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16,872 results for “Differences”
Long-term composited and land cover-adjusted Enhanced Normalized Difference Impervious Surface Index (ENDISI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2020
This data package consists of multiple decades of Enhanced Normalized Difference Impervious Surface Index (ENDISI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona, USA, temporally aggregated by year and by four meteorological seasons (winter, spring, summer, fall). To serve as a proxy measurement of impervious surface and urbanization across years and seasons, we derived values of ENDISI – following the methods of Chen et al. 2019 from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. Next, we corrected the underestimated ENDISI values of dark impervious surface cover and the overestimated ENDISI values of bright bare soils based on visible Landsat bands and 2020 land cover (Sabu et al. 2023). Finally, we exported images as individual GeoTIFF raster files, each with five bands corresponding values summarized annually (band 1) and seasonally (bands 2-5). All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031 - Sabu, S., Frazier, A., & Rashid, B. (2023). Land use and land cover (LULC) classification of the CAP LTER study area (central Arizona, USA) using Landsat imagery: 2015 and 2020 [Dataset]. Environmental Data Initiative. https://doi.org/10.6073/PASTA/BF18E5856215BD2D4DAB3B024BA87A7E
Long-term composited Enhanced Normalized Difference Impervious Surface Index (ENDISI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2023
This data package consists of multiple decades of Enhanced Normalized Difference Impervious Surface Index (ENDISI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona, USA, temporally aggregated by year and by four meteorological seasons (winter, spring, summer, fall). To serve as a proxy measurement of impervious surface and urbanization across years and seasons, we derived values of ENDISI – following the methods of Chen et al. 2019 – from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. Finally, we exported images as individual GeoTIFF raster files, each with five bands corresponding values summarized annually (band 1) and seasonally (bands 2-5). All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031
Long-term composited Normalized Difference Vegetation Index (NDVI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2023
### overview This data package consists of multiple decades of normalized difference vegetation index (NDVI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona (USA), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). To serve as a proxy measurement of vegetation greenness and productivity across years and seasons, NDVI was derived from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031
Soil microbial and physicochemical data from watersheds impacted by different management practices or wildfire in the Southern Appalachian Mountains, 2023
Four forested watersheds in Western North Carolina with different management practices or disturbance were sampled in the summer of 2023 to compare soil physicochemical, microbial, and functional differences. These data include mineral soil physicochemical properties (location, elevation, aspect, gravimetric moisture content, pH, total carbon and nitrogen, total organic carbon, dissolved organic carbon and nitrogen, total dissolved nitrogen, dissolved inorganic nitrogen (NO3 and NH4), and microbial biomass carbon and nitrogen), soil microbial properties (16S ASV community sequences, ITS ASV community sequences, extracellular enzyme activity, carbon mineralization rates, and ammonium mineralization rates), and organic soil properties (total organic carbon, total carbon and nitrogen, 16S ASV sequences, pH, and moisture). Together, this dataset provides context to understanding the impacts of different management practices and relevant disturbances, such as severe wildfire, on soil in the Southern Appalachian region.
Endurance swimming performance and physiology of juvenile Green Sturgeon (Acipenser medirostris) at different temperatures, CA, 2022
This dataset provides information on the endurance swimming performance and physiological responses of juvenile Green Sturgeon (Acipenser medirostris), reared and tested at the University of California, Davis, in 2022. Fish were acclimated to two temperature treatments (13°C and 18°C) for 14 days prior to swimming trials. Endurance tests were conducted at 47–53 days post-hatch (DPH) in modified swim tunnels at fixed water velocities (25–55 cm s⁻¹) to measure time-to-fatigue (End.min), station-holding behavior (Station_holding), and swimming type (Swim.type). Fish morphometrics (e.g., weight, fork length, total length) were recorded before trials. Post-swim physiological analyses included whole-body measurements of cortisol, glucose, lactate, and protein. Tissue homogenates were processed to determine concentrations normalized to fish weight (e.g., Cortisol_ng_g, Glucose_ug_g, Lactate_ug_g). Standard curves showed high assay linearity (R² > 0.98) and low variability (CV < 10%). This dataset contributes to understanding sturgeon endurance and physiological stress under different environmental conditions, providing insights into their resilience to temperature and flow changes relevant to river management and conservation efforts. Variables include: species, developmental stage (DPH), rearing and trial conditions (tank, temperature, velocity), fish morphometrics (weight, fork length, total length), and physiological metrics (cortisol, protein, glucose, and lactate).
Long-term soil properties after different biochar feedstock treatments in a Southwest Virginia Pasture, 2024
Biochar is an agricultural amendment that can improve soil health and promote carbon (C) sequestration. Effects of biochar can vary and depend on the biochar feedstock, method of production, soil conditions, and amendment method and frequency. These data include soil physicochemical properties from plots amended with hay, softwood, and hardwood biochar types produced under similar conditions (479°C – 522°C for 3.5-10.2 hours) with and without a nitrogen addition (porcine blood meal) in a randomized complete block design after 4.5 years. Plots were first established at the Virginia Tech Catawba Sustainability Center in Catawba, VA in June of 2019 and sampled in March 2024. Soil measurements include total nitrogen, total carbon, carbon:nitrogen ratios, gravimetric moisture, pH, electrical conductivity, dissolved inorganic nitrogen (NO3 and NH4), bulk density, and moisture from bulk density measurements. These data contribute to a long-term understanding of different biochar feedstock effects on Southwest Virginia pasture soils.
Summary of three different Leaf Area Index (LAI) methodologies of 19 1m x 1m point frame plots sampled near the LTER Shrub plots at Toolik Field Station in AK the summer of 2012.
Summary of three methods used to estimate the Leaf Area Index (LAI) of 19 1m x 1m plots sampled with a point frame near the LTER Shrub plots at the Toolik Field Station in AK the summer of 2012. The methods used were: (1) exponential relationship between LAI and Normalized Leaf Index (NDVI) as measured above the canopy with a Unispec spectroradiometer; (2) Delta-T SunScan canopy analyzer held at 5 cm above the ground under both direct and diffuse light conditions; (3) pin-drop point frame technique. Where values have been averaged (such as for the NDVI and SunScan measurements), the standard deviation is given. Raw data are available upon request for the Unispec data; the raw SunScan data is available under the file "PF_SunScan_LAI".
Normalized Difference Vegetation Index (NDVI) derived from 2019 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates two vegetation indices—Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) from the National Agriculture Imagery Program (NAIP) remotely sensed imagery. The intent is to make remotely sensed variables and visualizations accessible to stakeholders and researchers studying the Phoenix metropolitan area. NDVI and SAVI are calculated from the 2019 NAIP imagery (1m resolution). This dataset extends the 2010, 2013, 2015, and 2017 NDVI and SAVI products derived from NAIP imagery (also 1m resolution). All images are cropped to the CAP study area boundary.
CBP01 Variable distance line-transect sampling of bird population numbers in different habitats on Konza Prairie
Records of bird species based on line transect sampling, giving perpendicular distance of sighting from the transect line on 16 separate transects. Bird surveys were conducted 2-4 times per year in January, April, June, and October for a 29-year period from 1981 to 2009. Transects were designed to determine bird communities and population numbers associated with tallgrass prairie habitats with different experimental treatments (fire frequency, grazed by bison vs. ungrazed), riparian habitats on forest edge, and gallery forests dominated by oak woodland.
CGP01 Gall-insect densities on selected plant species in watersheds with different fire frequencies
Long-term monitoring of gall-insect densities on Solidago canadensis, Vernonia baldwinii, and Ceanothus herbaceous. Gall abundances are censused in watersheds burned at one- to twenty- year intervals to asses the role of fire frequency and time since fire on gall-insect population dynamics. The data sets contain the following: Watershed fire frequency, number of growing seasons since last fire, plant species, number of galled stems, and number of censused stems. Censuses conducted for the 1989-1996 growing seasons except 1992 and 1994, next scheduled census is fall 1997.
NSW01 Soil water chemistry from porous cup lysimeters on watersheds with different fire treatment
Soil water nitrogen composition is measured using porous cup lysimeters. Measurements include nitrate, ammonia, phosphate, and organic nitrogen and phosphorus. Variables of interest are rainfall patterns, vegetation types, and time since burning.
Neural responses to naturalistic clips of behaving animals in two different task contexts
Open the record for dataset details and reuse information.
The physiological effects of non-invasive brain stimulation fundamentally differ across the human cortex
Open the record for dataset details and reuse information.
Explicit FE simulation results for orthopedic screw-bone interaction, for different screw geometries and bone quality
<p>The dataset disclosed herein was employed to train artificial neural network surrogate models, specifically for tasks related to screw optimization. Please read "_readMe.txt" before using.</p>
Long-term MODIS LST day-time and night-time temperatures, sd and differences at 1 km based on the 2000–2020 time series
<p>Layers include: Land Surface Temperature daytime monthly median value 2000–2017, Land Surface Temperature daytime monthly sd value 2000–2017, Land Surface Temperature daytime monthly day-night difference 2000–2017. Derived using the <a href="https://gitlab.com/openlandmap/global-layers/-/tree/master/input_layers/MOD11A2">data.table package and quantile function in R</a>. We derived four standard statistics: (1) lower 2.5% probability (l.025), median (m), upper 97.5% probability (u.975) and standard deviation (sd). Updated long-term values for 2000–2022+ are pending.</p> <p>Includes also long-term trends (trend.logit.ols) which was produced by fitting regression models to de-seasonalized time-series as explained in this <strong><a href="https://gitlab.com/openlandmap/global-layers/-/blob/master/input_layers/MOD13Q1/03-data-access.ipynb">python tutorial</a></strong>. Basically models are fitted for <strong>each pixel</strong> and the model parameters are saved as images.</p> <p>For more info about the MODIS LST product see: <a href="https://lpdaac.usgs.gov/products/mod11a2v006/"><strong>https://lpdaac.usgs.gov/products/mod11a2v006/</strong></a>. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency, 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>clm = theme: climate,</li> <li>lst = variable: land surface temperature,</li> <li>mod11a2.oct.day = determination method: MOD11A2 product, day time values for October,</li> <li>d = median value / sd = standard deviation / u.975 = aggregation/statistics method: 97.5% probability upper quantile,</li> <li>1km = spatial resolution / block support: 1 km,</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>
Forest expansion for different warming scenarios simulated for 2010 to 3000 CE with LAVESI for Siberia
<p>Simulations with the spatially explicit and individual-based Siberian forest model LAVESI (Kruse et al., 2016, 2018, 2019) were set-up for transect in four focus regions covering the East Siberian treeline and tundra area (details in Kruse & Herzschuh, submitted). The model was updated to include climate forcing data for 300-800 km long and 20 m wide transects necessary for simulating the forest development between the northern taiga forests and the coast of the Arctic Ocean. Forced with climate forecasts driven by relative concentration pathway (RCP) scenarios 2.6, 4.5 and 8.5 and one with half the warming of RCP 2.6 named 2.6*. These were extended until 3000 AD either following the cooling of the scenarios after peak-warming, or with an arbitrary cooling back to levels of the 20th century.</p> <p>During the simulations, three key variables were extracted in 10-year steps for 2000-3000 AD: single-tree line, treeline, and, forest line, which are defined as the northernmost position of stands with >1 stem (tree > 1.3 m tall) per ha, the northernmost position of a forest cover not falling below 1 stem per ha, and, the northernmost position of a forest cover not falling below 100 stems ha per ha (see for a graphical representation Fig. 2 in Kruse et al., 2019). The determined treeline at year 2000 was used as baseline expansion and subtracted from each following years’ values.</p> <p>Furthermore, the tundra area was estimated for each of the four regions as the area between the treeline and the Arctic Ocean, based on interpolating the treeline position at the four transects over the complete modern treeline (Walker et al., 2005).</p> <ol> <li>Content of Table 1 "Kruse_and_Herzschuh_2022_Forest_expansion_in_Siberia_2010_to_3000_CE.csv": <ul> <li>Column 1: Scenario: RCP scenario used</li> <li>Column 2: Region: One of the four regions, from east-to-west Taimyr Peninsula, Buor Khaya Peninsula, Kolyma River Basin, Chukotka</li> <li>Column 3: Year: Year in CE of the simulation in 10 year steps</li> <li>Column 4: Forest line in m</li> <li>Column 5: Treeline in m</li> <li>Column 6: Single-tree line in m</li> </ul> </li> <li>Content of Table 2 "Kruse_and_Herzschuh_2022_Tundra_area_in_Siberia_2010_to_3000_CE.csv": <ul> <li>Column 1: Scenario: RCP scenario used</li> <li>Column 2: Year: Year in CE of the simulation in 10 year steps</li> <li>Column 3: Tundra area at region Taimyr Peninsula in km²</li> <li>Column 4: Tundra area at region Buor Khaya Peninsula in km²</li> <li>Column 5: Tundra area at region Kolyma River Basin in km²</li> <li>Column 6: Tundra area at region Chukotka in km²</li> </ul> </li> <li>The zip-file "Kruse_and_Herzschuh_2022_Forest_expansion_maps_in_Siberia_2010_to_3000_CE.zip" contains shape files with the tundra area in 10 year steps starting in 2000 until 3000 CE <ul> <li>projection: Albers azimuthal equidistant projection centered at Longitude of 100 °E (PROJ4 string: "+proj=aea +lat_1=50 +lat_2=70 +lat_0=56 +lon_0=100 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs")</li> </ul> </li> </ol> <p>This study was supported by the Initiative and Networking Fund of the Helmholtz Association and by the ERC consolidator grant Glacial Legacy of Ulrike Herzschuh (grant no. 772852).</p>
Cross cultural tears: A systematic investigation of the interpersonal effects of emotional crying across different cultural backgrounds
<p>The present project wants to examine the importance of emotional crying as an attachment behaviour and its fundamental role across a number of diverse cultures.</p> <p>Emotional tears are uniquely human and have fascinated scholars across several decades (Vingerhoets, 2013). Some researchers argue that tearful crying played a significant role in the evolution of humankind with regard to social development and solidarity (Walter, 2006). Recent years have seen an increased interest in exploring the interpersonal effects of human tears (see Gračanin, Bylsma, & Vingerhoets, 2018 for a review), with findings that emotional tears foster approach or support behavior (Gračanin, Krahmer, Rinck, & Vingerhoets, 2018) and crying individuals being evaluated as more communal (e.g., Zickfeld, van de Ven, Schubert, & Vingerhoets, 2018). These findings generally fit the hypothesis that emotional tears constitute a social act, promote social bonding and fulfill an attachment function (Nelson, 2005; Bowlby, 1982; Gračanin, Bylsma, et al., 2018; Murube, Murube, & Murube, 1999; Radcliffe-Brown, 1922; Vingerhoets, 2013). The present projects aims to answer the question whether emotional tears present a fundamental form of solidarity and bonding and whether the findings on increased attributions of warmth and higher approach intentions for tearful individuals replicate across a number of diverse contexts.</p> <p><a href="https://osf.io/fj9bd">Published in OSF: https://osf.io/fj9bd</a>.</p> <p>The OPen SCience FRamework also includes:</p> <ul> <li>Data from the pilot study: https://osf.io/txcw3/</li> <li>General information about the translation process (including the Portuguese version https://osf.io/t4cas/),</li> <li>Data management and research protocol (https://osf.io/5bh7m/),</li> <li>Approvals from ethical committees (https://osf.io/v8rqh/),</li> <li>Data and descriptive document of supplementary analyses (https://osf.io/s8ack/),</li> <li>Data and syntax (https://osf.io/x2pks/),</li> <li>Information about stimuli (https://osf.io/x2pks/) </li> </ul>
Dataset of The distinct influence of different maternal mental health symptom profiles on infant sleep during the first year postpartum: a cross-sectional survey
<p>The distinct influence of different, but comorbid, maternal mental health difficulties, such as postpartum depression, anxiety, or childbirth-related posttraumatic stress disorder (CB-PTSD) on infant sleep is unknown, although maternal mental health was reported to be associated with infant sleep. This paper first aimed to associations between maternal mental health symptoms and infant sleep. Second, it aimed to exploratory obtain maternal mental health symptom profiles from maternal mental health symptoms. Finally, it aimed to investigate the distinct influence of these maternal mental health symptom profiles on infant sleep, when including mediators (i.e., maternal perception of infant temperament and method to fall asleep) and moderators (maternal educational level and infant age).</p> <p>This dataset contains data on the mental health (i.e., CB-PTSD, depression, anxiety) of 410 mothers with an infant aged between 3 to 12 months old. Information on infant sleep and temperament (negative emotionality) was collected via standardised maternal-report questionnaires (City BiTS, EPDS, HADS, BISQ, and IBQ-R very short form). Sociodemographic data such as maternal age, marital status, educational level, infant age, and week of gestation are reported.</p> <p>This dataset is related to: Sandoz, V.; Lacroix, A.; Stuijfzand, S.; Bickle Graz, M.; Horsch, A. Maternal Mental Health Symptom Profiles and Infant Sleep: A Cross-Sectional Survey. <em>Diagnostics</em> <strong>2022</strong>, <em>12</em>, 1625. https://doi.org/10.3390/diagnostics12071625. </p>
30 m Normalized Difference Vegetation Index Maps of Pure Pixels over China for Estimation of Fractional Vegetation Cover (2014, 2018, 2022)
<p>Using multi-angle remote sensing data, we generated 30-m maps for the normalized difference vegetation index (NDVI) of fully-covered vegetation (<em>Vv</em>) and bare soils (<em>Vs</em>) across China in 2014, 2018 and 2022. These pixel-wise <em>Vv</em> and <em>Vs</em> maps can be integrated with the vegetation index (VI)-based model to facilitate the accurate and rapid estimation of fractional vegetation cover (FVC) across various spatial resolutions and large scales. The products were produced using a multi-angle algorithm (MultiVI), which effectively addressed the spatial variability inherent in <em>Vv</em> and <em>Vs</em> and enhanced the accuracy of FVC estimations in comparison to traditional statistical methods. The estimated FVC demonstrated a root mean square deviation (RMSD) of approximately 0.1 when evaluated against field-measured FVC across different experimental sites.</p>
Endurance swimming performance and physiology of juvenile sturgeon at different temperatures, 2022-2024
Endurance swimming trials were conducted on both Green Sturgeon (Acipenser medirostris) and White Sturgeon (Acipenser transmontanus) across multiple size classes to assess the effects of temperature and velocity on swimming performance. Fish were exposed to various water temperatures for at least 14 days and swam at fixed-water velocities (cm/s) in controlled swim tunnels. Data were collected on parameters such as species, size class, trial temperature, velocity, recovery time, time-to-fatigue, swim type classifications, and whether the trials were completed. Additional metadata included fish morphometrics such as fork length, total length, weight, and trial dates, enabling comparisons across species, size, and treatment conditions. Physiological responses were measured post-swimming to evaluate the impacts of endurance trials on the smallest size class of Green Sturgeon (5cm fork length): - In 2022, whole-body cortisol, glucose, and lactate concentrations were measured immediately following endurance trials (0 min recovery). - In 2023, recovery dynamics were incorporated, with physiological responses assessed at multiple time points (0 min, 15 min, 30 min, and 60 min post-trial). - In 2022 and 2023 the baseline physiological metrics (whole-body cortisol, glucose, and lactate concentrations) of control fish (not subjected to swimming trials) were measured across all temperatures, years, species, and size classes. This dataset provides information on sturgeon swimming performance under varied temperature conditions, as well as the associated physiological stress responses, offering valuable insights into their endurance capabilities and recovery processes. The findings can inform conservation strategies, habitat management, and aquaculture practices for these ecologically and economically important species.
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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.