>), anisotropic intensity (I_ani, unit mm<sup>-1</sup>), error of anisotropic intensity (dI_ani, unit mm<sup>-1</sup>), and isotropic intensity (I_iso, unit mm<sup>-1</sup>).</li> </ul> <p>More detailed descriptions of the sample, the measurement, and the data processing can be found in the following reference:<br> Antti Paajanen, Aleksi Zitting, Lauri Rautkari, Jukka A. Ketoja, Paavo A. Penttilä. Nanoscale mechanism of moisture-induced swelling in wood microfibril bundles. <em>Nano Letters</em> 2022, 22(13): 5143–5150, DOI: 10.1021/acs.nanolett.2c00822</p>
Refined Land cover for Beijing, Shanghai, Ningbo in China and Paris Region, Velika Gorica, Aarhus in Europe under different scenarios in 2030
<p>Europe and China Refined Land Cover 2030 (ECRLC2030) was derived from historical landcover observations, natural geographical, location, and socio-economic factors and the Conversion of Land Use and its Effects at Small Regional Extent model (CLUE-S). With a spatial resolution of 60m, landcover under three different scenarios were simulated: the business-as-usual scenario (BAU), the market-liberal scenario (MLS), and the ecological protection scenario (EPS).</p>
Organic micropollutants and heavy metals in stormwater runoff of five different catchment types in Berlin (Germany)
<p>This dataset includes concentrations of micropollutants (67), heavy metals (8) and standard parameters (9) for stormwater runoff taken from separated sewers of five catchments between 3 and 37 ha in Berlin (Germany). It also includes rain data of analyzed events as separate file. Samples were taken as part of the OgRe research project of Kompetenzzentrum Wasser Berlin (<a href="https://www.kompetenz-wasser.de/en/project/ogre/">www.kompetenz-wasser.de/en/project/ogre/</a>) in 2014 and 2015. Sampling and analytical methods are detailed in "Concentrations of micropollutants in urban stormwater runoff of different land uses" (<a href="https://doi.org/10.3390/w13091312">https://doi.org/10.3390/w13091312</a>). A dataset with concentrations of the urban stream Panke in Berlin during dry and wet weather (samples were taken as part of the same project) is available separately (<a href="https://zenodo.org/record/4633779">https://zenodo.org/record/4633779</a>).</p> <p><strong>Description of fields (concentrations):</strong></p> <ul> <li><strong>SampleID</strong>: unique sample identifier</li> <li><strong>SiteID</strong>: unique site identifier (catchment type) <ul> <li> 1 - OLD: area with typical five-storey perimeter blocks built between 1870 and 1930 (31 ha)</li> <li> 2 - NEW: newer area of 4-8-storey concrete slab buildings built between 1960 and 1980 (16 ha)</li> <li> 3 - STR: 1.3 km of a busy streeat with intersection with traffic lights and bus stops (3 ha)</li> <li> 4 - OFH: a residential area characterized by one-family houses and villas with gardens (17 ha)</li> <li> 5 - COM: a commercial and industrial area of high imperviousness with large flat-roof buildings and yards (37 ha)</li> <li> 6 - PNK: urban stream Panke (characterized by strong stormwater inputs from separate sewer discharges - available in separate dataset)</li> </ul> </li> <li><strong>LocalDateTime</strong>: start time of sampling (local)</li> <li><strong>DateTimeUTC</strong>: start time of sampling (UTC)</li> <li><strong>UTCOffset</strong>: UTC offset to local time in h</li> <li><strong>SampleType</strong>: either "composite" for volume proportional composite sample (all samples from storm sewers) or "single" for grab sample (all stream samples, separate dataset)</li> <li><strong>VariableName</strong>: name of analysed substance/parameter</li> <li><strong>UnitsAbbreviation</strong>: either "ug/L" (microgram per litre) or "mg/L" (milligram per litre)</li> <li><strong>CensorCode</strong>: either "lt" (less than) for concentration below detection limit (value is detection limit) or "nc" (not censored) for concentration above detection limit</li> <li><strong>DataValue</strong>: measured value (if censor code is lt, value indicates detection limit)</li> </ul> <p><strong>Description of fields (rain data):</strong></p> <ul> <li><strong>SampleID</strong>: sample identifier of matching sample (see above)</li> <li><strong>SiteID and SiteName</strong>: unique site identifier and name (catchment type) (see above)</li> <li><strong>tBeg_rain, tEnd_rain</strong>: begin and end of rain event in local time</li> <li><strong>depth.mm</strong>: rain depth of rain event in mm</li> <li><strong>duration_rain.h</strong>: duration of rain event in h</li> <li><strong>intensity_max_10min.mm_h</strong>: maximum rain intensitity of rain event in 10-min interval in mm/h</li> <li><strong>intensity_mean_event.mm_h</strong>: mean rain intensitity of rain event in mm/h</li> <li><strong>ADD.d</strong>: number of antecedent dry days in days</li> </ul> <p>Rain data was collected by rain gauge network of Berlin waterworks (>40 gauges) — gauge with best correlation between rain depth and event volume in storm sewer was chosen (distances to monitoring sites: 2–6 km).</p> <p>Two data files are provided in comma separated format:</p> <ul> <li>"OgRe_drain.csv" contains concentrations of all stormwater runoff samples taken in separate storm sewers</li> <li>"OgRe_rain.csv" contains rain data for all stormwater runoff samples</li> </ul>
Performance Criteria and Example Parameter Sets Comparing Different Variants of the Ensemble Kalman Filter as Applied to Volcanology
<p>This dataset contains the results of various Ensemble Kalman Filter (EnKF) inversions in which synthetic GNSS and InSAR observations from an inflating magma system are assimilated into numerical models of rock deformation around a pressurized ellipsoidal magma reservoir. Each inversion uses a different variant of the EnKF, with changes to workflow meta-parameters such as the number of ensemble members or the particular update algorithm used. In particular, each filter variant is evaluated by comparing the final output model to the original synthetic model. The specific performance criteria used include (1) the root mean square error (RMSE) between the model predictions and the assimilated observations, as well as normalized misfit terms measuring the filter's ability to resolve (2) reservoir wall tensile stress, (3) easily-observable unique parameters such as reservoir position and aspect ratio, and (4) difficult-to-derive non-unique parameters such as the specific size and internal pressure of the reservoir. The assimilated data include two different scenarios, one in which inflation is caused by pressurization and another in which it is driven by a lateral reservoir expansion. Both datasets are tested with each EnKF variant. Finally, we include example matrices from within an EnKF update step to demonstrate inter-parameter correlations that develop during the assimilation and how they can be mitigated through randomization.</p>
Mass spectrometry raw data for "Proteomics reveals substantial differences between in vitro matured abattoir-derived and in vivo matured oocytes in cattle"
<p><em><span>In vitro</span></em><span> production (IVP) of bovine embryos still has its limitations such as low blastocyst rate and lower embryo quality, resulting in lower pregnancy rates following the transfer of IVP embryos compared to <em>in vivo</em> produced embryos. </span><span>Given these differences in developmental competence, RNA sequencing and microarray technology have been applied to describe the differences in transcriptional activity between <em>in vitro</em> and <em>in vivo</em> produced embryos. All but one of these studies solely utilized oocytes obtained from slaughterhouse material for the <em>in vitro</em> production of embryos, thereby introducing the possibility, that differences between IVP and <em>in vivo</em> embryos are in part attributable to differing sources of oocytes. The aim of the present study was therefore to compare the proteome of oocytes retrieved from slaughterhouse material, with and without a period of <em>in vitro</em> maturation and <em>in vivo</em> matured oocytes obtained from donor cattle following superovulation. <span>For each group the protein pattern of four biological replicates containing ten oocytes each were analyzed via SWATH<sup>TM</sup>-MS.</span></span></p>
MOD17A2H version 6 Gross Primary Productivity (GPP) global mosaics at 500 m resolution and difference 2000-2017
<p>MOD17A2H version 6 Gross Primary Productivity (GPP) global mosaics at 500 m resolution and difference in GPP for the period 2000-2017. Changes in GPP could be used to estimate land degradation or similar. Derived using <a href="https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/MOD17A2H">the data.table package and quantile function in R</a>. For more info about the MODIS LST product see: https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod17a2h_v006. Antartica is not included.</p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>veg = theme: vegetation,</li> <li>gpp = variable: gross primary productivity in kg C m<sup>2</sup>,</li> <li>mod17a2h.oct = determination method: MOD17A2H product, GPP values for October,</li> <li>d = median value / difference = difference between periods / u.975 = aggregation/statistics method: 97.5% probability upper quantile,</li> <li>500m = spatial resolution / block support: 500 m,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2000..2017 = time reference: from 2000 to 2017,</li> <li>v1.0 = version number: 1.0,</li> </ul>
Data for the publication: Recombinant silk protein condensates show widely different properties depending on the sample background
<p>This entry includes raw data for the publication "Recombinant silk protein condensates show widely different properties depending on the sample background". The original publication was published in: Journal of Materials Chemistry B, DOI: 10.1039/d4tb01422g</p> <p>The folder "Videos_Micropipette_Aspiration_Zenodo.zip" contains 9 TIF files, labeled Number1 - Number9. The numbering corresponds to the numbering of IMAC condensates studied with micropipette aspiration in the publication. Each TIF file is an image stack from a time series.</p> <p>The folders "Videos_IMAC_silk_with_BG_lysate_coalescence.zip", "Videos_HT_silk_coalescence.zip", and "Videos_IMAC_silk_coalescence.zip" all contain subfolders labeled with the purification method, the framerate of the videos and then consecutive numbering. Each of these folders contains the frames of the video as single TIF files.</p> <p>Please find more information in the read_me file uploaded.</p>
Urban pluvial flood maps under different green cover scenarios
<p>This dataset provides pluvial flood water depth maps for the cities of Logroño, Spain; Gdynia, Poland; Milan Italy; and Athens Greece as a part of the REACHOUT project. The maps are generated using a Pluvial Flood Tool for different return periods estimated based on observations and EURO-CORDEX future climate change scenarios (Logroño only) under different nature-based green cover scenarios, depending on the city.</p> <p>Technical Info</p> <p>The pluvial flood hazard maps are generated for each event using rainfall intensity as input for the hydrostatic inundation model SaferRAIN (Samela et al., 2020). This is a simplified raster-based model based on a hierarchical filling and spilling algorithm, identifying inundated areas on the basis of high-resolution digital elevation model. It accounts for spatially distributed rainfall input and infiltration, building upon the pixel-based Green-Ampt model (Green and Ampt, 1911). It is suitable for applications over large urban areas.</p> <p>Rainfall input for the pluvial flood model is computed for return periods (RPs) of 2-, 5-, 10-, 25-, 50-, 100-, 200-years based on the historical rainfall data. Different datasets have been utilized in various cities to tailor the analysis to their specific needs. More specifically:</p> <ul> <li> <p>In the city of Gdynia, historical local station data (Climate data IMGW 1960-2021: https://danepubliczne.imgw.pl/) are used to estimate RPs and assess different precipitation events. </p> </li> </ul> <ul> <li> <p>For the cities of Milan and Athens, 2.2-km ERA5 downscaled data are employed to assess historical precipitation events under different RPs (Essenfelder et al., 2021). </p> </li> <li> <p>In the city of Logroño, historical local station data (SOS-Logroño precipitation data 1999-2022: https://www.larioja.org/emergencias-112/es/meteorologia/datos-actuales-rioja/detalle-estacion?homepage=9&cod_muni=89) are used to estimate RPs and assess different precipitation events. Additionally, here, future climate change projections have been analyzed. These projections are based on the precipitation Intensity-Duration-Frequency (IDF) curves computed from the EURO-CORDEX data (Pal J et al., 2024 - <a href="https://doi.org/10.5281/zenodo.14035736" target="_blank" rel="noopener">10.5281/zenodo.14035736</a>). Observations are then scaled according to the changes simulated between future and historical scenarios, using the median and 90th percentile values estimated from the EURO-CORDEX ensemble.</p> </li> </ul> <p>Different urban green cover maps are used as input for the model to simulate the pluvial flood maps under the current land cover conditions and for different nature-based adaptation scenarios for each city to estimate their benefits. Nature-based adaptation scenarios are the result of codesign processes carried out within REACHOUT, involving local stakeholders, experts and representatives of local administrations. Urban green cover scenarios were identified based on areas that could be converted from built-up areas and concrete surfaces (no water infiltration) to green areas allowing for rainwater infiltration. In addition, during this process, local station precipitation, high-resolution digital elevation model and high-resolution land cover data were collected to configure and run the pluvial flood model.</p> <p>Short description of the datase:</p> <p>This dataset contains urban pluvial flood maps for return periods of 2-, 5-, 10-, 25-, 50-, 100-, 200-years for hourly and 15-minute events for different urban green cover scenarios and climate change scenarios depending on the city.</p> <p>Format:</p> <p>The format of this dataset is organized in a ZIP file: PluvialFloodMap_{Cityname}.zip. The zip file is organised into sub-folders, one for each urban green cover scenario, including raster (Tiff) files for the rainfall event associated with each return period.</p> <p>Logrono:</p> <ul> <li> <p>Precipitation events historical: 15-minute events – 9.79 mm (RP2), 13.51mm (RP5), 16.27 mm (RP10), 20.15 mm (RP25), 23.33 mm (RP50), 26.77 mm (RP100), 30.50 mm (RP200)</p> </li> <li> <p>Precipitation events climate change: 15-minute events – CC_Q50 (median): 10.49 mm (RP2), 14.91 mm (RP5), 18.32 mm (RP10), 23.18 mm (RP25), 26.61 mm (RP50), 31.25 mm (RP100), 35.40 mm (RP200): CC_Q90 (90th percentile): 11.83 mm (RP2), 16.76 mm (RP5), 20.96 mm (RP10), 26.87 mm (RP25), 32.27 mm (RP50), 38.99 mm (RP100), 46.65 mm (RP200)</p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>NBS planned: baseline + additional 4 bioswales/ponds (= 29,850 m3) and a green corridor (5.3 km x 5 m) in the southern part of the city.</p> </li> <li> <p>NBS planned plus: NBS planned scenarios + additional small ponds/rain gardens (depth 0.5 m, 13,500 m3)</p> </li> <li> <p>All Green: baseline + all open spaces converted to green</p> </li> </ul> <p>Milan</p> <ul> <li> <p>Precipitation events historical: 1-hour events – 33.36 mm (RP5), 38.52 mm (RP10), 45.04 mm (RP25), 49.88 mm (RP50), 54.68 mm (RP100)</p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>DMG_Green Buildings*: baseline + establishment of new green roofs, defined to prioritise economic damage reduction</p> </li> <li> <p>DMG_Green Spaces*: baseline + open, ground spaces converted to green, defined to prioritise economic damage reduction</p> </li> <li> <p>DMG_Green City*: combination of Green Buildings and Green Spaces scenarios, defined to prioritise economic damage reduction</p> </li> <li> <p>POP_Green Buildings*: baseline + establishment of new green roofs, defined to prioritise exposed population reduction</p> </li> <li> <p>POP_Green Spaces*: baseline + open, ground spaces converted to green, defined to prioritise exposed population reduction</p> </li> <li> <p>POP_Green City*: combination of Green Buildings and Green Spaces scenarios, defined to prioritise exposed population reduction</p> </li> </ul> <p>* Each green conversion scenario considers four different incremental conversion percentages: 25%, 50%, 75%, and 100% of all potential green areas.</p> <p>Gdynia</p> <ul> <li> <p>Precipitation events historical: 6-hours events – 24.89 mm (RP2), 35.93 mm (RP5), 43.55 mm (RP10), 53.19 mm (RP25), 60.60 mm (RP100), 75.74 mm (RP200) </p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>NBS: baseline + bioswales/ponds (+ 50,000 m3)</p> </li> <li> <p>All green: baseline + all open spaces converted to green</p> </li> <li> <p>NBS All green: all green + NBS</p> </li> </ul> <p>Athens</p> <ul> <li> <p>Precipitation events historical: 1-hour events – 28.05 mm (RP5), 34.08 mm (RP10), 42.28 mm (RP25), 48.83 mm (RP50), 55.74 mm (RP100)</p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>NBS: baseline + ponds/rain gardens in existing green spaces (depth 1m) in the northern district of the city</p> </li> <li> <p>All green: baseline + all open spaces (>100 m2) converted to green</p> </li> </ul>
Crowdsourcing vibration data stemming from different transportation usages
<p> </p> <p>Crowdsourcing vibration data stemming from different activities and transportation usages (by trains, by buses, by bicycles by walking). We present a comprehensive dataset that provides the pattern of five activities walking, cycling, taking a train, a bus or a taxi. The measurements are carried out by embedded sensor accelerometer in smartphones. The dataset offers dynamic responses of subjects carrying smartphones in varied styles as they performing the five activities through vibrations acquired by accelerometers. The dataset contains corresponding time stamps and vibrations in three directions longitudinal, horizontal, and vertical stored in an Excel Macro-enabled Workbook (xlsm) format can be used to train an AI model in a smartphone which has potentials to collect people’s vibration data and decides what movement is being conducted. Besides, with more data are received, the database can be updated and it can be fed to train the model with a larger dataset. The prevalent of the smartphone opens the door of crowdsensing which leads to the pattern of people talking public transports can be understood. Furthermore, the time consumed in each activity is available in the dataset. Therefore, with a better understanding of people using public transports, the service and schedule can be planned perceptively. Activities to obtain the dataset are jointly funded by H2020 and Hitachi Europe.</p>
Data from systematic audit for paper: Insights into the quantification and reporting of model-related uncertainty across different disciplines
<p>This upload contains 7 data files (each contains cleaned and compiled data for a given scientific field) and 2 R scripts. These files support the paper: Insights into the quantification and reporting of model-related uncertainty across different disciplines.</p> <p> </p> <p><strong>Description of the data</strong></p> <p>Compiled data files for each field contain all reviewers audit answers for eligible papers. All papers that met exclusion criteria have been removed.</p> <p>Data checks have been performed and formatting errors corrected either in R or manually, following steps detailed in the STAR methods.</p> <p>Column names and description:</p> <ul> <li>Number: number of question from 1 to 9</li> <li>Questions: question text – question to be answered by the reviewer</li> <li>QuestionCode: shortened code for each question</li> <li>Paper: paper code - first author surname/initial and surname and year</li> <li>Initials: initials of reviewer</li> <li>Answer: answer to the question</li> <li>Details: extra details to support the answer</li> <li>Location: where in the text the uncertainty was presented</li> <li>Presentation: how the uncertainty was presented</li> <li>ModelType: type of model (focal model)</li> <li>Comments: any other comments from the reviewer</li> <li>Checks: checks of whether NA or no have been included in correct places e.g. if answers to questions 1:4 are no then question 9 is NA, if question 7 is no then 8 is NA</li> <li>Check 1 = when Answer = No, Location is NA</li> <li>Check 2 = when Answer to Number 1-4, 6 or 8-9 is Yes that Details are not NA</li> <li>Check 3 = when Answer = No, Presentation = NA</li> <li>Check 4 = when Location is not NA, presentation is not NA</li> <li>Check 5 = if the Answer to 5 or 7 is "No" then Answer to 6 and 8 = "NA"</li> <li>Check 6 = if Answer for 1-4 is "No", then Answer for 9 = "NA"</li> </ul> <p><strong>Code description</strong></p> <p>Two scripts are included, the first is theme_script.R, this includes code to set up a ggplot theme for the figures. The second is Figure_code.R, this script contains all code to plot and save the three figures from the paper.</p>
Methane losses from different biogas plant technologies
<p>This dataset and R code supplement the publication "Methane losses from different biogas plant technologies" by Wechselberger et al. (2023).</p> <p>The dataset contains primary and secondary data underlying the reported emission factors. By using the R code, emission factors are calculated as published.</p> <p>Available files:</p> <ul> <li>Glossary.csv (column/variable descriptions of dataset)</li> <li>Wechselberger_et_al_2023_data.csv (dataset)</li> <li>Wechselberger_et_al_2023_R_code.Rmd (code for calculating the emission factors reported in Table 2 of the publication)</li> <li>Wechselberger_et_al_2023_data_supplement.zip (containing all of the files above)</li> </ul> <p>Version v2 contains the final reference to the publication Wechselberger et al. (2023). The data are the same as in version v1.</p>
Dataset Treatment of early childhood caries with three different topical fluoride treatments: a randomised clinical trial
<p>These files contain the data and the research script about the effectiveness of the 38% silver diamine fluoride (SDF, SDI Riva Star) and Tiefenfluorid (TF, Humanchemie GmbH), using two different application protocols in children ≤71 months of age with early childhood caries, with a focus on patient-centred outcomes including major complications (pain, abscess, extraction) and minor complications (lesion progression).</p> <p>This work was funded by European Regional Development Fund Postdoc Latvia 1.1.1.2/VIAA/3/19/543, Contract No 9.-14.5/27. Uribe was supported by European Union’s Horizon 2020 grant agreement 857287 for the Baltic Biomaterials Centre of Excellence.</p>
Population genomics reveals differences in genetic structure between two endemic arboreal rodent species in threatened cloud forest habitat
<p>SNPs obtained by UNEAK pipeline for <em>Habromys schmidlyi </em>and <em>Reithrodontomys microdon</em>. </p> <p>Pleae cite as: </p> <p>Colunga-Salas P., T Marines-Macías, G Hernández-Canchola, S Barbosa, C Ramírez, JB Searle, L León-Paniagua. 2022. <strong>Population genomics reveals differences in genetic structure between two endemic arboreal rodent species in threatened cloud forest habitat</strong>. Mammalian Reasearch. Doi: 10.1007/s13364-022-00667-x</p>
Performance Data of an Ice-Melting Probe from Field Tests in two Different Ice Environments
<p>This dataset was acquired at field tests of the steerable ice-melting probe "EnEx-IceMole" (Dachwald et al., 2014). A field test in summer 2014 was used to test the melting probe's system, before the probe was shipped to Antarctica, where, in international cooperation with the MIDGE project, the objective of a sampling mission in the southern hemisphere summer 2014/2015 was to return a clean englacial sample from the subglacial brine reservoir supplying the Blood Falls at Taylor Glacier (Badgeley et al., 2017, German et al., 2021).</p> <p>The standardized log-files generated by the IceMole during melting operation include more than 100 operational parameters, housekeeping information, and error states, which are reported to the base station in intervals of 4 s. Occasional packet loss in data transmission resulted in a sparse number of increased sampling intervals, which where compensated for by linear interpolation during post processing. The presented dataset is based on a subset of this data: The penetration distance is calculated based on the ice screw drive encoder signal, providing the rate of rotation, and the screw's thread pitch. The melting speed is calculated from the same data, assuming the rate of rotation to be constant over one sampling interval. The contact force is calculated from the longitudinal screw force, which es measured by strain gauges. The used heating power is calculated from binary states of all heating elements, which can only be either switched on or off. Temperatures are measured at each heating element and averaged for three zones (melting head, side-wall heaters and back-plate heaters).</p>
A dataset from a survey investigating disciplinary differences in data citation
<p><strong>GENERAL INFORMATION</strong></p> <p><em>Title of Dataset: </em> A dataset from a survey investigating disciplinary differences in data citation</p> <p><em>Date of data collection:</em><strong> </strong>January to March 2022</p> <p><em>Collection instrument: </em>SurveyMonkey</p> <p><em>Funding:</em> Alfred P. Sloan Foundation</p> <p><br> <strong>SHARING/ACCESS INFORMATION</strong></p> <p><em>Licenses/restrictions placed on the data: </em>These data are available under a <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0 license</a> </p> <p><em>Links to publications that cite or use the data: </em></p> <p>Gregory, K., Ninkov, A., Ripp, C., Peters, I., & Haustein, S. (2022). Surveying practices of data citation and reuse across disciplines. Proceedings of the 26th International Conference on Science and Technology Indicators. <em>International Conference on Science and Technology Indicators</em>, Granada, Spain. https://doi.org/10.5281/ZENODO.6951437</p> <p>Gregory, K., Ninkov, A., Ripp, C., Roblin, E., Peters, I., & Haustein, S. (2023). <em>Tracing data:<br> A survey investigating disciplinary differences in data citation.</em> Zenodo. https://doi.org/10.5281/zenodo.7555266</p> <p><br> <strong>DATA & FILE OVERVIEW</strong></p> <p><em>File List</em></p> <ul> <li>Filename: MDCDatacitationReuse2021Codebookv2.pdf<br> <em>Codebook</em></li> <li>Filename: MDCDataCitationReuse2021surveydatav2.csv<br> <em>Dataset format in csv</em></li> <li>Filename: MDCDataCitationReuse2021surveydatav2.sav<br> <em>Dataset format in SPSS</em></li> <li>Filename: MDCDataCitationReuseSurvey2021QNR.pdf<br> <em>Questionnaire</em></li> </ul> <p><em>Additional related data collected that was not included in the current data package: </em>Open ended questions asked to respondents</p> <p><br> <strong>METHODOLOGICAL INFORMATION</strong></p> <p><em>Description of methods used for collection/generation of data: </em></p> <p>The development of the questionnaire (Gregory et al., 2022) was centered around the creation of two main branches of questions for the primary groups of interest in our study: researchers that reuse data (33 questions in total) and researchers that do not reuse data (16 questions in total). The population of interest for this survey consists of researchers from all disciplines and countries, sampled from the corresponding authors of papers indexed in the Web of Science (WoS) between 2016 and 2020. </p> <p>Received 3,632 responses, 2,509 of which were completed, representing a completion rate of 68.6%. Incomplete responses were excluded from the dataset. The final total contains 2,492 complete responses and an uncorrected response rate of 1.57%. Controlling for invalid emails, bounced emails and opt-outs (n=5,201) produced a response rate of 1.62%, similar to surveys using comparable recruitment methods (Gregory et al., 2020).</p> <p><em>Methods for processing the data: </em></p> <p>Results were downloaded from SurveyMonkey in CSV format and were prepared for analysis using Excel and SPSS by recoding ordinal and multiple choice questions and by removing missing values.</p> <p><em>Instrument- or software-specific information needed to interpret the data: </em></p> <p>The dataset is provided in SPSS format, which requires IBM SPSS Statistics. The dataset is also available in a coded format in CSV. The Codebook is required to interpret to values.</p> <p><br> <strong>DATA-SPECIFIC INFORMATION FOR: MDCDataCitationReuse2021surveydata</strong></p> <p><em>Number of variables:</em> 95</p> <p><em>Number of cases/rows:</em> 2,492</p> <p><em>Missing data codes:</em> 999 Not asked</p> <p>Refer to MDCDatacitationReuse2021Codebook.pdf for detailed variable information.</p>
Data on different types of green spaces and their accessibility in the seven largest urban regions in Finland
<p>This repository contains data described in the article "Data on different types of green spaces and their accessibility in the seven largest urban regions in Finland" (Heikinheimo et al. 2023) and used in the research article "Associations of neighborhood-level socioeconomic status, accessibility, and quality of green spaces in Finnish urban regions" (Viinikka et al. 2023). <br> <br> This repository contains data on green space quality and path distances to different types of green spaces. The path distances represent green space accessibility using active travel modes (walking, cycling). The path distances were calculated using the pedestrian street network across the seven largest urban regions in Finland. We derived the green space typology from the Urban Atlas Data that is available across functional urban areas in Europe and enhanced it with national data on water bodies, conservation areas and recreational facilities and routes from Finland. We extracted the walkable street network from OpenStreetMap and calculated shortest paths to different types of green spaces using open-source Python programming tools. Network distances were calculated up to ten kilometers from each green space edge and the distances were aggregated into a 250 m x 250 m statistical grid that is interoperable with various statistical data from Finland. The geospatial data files representing the different types of green spaces, network distances across the seven urban regions, as well as the processing and analysis scripts are shared in an open repository. These data offer actionable information about green space accessibility in Finnish city regions and support the integration of green space quality and active travel modes into further research and planning activities.</p> <p> </p> <p><strong>Data description article: </strong></p> <p>Heikinheimo, V., Tiitu, M., & Viinikka, A. (2023). Data on different types of green spaces and their accessibility in the seven largest urban regions in Finland. <em>Data in Brief</em>, <em>50</em>, 109458. <a href="https://doi.org/10.1016/j.dib.2023.109458">https://doi.org/10.1016/j.dib.2023.109458</a></p> <p><strong>Related research article:</strong> </p> <p>Viinikka, A., Tiitu, M., Heikinheimo, V., Halonen, J. I., Nyberg, E., & Vierikko, K. (2023). Associations of neighborhood-level socioeconomic status, accessibility, and quality of green spaces in Finnish urban regions. <em>Applied Geography</em>, <em>157</em>, 102973. <a href="https://doi.org/10.1016/j.apgeog.2023.102973">https://doi.org/10.1016/j.apgeog.2023.102973</a></p>
Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes
<p><strong>Abstract</strong></p> <p>Brain ageing is a highly variable, spatially and temporally heterogeneous process, marked by numerous structural and functional changes. These can cause discrepancies between individuals’ chronological age and the apparent age of their brain, as inferred from neuroimaging data. Machine learning models, and particularly Convolutional Neural Networks (CNNs), have proven adept in capturing patterns relating to ageing induced changes in the brain. The differences between the predicted and chronological ages, referred to as brain age deltas, have emerged as useful biomarkers for exploring those factors which promote accelerated ageing or resilience, such as pathologies or lifestyle factors. However, previous studies rely only on structural neuroimaging for predictions, overlooking potentially informative functional and microstructural changes. Here we show that multiple contrasts derived from different MRI modalities can predict brain age, each encoding bespoke brain ageing information. By using 3D CNNs and UK Biobank data, we found that 57 contrasts derived from structural, susceptibility-weighted, diffusion, and functional MRI can successfully predict brain age. For each contrast, different patterns of association with non-imaging phenotypes were found, resulting in a total of 191 unique, statistically significant associations. Furthermore, we found that ensembling data from multiple contrasts results in both higher prediction accuracies and stronger correlations to non-imaging measurements. Our results demonstrate that other 3D contrasts and modalities, which have not been considered so far for the task of brain age prediction, encode different information about the ageing brain. We envision our work as being the starting point for future investigations into the causal links underpinning the observed brain age deltas and non-imaging measurement associations. For instance, drug effects can be monitored, given that certain medications correlated with accelerated brain ageing. Furthermore, continued development of brain age models could facilitate their deployment in clinical trials for recruitment and monitoring, and hospitals for diagnostic and screening tasks.</p> <p><strong>Data Description</strong></p> <p>This dataset contains the full correlation results with all nIDPs in the UK Biobank. These are presented in datasets split by sex in Female and Male subjects. For easier data manipulation, two smaller datasets have also been made available, containing just those correlation which pass the False Discovery Rate (FDR) threshold. </p> <p>As experiments were also conducted for ensembles using multiple contrasts, similar datasets are provided for those.</p> <p>Finally, global datasets are also provided. These are the concatenation of the associations contained in the Male and Female datasets.</p> <p><strong>Paper & Code</strong></p> <p>The original paper for this article can be accessed here:</p> <ul> <li><a href="https://ieeexplore.ieee.org/abstract/document/10196736">https://ieeexplore.ieee.org/abstract/document/10196736</a></li> </ul> <p>To access the codes relevant for this project, please access the project GitHub Repos:</p> <ul> <li><a href="https://github.com/AndreiRoibu/AgeMapper">https://github.com/AndreiRoibu/AgeMapper</a></li> </ul> <p>If using this work, please cite it based on the above paper, or using the following BibTex:</p> <pre><code class="language-markdown">@inproceedings{roibu2023brain, title={Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes}, author={Roibu, Andrei-Claudiu and Adaszewski, Stanislaw and Schindler, Torsten and Smith, Stephen M and Namburete, Ana IL and Lange, Frederik J}, booktitle={2023 10th IEEE Swiss Conference on Data Science (SDS)}, pages={17--25}, year={2023}, organization={IEEE}, doi={10.1109/SDS57534.2023.00010} }</code></pre> <p> </p> <p><strong>Data Access</strong></p> <p>The data for this project is freely available upon application at the UK Biobank. For more information regarding the individual nIDPs, please access the UK Biobank Showcase website at: https://biobank.ctsu.ox.ac.uk/showcase/search.cgi</p> <p><strong>Funding</strong></p> <p>ACR is supported by EPSRC Grant EP/S024093/1, F. Hoffmann-La Roche AG and a 2021 Industrial Fellowship offered by the Royal Commission for the Exhibition of 1851. SMS is supported by a Wellcome Trust Collaborative Award 215573/Z/19/Z. AILN is grateful for support from the Academy of Medical Sciences under the Springboard Awards scheme (SBF005/1136), and the Bill and Melinda Gates Foundation. FJL is supported by a Wellcome Trust Collaborative Award (215573/Z/19/Z). The WIN is supported by core funding from the Wellcome Trust (203139/Z/16/Z). The computational aspects were supported by the Wellcome Trust (203141/Z/16/Z) and the NIHR Oxford BRC. Corresponding authors: ACR (andreiroibu@icloud.com), SA (stanislaw.adaszewski@roche.com) and AILN (ana.namburete@cs.ox.ac.uk).</p>
Datasets of "Differences in the stool metabolome between vegans and omnivores: analyzing the NIST stool reference material" publication
<p>To gain confidence in results of omic-data acquisitions, methods must be benchmarked by validated quality control materials. We here report data combining both untargeted and targeted metabolomics assays for the analysis of four new human fecal reference materials developed by the U.S. National Institute of Standards and Technologies (NIST) for metagenomics and metabolomics measurements. These reference grade test materials (RGTM) were established by NIST based on two different diets and two different samples treatments: homogenized fecal matter from subjects eating vegan diets, stored and submitted in either lyophilized (RGTM 10162) or aqueous form (RGTM 10171); secondly, homogenized fecal matter from subjects eating omnivore diets, stored and submitted in either lyophilized (RGTM 10172) or aqueous form (RGTM 10173). We used four untargeted metabolomics assays (lipidomics, primary metabolites, biogenic amines and polyphenols) and one targeted assay on bile acids.</p>
REU data set from summer of 2022. Project was designed to understand how crayfish (Faxonius rusticus) respond to chemical cues from largemouth bass predators under different shelter distributions.
Research into predator–prey interactions has focused on the landscape of fear and nonconsumptive effects that result from prey responses. Prey behavior is influenced by predator presence and the location and quality of foraging resources in habitats. These areas have been fruitful, but the role of prey refuges has lagged. We investigated how refuge spatial distribution and quality influence prey behavior. To determine the role of the landscape of safety (LOS) in prey decision-making, we altered spatial relationships between refuges, refuge quality, and predation threats in mesocosms. Mesocosms were constructed such that prey only received predatory chemical cues. We employed a behavioral assay including largemouth bass (Micropterus salmoides (Lacepède, 1802): predator) and virile crayfish (Faxonius rusticus (Girard, 1852): prey). Crayfish shelter use was significantly influenced by quality and spatial relationship of shelters to predatory threats, and the interaction of these two factors. Particularly, crayfish used high-quality shelters more often when located closer to predatory cues than farther away and did not use low-quality shelters more than controls. High-quality shelter usage decreased as threat level (measured by gape ratio) decreased. These results support the idea that prey utilize an LOS, and information contained in these two landscapes may alter behavioral decisions.
Monitoring of Microtus ochrogaster and Microtus pennsylvanicus populations in three different habitats in east-central Illinois, 1972 to 1997.
Populations of 2 species of arvicoline rodents, the prairie vole (Microtus ochrogaster) and meadow vole (Microtus pennsylvanicus), were monitored monthly from 1972-1997 in three distinct habitats: restored tallgrass prairie, bluegrass (Poa pratensis) and alfalfa (Medicago sativa). The study sites were located in the University of Illinois Biological Research Area (Phillips Tract) and Trelease Prairie. Tallgrass prairie was the original habitat of both species in Illinois. Bluegrass, an introduced species, represents the more common habitat in which the two species can be found today in Illinois. Alfalfa, an atypical habitat, provides an abundant source of high-quality food for both species. At each station, one wooden multiple-capture live-trap was placed. Every month, a two-day period of prebaiting was followed by a 3-day trapping session. The data include the species, individual identification, grid station, sex, reproductive status and body mass. Over the span of 25 years, three trapping sessions monthly were conducted to cover the three habitats, dedicating three weeks each month. Several papers have been based on these data.
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.