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Canopy Phenology, Remote Sensing and Microclimate at Harvard Forest 2006-2011
Our research at the Harvard Forest walk-up tower site examines how seasonality of canopy leaf area, or canopy phenology, influences, and is influenced by, local climate. As part of this activity we are studying methods for (and limits to) remote sensing of canopy phenology. To address this research topic, we have initiated measurements to quantify how radiation fluxes through a deciduous forest canopy are modified by seasonal canopy leaf dynamics. We continuously measure above- and below-canopy radiation fluxes at a variety of spectral bands (shortwave, photosynthetic) and with digital photography. These measurements provide a surrogate measures of canopy leaf area dynamics, and directly represent the radiation component of the surface energy balance. These measurements complement ongoing microclimate and eddy covariance measurements of water and carbon exchange at the EMS flux tower.
Species-level map of Smith Island, VA from remote sensing 2003
Species-level vegetation map for Smith Island off the tip of the Delmarva Peninsula in Virginia. Created by Charles M. Bachmann of the Naval Research Laboratory based classification of hyperspectral imagery using 3-season data (two PROBE2 scenes and a Hymap scene).
Satellite-based remote sensing of water clarity in the shallow coastal lagoons of Virginia 2013-2021
This dataset contains raw data, analysis products and code for a study of satellite-based estimation of water clarity. The files are: Match-up.csv: In situ Secchi depths collected by the Virginia Coast Reserve Long Term Ecological Research project (VCR LTER), matched with satellite (Landsat-8/Sentinel-2) Secchi depth estimates from 2013-2022 from NASA SeaDAS 8.2. Satellite overpasses occurred +/- 0-1 days within in situ sampling. Valid remote sensing reflectance values (Rrs) from NASA SeaDAS (not masked by quality flags) were recovered at 12 of 17 in situ sampling sites: 6 ocean inlet sites, 2 lagoon site, and 3 mainland tidal creek sites. Therefore, there are 12 in situ sites available for comparison with satellite estimates. compare_L8S2.csv: Satellite data and water clarity estimates from 150 randomly sampled sites across 5 clear day images in the Virginia Coast Reserve, 2021. Satellite data are from Landsat-8 and Sentinel-2 and processed/atmospherically-corrected using NASA SeaDAS 8.2. The Virginia Coast Reserve is a coastal lagoon system located in Virginia, USA, near the southern tip of the Delmarva Peninsula. Due to low nitrogen inputs and frequent exchange with the Atlantic Ocean via inlets between barrier islands, water quality is high relative to many other coastal bays in the United States and worldwide. Spatial_averaging_analysis.csv: Secchi depths at in situ water quality sites at 10 m resolution (Sentinel-2 only), 30 m resolution (Landsat-8 and Sentinel-2), and 90 m resolution (Landsat-8 and Sentinel-2) where there are in situ match-ups. atmocorrect.csv: In situ Secchi depths collected by the Virginia Coast Reserve Long Term Ecological Research project (VCR LTER), matched with satellite (Landsat-8/Sentinel-2) Secchi depth estimates from 2013-2022 from NASA SeaDAS 8.2 and ACOLITE Version 2022022.00. L8_ALL.csv: All Landsat-8 Secchi depth data available between 2013-2021 at in situ water quality sites. S2_ALL.csv: All Sentinel-2 Secchi depth data availab
Wearable data and self reported fatigue scores from a remote observational study in Sjogren's disease, SLE and healthy participants
<p>Fatigue is a subjective, complex, and multi-faceted phenomenon, commonly experienced as tiredness. However, pathological fatigue is a major debilitating symptom associated with overwhelming feelings of physical and mental exhaustion. To date, there is no consensus about reliable quantitative assessments of fatigue.</p> <p>We collected observational data for a period of one month from 296 participants (healthy volunteers, Sjogren’s Syndrome, and Systemic Lupus Erythematosus patients) in the United States. Data comprised continuous multimodal digital data from Fitbit, including heart rate, physical activity, and sleep daily features, and app-based daily and weekly questions (e.g., pain, mood, general physical activity, and fatigue). When matching both sensor data and PROs, and excluding missing data, the dataset contains data from 183 subjects and 3950 recording days.</p> <p>The analysis of the association of digital data to self-reported fatigue was published at <em><strong>Rao C., et. al. (2023), Association of digital measures and self-reported fatigue: a remote observational study in healthy participants and participants with chronic inflammatory rheumatic disease, Frontiers in Digital Health</strong></em>.</p> <p>Demographics, digital parameters, and other information on this dataset can be found in the aforementioned manuscript and related supplementary material. Details on the data files can be found under README.txt.</p>
Fish tag data remotely detected using whole stream antennas or hand held tag readers in the Kuparuk, Itkilik, and Sagavanirktok drainages near Toolik Field Station, Alaska, from 2010 to 2017
From 2009 to 2017, the FISHSCAPE Project (grant numbers 1719267, 1417754, and 0902153), based at Toolik Field Station, has monitored physical, chemical, and biological parameters within three watersheds: The Kuparuk (including Toolik Lake and Toolik outlet stream); The Sagavanirktok (primarily Oksrukuyik Creek, but also including sections of the Ailish and Atigun Rivers and the Galbraith Lakes); and The Itkillik (primarily the I-Minus outlet stream, a tributary that that feeds into the Itkilik River). Target species were primarily Arctic grayling and Lake trout, although Arctic char, Burbot, Dolly varden, round whitefish, and slimey sculpin were also captured. This file contains the detectioned fish tags using whole stream or hand-held antennas in the three watersheds. We had no field season in 2014 and thus did not deploy antennaes. Fish were tagged with Passive Integrated Transponder (PIT) tags which can be read with a whole stream antenna to track the migration of the fish, predominately Arctic grayling, throughout the systems. Fish tags detected with a handheld readers are designated in Site ID as "XXX_capture". For "capture" fish time is arbitraily set at '7:00:00'' of the day of capture and tagging because actual time was not recorded. The individual fish data (date, tag number, length, weight, species) associated with the tag can be found in the 2009-2017_FISHSCAPE_fish_tagging file.
Seasonal and annual summary statistics of urbanization, vegetation, land surface temperature, and bioclimatic variables derived from remotely-sensed imagery in areas surrounding long-term bird monitoring locations in the greater Phoenix, Arizona, USA metropolitan area (1997-2023)
This data package consists of 26 years (1998-2023) of environmental data and 22 years (2000-2022) years of bioclimatic data associated with CAP-LTER long-term point-count bird censusing sites (https://doi.org/10.6073/pasta/4777d7f0a899f506d6d4f9b5d535ba09), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). The environmental variables include land surface temperature (LST), three spectral indices of vegetation and water – the normalized difference vegetation index (NDVI), the soil adjusted vegetation index (SAVI), and modified normalized difference water index (MNDWI) – and four spectral indices of impervious surface/urbanization. Impervious surface indices include the normalized difference built-up index (NDBI), the normalized difference impervious surface index (NDISI), the enhanced normalized differences impervious surface index (ENDISI), and the normalized impervious surface index (NISI). LST and all spectral indices were derived from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. The seven bioclimatic variables (e.g., air temperature, precipitation) were sourced from 1-km resolution gridded estimates of daily climatic data from NASA Daymet V4. We created temporally-aggregated Daymet raster images by calculating mean pixel-values for each season and year, as well as seasonally and annually summed precipitation. We summarized the values of each environmental variable by generating variously-sized (100-m, 500-m, 1000-m) buffers around each bird point count location and extracting weighted mean values of each environmental variable, with each pixel's values weighted by the proportion of its area falling within the buffer. 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 s
RIV06 Remote sensing in and around riparian zones at Konza Prairie
The goal of this project was the measure changes in woody vegetation cover over time, in riparian and non-riparian locations. The study was retrospective, using high resolution aerial imagery to identify areas dominated by grasslands, shrubs, trees, and woody plant that could not be differentiated as shrubs or trees (referred to as “unk” or “unknown”). These data help us understand rates of woody plant cover over time and how these changes might affect other populations (e.g., avifauna) and processes (e.g. hydrology). These data show and increase in woody plant cover across all three watersheds up until 2010, but with less woody plants expansion in the non-riparian zone of watershed and N1B. Through 2020, woody plant expansion continued in watersheds N1B and N4D. In N2B, tree cover decreased sharply in 2011 and remained low through 2020. This was expected due to the tree removal treatment. However, shrub cover increased rapidly over this same time frame, resulting in little net change in total woody cover (tree plus shrub cover). These results suggest that even an extreme intervention of repeated tree removal is not enough to return the riparian zone to a grassland state.
MCR LTER: Coral Reef: Quantifying 2019 coral bleaching; data for Kopecky et al., 2023 Remote Sensing
This data package contains a dataset generated using image AI-assisted image segmentation of live and dead corals within ortho-photomosaics of benthic reef habitat on the North shore fore reef of Moorea, French Polynesia. The orthophotomosaics were produced through a rigorous method of underwater photogrammetry that allowed for spatial and temporal co-registration of ortho-photomosaics of the same location over time (for full photogrammetric methods, see Nocerino et al. 2020: https://doi.org/10.3390/rs12183036). Using the image segmentation software, TagLab (see Pavoni et al. 2021: https://doi.org/10.1002/rob.22049), we quantified live and dead coral before and after a bleaching event to estimate the amount of coral loss as a result of this event. This data package also contains code necessary to conduct the analyses of the dataset described above and create data visualizations used in the manuscript “Quantifying the Loss of Coral from a Bleaching Event Using Underwater Photogrammetry and AI-Assisted Image Segmentation”, published in the journal Remote Sensing in 2023, and as part of the dissertation of K. Kopecky. Analyses of these data and full methods descriptions can be found at https://doi.org/10.3390/rs15164077. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 22-24354 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2024). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
Enrichment index related to seamounts and islands in the South West Indian Ocean from chlorophyll-a satellite remote sensing data
<p>This data set is the result of the calculation of an original “enrichment index” (EI) from chlorophyll-a (chl-a) remote sensing data (MODIS-Aqua sensor) and initially dedicated to highlight localized chl-a enrichments associated to isolated seamounts and islands in the South West Indian Ocean, in order to estimate their contribution in increasing the local primary productivity. Details and results are described in the DSR-II paper entitled “Satellite observations of phytoplankton enrichments around seamounts in the South West Indian Ocean with a special focus on the Walters Shoal” from Demarcq et al. 2020.<br> 1. Initial data used<br> We used daily L3 data chl-a and sea surface temperature (SST) collected by the MODIS (Moderate-resolution Imaging Spectroradiometer) sensor on board the Aqua platform (downloaded from https://oceancolor.gsfc.nasa.gov/) from January 2003 to December 2018. This has a spatial resolution of 1/24° (ca. 4.5–5 km). The data covers the region (45°S – 10°S / 25°W – 80°W).<br> 2. The calculation method<br> The calculations were done at the pixel level. The EI is the difference (expressed in %) between the value of each ‘candidate pixel’ and its medium range surrounding, defined as the average value of all chl-a values around the candidate pixel between a fix range of distance between 30 and 90 km, the R1 and R2 terms of the equation enclosed.<br> 3. Data sets<br> The data set contains two files:<br> - the monthly climatology (12 frames) of the EI from January to December (2003 to 2018 average), in an internally compressed netCDF-4 format (NC-compliant or almost)<br> - the yearly average of the EI (period 01/2003 - 12/2018)<br> <br> Two images are joined with this data set:<br> - a "technical view" of the yearly average of the index for the full region sub-region (45°S – 10°S / 25°W – 80°W)<br> (file: indsw4_modis_p100_4km_16y_20030101_20181231.R2018.0.enrichment-index.dist-30-90km.png).</p> <p> - a slightly improved view of the yearly average of the index for the sub-region (40°S – 10°S / 30°W – 70°W).<br> (file: Figure-enrichment-index.pdf)<br> <br> An improved version of this index will be available in a near future.</p>
Satellite remote sensing dataset for urban climate in Bergen and Prague (TURBAN-D09)
<p><span>Shared dataset contains remote sensing data necessary for a land surface temperature (LST) calculation. Layers were processed for two cities; Bergen (Norway) and Prague (Czech Republic). Original data were downloaded from the U.S. Geological Survey (https://doi.org/10.5066/P975CC9B). For a LST calculation, a land surface emissivity (LSE) algorithm was used.</span></p> <h3><span>Processing of LANDSAT-8 and LANDSAT-9 data</span></h3> <ol> <li><span>Reading metadata file for each scene (*MTL.txt)</span></li> <li><span>Reprojection of scene (note: Bergen scenes have two UTM Zones; 31N and 32N)</span></li> <li><span>Cloud cover raster (see folder 01_CloudCover)</span></li> <li><span>Calculation Top-Of-Atmosphere (TOA) reflectance for bands 10 and 11 (TB_10 and TB_11), saving to folder 02_TOA-reflectance</span></li> <li><span>Calculating of NDVI and Fractional Vegetation Cover (FVC), saving to folder 03_FVC-NDVI</span></li> <li><span>Calculating of LSE for both bands, same as different and mean LSE (folder 04_LSE)</span></li> <li><span>Calculating of LST</span></li> <li><span>Saving of metadata file (see *metadata.txt)</span></li> </ol>
Raw data from the manuscript "Full-aperture extended-depth oblique plane microscopy through dynamic remote focusing"
<p>The repository contains all the raw data from the manuscript titled "Full-aperture extended-depth oblique plane microscopy through dynamic remote focusing" (https://doi.org/10.1117/1.JBO.29.3.036502).<br> The data consists in 3D stacks acquired with the method described in the manuscript. Since raw images are acquired along a diagonal plane, and are stretched in one direction, the dataset also includes a Python script to perform an affine transform projecting the stack on cartesian coordinates.</p> <p>Samples imaged include sub-resolution microbeads in agarose gel, a fixed slice of mouse kidney (fluocells prepared slide #3, invitrogen), and 3 to 5 days post fertilization Tg(kdrl:eGFP)s843 Zebrafish.</p>
Remote Sensing Drought Monitoring Dataset based Temperature Vegetation Precipitation Dryness Index (TVPDI) from 2001 to 2020 in China
<p>In this dataset, the MODIS vegetation index and land surface temperature products are processed into NDVI and LST monthly time series with a spatial resolution of 1 km, and the final precipitation data of GPM IMERG are downscaled, unified at a spatial resolution of 1 km. And after a standardization process, using the spatial distance model, a remote sensing drought monitoring dataset in China from 2001 to 2020 was produced based on the Temperature Vegetation Precipitation Dryness Index. For the specific construction process of this data, please refer to https://linkinghub.elsevier.com/retrieve/pii/S0034425720303278</p>
Remote Rapid Visual Screening (RRVS) Buildings Survey Data - DESTRESS - France
<p>The dataset contains a set of structural and non-structural attributes collected using the GFZ RRVS methodology in Alsace, France, within the framework of the DESTRESS project. The survey has been carried out between May and June 2017 using a Remote Rapid Visual Screening system developed by GFZ and employing omnidirectional images from Google StreetView (vintage: February 2011) and footprints from OpenStreetMap.<br> Surveyor: Konstantinos G. Megalooikonomou (GFZ-Potsdam)<br> The attributes are encoded according to the GEM taxonomy v2.0 (see https://taxonomy.openquake.org). <br> The following attributes are defined (not all are observable in the RRVS survey):<br />code,description<br> lon, longitude in fraction of degrees<br> lat, latitude in fraction of degrees<br> object_id, unique id of the building surveyed <br> MAT_TYPE,Material Type<br> MAT_TECH,Material Technology<br> MAT_PROP,Material Property<br> LLRS,Type of Lateral Load-Resisting System<br> LLRS_DUCT,System Ductility<br> HEIGHT,Height<br> YR_BUILT,Date of Construction or Retrofit<br> OCCUPY,Building Occupancy Class - General<br> OCCUPY_DT,Building Occupancy Class - Detail<br> POSITION,Building Position within a Block<br> PLAN_SHAPE,Shape of the Building Plan<br> STR_IRREG,Regular or Irregular<br> STR_IRREG_DT,Plan Irregularity or Vertical Irregularity<br> STR_IRREG_TYPE,Type of Irregularity<br> NONSTRCEXW,Exterior walls<br> ROOF_SHAPE,Roof Shape<br> ROOFCOVMAT,Roof Covering<br> ROOFSYSMAT,Roof System Material<br> ROOFSYSTYP,Roof System Type<br> ROOF_CONN,Roof Connections<br> FLOOR_MAT,Floor Material<br> FLOOR_TYPE,Floor System Type<br> FLOOR_CONN,Floor Connections</p>
The Role of Informal Communication in Building Shared Understanding of Non-Functional Requirements in Remote Continuous Software Engineering
<p><strong>Study Information</strong></p> <p>We conducted an ethnography-informed case study of a remote software organization that adopts CSE practices to explore how the organization builds a shared understanding of NFRs. Our study uses semi-structured interviews with a period of observations to answer the following research questions:</p> <p> </p> <ol> <li> <p>How does a remote software organization that adopts CSE practices reach a shared understanding of NFRs?</p> </li> <li> <p>What are the limitations to the shared understanding of NFRs in a remote software organization that adopts CSE practices?</p> </li> <li> <p>What organizational practices for remote collaboration supported a shared understanding of NFRs?</p> </li> </ol> <p> </p> <p>In our study, we refer to our partner organization as Alpha. We used ethnography-informed methods to study Alpha's practices and processes and how they approach a shared understanding of NFRs in their product development. </p> <p> </p> <p><strong>Data Analysis</strong></p> <p>We performed a qualitative study through semi-structured interviews and observations. We use the open, axial and selective coding approach from grounded theory [1] to create our codebook, which informed the results and discussion of our study. Two independent coders held agreement sessions to discuss the codes, consolidate the codes and calculate the inter-rater reliability using the Cohen Kappa's coefficient for measuring observer agreement for categorical data [2]. </p> <p> </p> <p><strong>Artifact Descriptions</strong></p> <p>Our replication package contains three artifacts:</p> <p>1. Codebook.csv: The codebook contains rows for the list of codes used, including the code name and the description of the codes. The codes are the final set of themes derived during the thematic analysis of the interview responses. For example, 'Gaps in communication' means when interview participants describe miscommunications due to team members making assumptions about a project/process or having unclear expectations for a project.</p> <p>2. kappa-scores.csv: This contains the associated kappa values for each round of inter-rater agreement sessions. For each agreement session, the Cohen Kappa's coefficient was calculated from the number of agreements and disagreements of codes within one or two interview transcripts. The Kappa values represent the level of agreement ranging from 0 to 1, where > 0.6 represents substantial agreement. </p> <p>3. Interview-questions.csv: This contains the interview questions used in the semi-structured interviews. Some of the interview questions varied depending on the interviewee’s role, experience and the flow of the interviews.</p> <p><strong> </strong></p> <p><strong>Usefulness</strong></p> <p>We recognize that the value and usefulness of our replication package are yet-to-be-determined. In the interest of transparency of open science, we published our artifacts. We hope that these artifacts are useful to either replicate our findings or to further analyze them to produce other enlightening results.</p> <p><strong> </strong></p> <p><strong>References</strong></p> <p>1. Rashina Hoda, James Noble, and Stuart Marshall. "Grounded theory for geeks". In: Proceedings of the 18th conference on pattern languages of programs. 2011, pp. 1–17.</p> <p>2. J Richard Landis and Gary G Koch. "The measurement of observer agreement for categorical data". In: biometrics (1977), pp. 159–174.</p> <p><strong> </strong></p> <p> </p>
Remote Sensing Drought Monitoring Dataset based Temperature Vegetation Precipitation Dryness Index (TVPDI) from 2001 to 2021 in China (v2.0)
<p>The Enhanced Vegetation Index (EVI), Land Surface Temperature (LST) and Precipitation (P) were used as new data sources based on the spatial distance model to construct an optimized multi-source remote sensing dryness index named Temperature-Vegetation-Precipitation Dryness Index based on the shortcomings of the TVPDIorigin (i.e., TVPDI<sub>o</sub>) data source. The TVPDI<sub>n</sub> of the long time series was also compared and analyzed with the classical drought index - Standardized Precipitation Evapotranspiration Index (SPEI-3) on a 3-month scale, different drought response level products of Solar-Induced Chlorophyll Fluorescence (SIF), soil moisture (SM) from ESA CCI (European Space Agency's Climate Change Initiative), and total crop yield, then the sensitivity and validity of the TVPDI<sub>n</sub> for wetness and dryness monitoring were synthesized and validated. On this basis, here are the results of the verification:</p> <p>(1) Compared with the original data source TVPDI<sub>o</sub> using the new multi-source remote sensing data source of precipitation and vegetation index to construct TVPDI<sub>n</sub>, the overall correlation between the two and SPEI-3 was good, with a maximum of 0.57 and 0.56, respectively (p< 0.1), but the overall TVPDI<sub>n</sub> constructed in this study had a better fit compared to the original data source TVPDI<sub>o</sub> and was more sensitive to the monitoring of dry and wet conditions.</p> <p>(2) According to the comparison of TVPDI<sub>n</sub> with ESA CCI sm, TVPDI<sub>n</sub> showed a high correlation of more than 0.9 with soil water content, which proved that TVPDI<sub>n</sub> was highly consistent with soil moisture; compared with SIF, 54.5% of the regional correlation coefficients were greater than 0.8 (p< 0.01), and spatially, the correlation results were better in the northwest than in the east, indicating that the response of TVPDI<sub>n</sub> to vegetation productivity is more agile in regions with continental climate such as the northwest. The results of correlation with grain yield comparison showed that good positive correlations were presented with TVPDI<sub>n</sub> in Liaodong Peninsula, northern North China Plain, and most of Qilian Mountains, southern edge of Qinling Mountains, middle and lower reaches of Yangtze River, and South China, indicating that TVPDI<sub>n</sub> has a high consistency in the changes of agricultural grain production in the above mentioned regions, and also proving the index in monitoring agricultural aridity and guiding agricultural production The good performance of the index in monitoring agricultural aridity and guiding agricultural production.</p> <p> This dataset is version 2.0, and covers all of China's territory, but the temperature-vegetation- precipitation dryness index of the open water surface are often set to a null value. Note:The data format is "TIF", the spatial resolution is "1 km", the time resolution is "1 month" and dimensionless. The pixel value is the NTVPDI value, and the closer the pixel value is to 0, the drier it is, and the larger the data, the wetter the land surface. The practical utility of this dataset is to compare the degree of dryness and wetness of China's land, to monitor short-term and medium-term droughts, and to substitute model parameters related to soil moisture. This is of great value to the impartial formulation of China's environmental and economic policies, regular monitoring and evaluation of drought and flood conditions. This product will be freely available to all users worldwide and will be continuously improved to suit new goals and needs.</p>
LAGOS-US LANDSAT: Data module of remotely-sensed water quality estimates for U.S. lakes over 4 ha from 1984 to 2020
This data package, LAGOS-US LANDSAT, is one of the extension data modules of the LAGOS-US platform that provides six water quality estimates (chlorophyll, Secchi depth, dissolved organic carbon, total suspended solids, turbidity, and true water color) from remote sensing for lakes ≥ 4 ha in the conterminous U.S. (48 states plus the District of Columbia) for the years 1984-2020. These estimates are generated through machine learning models on in-lake water quality matchups from LAGOS-US LIMNO with Landsat 5, 7, and 8 whole lake median reflectance values and pixel-wise band ratios that are subsequently used to make predictions across the U.S. The LANDSAT module contains remotely sensed reflectance values for 136,977 of the 137,465 lakes ≥ 4 ha from the LAGOS-US research platform. Within the module are a total of 45,867,023 sets of reflectance values, a matchup dataset with a window of up to 7 calendar days with in situ data, and associated water quality parameter predictions for each reflectance set. Additional quality control flags are provided for predictions indicating whether reflectance extractions included negative values, the percent of the maximum pixels ever retrieved for that lake that the predictions are based on, and whether there are shared calendar day predictions due to scene overlap.
Numerical summaries of vegetation indices and land surface temperature derived from remotely sensed imagery in Phoenix Area Social Survey (PASS) neighborhoods of central Arizona
This project calculates two vegetation indices: Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI), and land surface temperature (LST) from remotely sensed imagery. NDVI and SAVI are calculated from the 2010, 2013, 2015, and 2017 NAIP imagery (1m resolution). LST is calculated from Landsat 5 and 8 imagery (30m resolution) from summer months in 1985, 1990, 1995, 2000, 2005, 2010, and 2015. Summary values are calculated for each of the aforementioned data resources for 2011 and 2017 Phoenix Area Social Survey (PASS) study area boundaries. Tabular summaries of the mean, median, minimum, maximum, and standard deviation of the NDVI, SAVI, and LST values for the 2011 and 2017 Phoenix Area Social Survey boundaries (45 and 12 neighborhoods, respectively) are provided. Javascript code used to process NDVI, SAVI, and LST imagery, and R code used to calculate numerical summaries of NDVI, SAVI, and LST in PASS neighborhoods are included with this dataset. Locations and areas of PASS study neighborhood boundaries and source imagery used to calculate these summaries are available through the Environmental Data Initiative - see resouce listing in the methods of this data set.
Predicting aboveground and belowground processes in diverse forest ecosystems using remote sensing and in-situ measurements
The Forest and Biodiversity (FAB2) experiment uses native tree species in varying levels of species richness, phylogenetic diversity, and functional diversity planted in 100 m2 and 400 m2 plots at 1 m spacing, appropriate for testing long-term ecosystem consequences. FAB2 was designed and established in conjunction with a prior experiment (FAB1) in which the same set of twelve species was planted in 16 m2 plots at 0.5 m spacing. This data package examines the connections between aboveground and belowground processes in FAB2. This data package includes information on tree diversity and community composition, forest structure, forest understories, soil microbes, net nitrogen mineralization, and canopy nitrogen. A wide variety of data types are included, such as data from hyperspectral and LiDAR remote sensing, percent cover analysis, soil microbial analyses, and soil assays including C:N, pH, and net nitrogen mineralization. This data package is included in the submission of the manuscript entitled “Predicting aboveground and belowground processes in diverse forest ecosystems using remote sensing and in-situ measurements.”
Synthetic magnetic nanoparticles for remote-controlled stemcell therapies of neurodegenerative disorders
<p>In the context of the MAGNEURON european project, we developed different types of magnetic nanoparticles that can act as nanoactuators to manipulate intracellular proteins involved in signaling pathways.</p> <p>Four types of particles are presented here. First, size-sorted maghemite cores of different diameter (8 to 20 nm) were synthesized. Then these cores were used to make Fe2O3@SiO2 core-shell nanoparticles that are colloidally stable and easy to functionalize, and poly(acrylic acid) coated nanoparticles. Both types of particles can be rendered fluorescent by the addition of a fluorophore. Finally, we also developed a way to synthesize micro-needles made of aligned maghemite cores encapsulated in a silica layer.</p> <p>In this dataset are presented some electron microscopy images of the optimized particles and their characterizations in terms of sizes and magnetic properties. These particles have then been used by the other members of the Magneuron consortium in order to manipulate different intracellular signalling pathways.</p>
Remote sensing based species distribution modelling based on GLCM and vegetation fractions for the city of Leipzig
<p>Modelling dataset and fractional vegetation cover dataset used in the study "Earth observation based indication for avian species distribution models using the spectral trait concept and machine learning in an urban setting" Wellmann et al. 2020.</p> <p> </p> <p>Reference:</p> <p></p> <p>Wellmann, T., Lausch, A., Scheuer, S., & Haase, D. (2020). Earth observation based indication for avian species distribution models using the spectral trait concept and machine learning in an urban setting. <em>Ecological Indicators</em>, <em>111</em>(April 2020), 106029. https://doi.org/10.1016/j.ecolind.2019.106029</p> <p></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.