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2,721 results for “connectivity”
Database of measurements for damage detection of steel beam splice connection by Coaxial Correlation Method in 6-D space
<p>This database includes series of measurements of the structure's response taken in six-dimensional space using two 6D sensors, coaxially positioned on either side of the investigated splice connection between two steel beams. The data set consists of two parts. The first part of the data set is measurements for six different specimens with wave type impact – short sweep signal with duration 0.05 s. The second part is the measurements during splice connection degradation of one of the specimens with short impulse. The degradation of a connection is presented by four different states of joints. In the "<strong>Read_me_first.pdf</strong>" is described the experiment, the format of .csv files names and files' structure.</p><p>Used materials, methods and results for the second part of the data set is described in Buka-Vaivade, K.; Kurtenoks, V.; Serdjuks, D. Non-Destructive Damage Detection of Structural Joint by Coaxial Correlation Method in 6D Space. <i>Buildings</i> <strong>2023</strong>, <i>13</i>, 1151. https://doi.org/10.3390/buildings13051151</p>
Database of measurements for damage detection of steel beam splice connections by Coaxial Correlation Method in 6-D space
<p>This database includes series of measurements of the structure's response taken in six-dimensional space using two 6D sensors, coaxially positioned on either side of the investigated splice connection between two steel beams. The data set consists of measurements for six different specimens with two types of impact – sweep signal with duration 0.5 s and short impulse, during degradation of the splice connections realised by unbolting the bolts in the connections. In the "<strong>Read_me_first.pdf</strong>" is described the experiment, the format of .csv files names and files' structure.</p><p>This database is a continuation of the database Kurtenoks, V., Buka-Vaivade, K., Serdjuks, D., Lapkovskis, V., Mironovs, V., & Podkoritovs, A. (2023). Database of measurements for damage detection of steel beam splice connection by Coaxial Correlation Method in 6-D space (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10077332<br>Suggested by authors data post-processing is described in Buka-Vaivade, K.; Kurtenoks, V.; Serdjuks, D. Non-Destructive Damage Detection of Structural Joint by Coaxial Correlation Method in 6D Space. <i>Buildings</i> <strong>2023</strong>, <i>13</i>, 1151. https://doi.org/10.3390/buildings13051151</p>
Role of Personal Connections in Shaping Decisions About Private Forest Use in Central Massachusetts 2008
We begin with a simple premise: Social and ecological systems are interconnected in complex ways. Forests are, perhaps, one of the most intriguing examples of this interconnectedness--particularly those in private ownerships. Forested landscapes are essential in maintaining human systems through the provision of multiple ecosystem services that span public (e.g., clean water, nutrient cycling) and private (e.g., fiber, maple syrup, home sites) goods. However, the majority of forestland in the Eastern United States is a mosaic of small landholdings (less than 20 ha) where property management is largely uncoordinated. On such landscapes, decentralized, ownership-centric decision-making defines the mix of ecosystem services provided and the landscape patterns present now and in the future. While somewhat effective for less spatially sensitive ecosystem services (e.g., fiber production), this ownership-centric approach is ill suited to spatially sensitive ones (e.g., water quality) and may, in some cases, be detrimental to them (e.g., habitat fragmentation). Improving the ecological and landscape sensitivity of private forest conservation and management is a major challenge facing researchers, practitioners, and policymakers in sustaining forest ecosystems. Central to unraveling this challenge is a fundamental understanding of how landowners simultaneously fit within their social and bio-physical landscapes. Despite their importance to broader forest sustainability, our collectively understanding of forest landowners has been primarily concerned with individual landowners and/or individual properties. For example, most research surrounding private forest landowners centers on primarily agent-based theories of behavior or decision-making (e.g., rational actor, theory of planned behavior). This perspective is useful in predicting and effecting behavior at broad scales, but lacks the specificity needed to address local landscape concerns and/or opportunities. Other studies
The influence of heart rate variability biofeedback on cardiac regulation and functional brain connectivity
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Dataset for Accuracy of Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and CNN-LSTM Model
<p>This research paper introduces a deep learning hybrid model employing Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) for short-term photovoltaic (PV) solar energy forecasting.The proposed method integrates the Variational Mode Decomposition (VMD) algo-rithm with the CNN-LSTM model to predict PV power generation from a solar farm in Boussada, Algeria, from January 1, 2019, to December 31, 2020. The performance of the developed model is benchmarked against other deep learning models (VMD-CNN, VMD-LSTM, CNN-LSTM) across various time horizons (15, 30, and 60 minutes) to provide a comprehensive evaluation. Our findings exhibit greater performance of the developed model compared to other architectures, showcasing promising results in solar power forecasting. This research contributes to the main goal of enhancing EMS by providing accurate solar energy forecasts.</p>
Forestry roads in the Purapel fluvial catchment and related changes in sediment connectivity
<p>This dataset contains georeferenced data of forestry roads and sediment connectivity in the Purapel catchment, which drains the Chilean Coastal Range. The forestry road network consists of all the dirt and gravel roads mapped in QGIS by observing open satellite images and vectorial data available during January 2021. The observed data are maps that were listed in the QGIS OpenLayers plugin (<a href="https://github.com/sourcepole/qgis-openlayers-plugin">https://github.com/sourcepole/qgis-openlayers-plugin</a>), such as Google Satellite (Map data ©2015 Google) and OpenStreetMap <sup>1</sup>, the road network of the Chilean Congress National Library (<a href="https://www.bcn.cl/siit/mapas_vectoriales">https://www.bcn.cl/siit/mapas_vectoriales</a>) and compositions of Sentinel 2 images (European Space Agency, courtesy of the U.S. Geological Survey) of the post-2017 fire period.</p> <p>Sediment Connectivity maps were calculated on a 5 m resolution LiDAR DTM using the Connectivity Index<sup> 2</sup>. The maps were derived from the stand-alone, free and open-source executable SedInConnect 2.3<sup> 3</sup> using the Weighting factor of <sup>2</sup> and two different targets, which are available as tif files:</p> <ul> <li>ICs.tif contains <em>IC<sub>s</sub></em>, the Connectivity Index to the stream network.</li> <li>ICrs.tif contains <em>ICr<sub>s</sub></em>, the Connectivity Index to the road and the stream network.</li> </ul> <p>Here, the Road Connectivity, <em>RC </em>(dimensionless) is defined as the difference between both previous maps, with the aim to describe the change in sediment connectivity due to forestry road network:</p> <ul> <li><em>RC = IC<sub>rs</sub> - IC<sub>s</sub></em></li> </ul> <p>It is available as RC.tif file. The area of<em> high RC </em>was defined using the percentile 95 (3.12). File RC95.tif is a mask of <em>RC </em><em>≥</em><em> 3.12</em>.</p> <p>The contributing area <em>CA </em>(m<sup>2</sup>) was calculated using the multiple flow D-infinity approach <sup>4</sup> using TauDEM (https://hydrology.usu.edu/taudem/taudem5/downloads.html).</p> <p>The file CA_RC95.tif contains the contributing area (m<sup>2</sup>) of the surfaces with highest changes in sediment connectivity due to the road network. That is:</p> <ul> <li><em>CA_RC95 = </em>{<em>CA </em>|<em> RC </em><em>≥</em><em> 3.12</em>}</li> </ul> <p>The landscape distribution of those surfaces, in terms of proximity to the hilltops and valleys, is described by the density plot of the raster file CA_RC95.tif in R:</p> <pre><code>library("raster") library("ggplot2") CA_RC95<-raster("CA_RC95.tif") CA_RC95<-CA_RC95*0.0025 df = as.data.frame(CA_RC95) df = na.omit(df) ggplot(df,aes(CA_RC95)) + geom_histogram(aes(y=..count..*25),binwidth = 50)+ geom_density(aes(y=50 * ..count..*25), col="blue",size=2, adjust=10000)+ xlab("Contributing Area [ha] \n Hilltop Valley") + ylab("Area [m2]")+ theme(axis.text.x = element_text(face="bold", size=30), plot.title = element_text(color="black", size=40, face="bold",hjust=0.5), axis.title.x=element_text(color="blue", size=40, face="bold"), axis.text.y = element_text(face="bold", size=30), axis.title.y=element_text(color="blue", size=40, face="bold"))+ scale_y_continuous(trans = 'log10')+ ggtitle("Upstream area of surfaces with \n High Road Connectivity (RC > 3.12)") </code></pre> <p>Bibliography</p> <p>1. OpenStreetMap contributors. Planet dump retrieved from https://planet.osm.org. https://www.openstreetmap.org/ (2017).</p> <p>2. Cavalli, M., Trevisani, S., Comiti, F. & Marchi, L. Geomorphometric assessment of spatial sediment connectivity in small Alpine catchments. <em>Geomorphology</em> <strong>188</strong>, 31–41 (2013).</p> <p>3. Crema, S. & Cavalli, M. SedInConnect: a stand-alone, free and open source tool for the assessment of sediment connectivity. <em>Computers and Geosciences</em> <strong>111</strong>, 39–45 (2018).</p> <p>4. Tarboton, D. G. A new method for the determination of flow directions and upslope areas in grid digital elevation models. <em>Water Resources Research</em> <strong>33</strong>, 309–319 (1997). </p>
Indirect impacts of a novel wildfire on a well-studied desert stream: connectivity, carbon, and communities
In 2020 the Bush Fire burned approximately half of the Sycamore Creek watershed in central Arizona. Sycamore Creek has been subject to >40 years of research and the stream has been monitored by NEON since 2017. We studied the effects of fire on biogeochemistry of the stream and its watershed. We deployed autosamplers to monitor stream chemistry during storms on the mainstem and in ephemeral tributaries draining burned and unburned watersheds. The storm sampling program commenced nearly a year following the fire because absence of summer monsoon or winter storms in 2020-21 resulted in no flow in tributaries and intermittent flow in the mainstem. Water chemistry was measured during 14 monsoon storms of 2021 and winter frontal storms of 2021-22 with samples of baseflow collected in the mainstem during intervening periods. Water samples were analyzed for dissolved organic carbon, nitrogen, phosphorus, and major anions and cations. We also measured nutrient content of ash and chemistry of ash leachate as a potential source of solutes to stream biota.
Functional Connectivity of Music-Induced Analgesia in Fibromyalgia
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Experimental data of dissipative embedded column base connections tested under cyclic lateral loading
<p>This experimental dataset is comprised of the following items:</p> <p>(a) the deduced experimental data of conventional/dissipative embedded column base connection specimens, which contains base moment, column drift ratio, and axial shortening responses (TestData.xlsx);</p> <p>(b) photos of each specimen taken during cyclic loading (C-N-0_Test_Photos.7z, D-M1-1_Test_Photos.7z, D-M1-3_Test_Photos.7z, D-M1-5_Test_Photos.7z, D-M2-2_Test_Photos.7z);</p> <p>(c) characteristic videos for each specimen that demonstrate the cyclic behavior (Test_Video.7z);</p> <p>(d) Digital image correlation (DIC) images taken during cyclic loading to obtain strain fields near the steel column/reinforced concrete foundation interface (C-N-0_DIC_Photos.7z, D-M1-1_DIC_Photos.7z, D-M1-3_DIC_Photos.7z, D-M1-5_DIC_Photos.7z, D-M2-2_DIC_Photos.7z); </p> <p>(e) Videos that demonstrate strain fields of column flanges of both conventional and dissipative embedded column base connection specimens (DIC_Video.7z) </p> <p>Please read the "README" file contained in each folder for more detailed information regarding each data.</p> <p> </p>
A dataset to model Levantine landcover and land-use change connected to climate change, the Arab Spring and COVID-19
<p><strong>Overview</strong></p> <p>This dataset is the repository for the following paper submitted to <em>Data in Brief</em>:</p> <p>Kempf, M. A dataset to model Levantine landcover and land-use change connected to climate change, the Arab Spring and COVID-19. <em>Data in Brief</em> (submitted: December 2023).</p> <p>The <em>Data in Brief</em> article contains the supplement information and is the related data paper to:</p> <p>Kempf, M. Climate change, the Arab Spring, and COVID-19 - Impacts on landcover transformations in the Levant. <em>Journal of Arid Environments</em> (revision submitted: December 2023).</p> <p><strong>Description/abstract</strong></p> <p>The Levant region is highly vulnerable to climate change, experiencing prolonged heat waves that have led to societal crises and population displacement. Since 2010, the area has been marked by socio-political turmoil, including the Syrian civil war and currently the escalation of the so-called Israeli-Palestinian Conflict, which strained neighbouring countries like Jordan due to the influx of Syrian refugees and increases population vulnerability to governmental decision-making. Jordan, in particular, has seen rapid population growth and significant changes in land-use and infrastructure, leading to over-exploitation of the landscape through irrigation and construction. This dataset uses climate data, satellite imagery, and land cover information to illustrate the substantial increase in construction activity and highlights the intricate relationship between climate change predictions and current socio-political developments in the Levant. </p> <p><strong>Folder structure</strong></p> <p>The main folder after download contains all data, in which the following subfolders are stored are stored as zipped files: </p> <p>“code” stores the above described 9 code chunks to read, extract, process, analyse, and visualize the data.</p> <p>“MODIS_merged” contains the 16-days, 250 m resolution NDVI imagery merged from three tiles (h20v05, h21v05, h21v06) and cropped to the study area, n=510, covering January 2001 to December 2022 and including January and February 2023.</p> <p>“mask” contains a single shapefile, which is the merged product of administrative boundaries, including Jordan, Lebanon, Israel, Syria, and Palestine (“MERGED_LEVANT.shp”).</p> <p>“yield_productivity” contains .csv files of yield information for all countries listed above.</p> <p>“population” contains two files with the same name but different format. The .csv file is for processing and plotting in R. The .ods file is for enhanced visualization of population dynamics in the Levant (Socio_cultural_political_development_database_FAO2023.ods).</p> <p>“GLDAS” stores the raw data of the NASA Global Land Data Assimilation System datasets that can be read, extracted (variable name), and processed using code “8_GLDAS_read_extract_trend” from the respective folder. One folder contains data from 1975-2022 and a second the additional January and February 2023 data.</p> <p>“built_up” contains the landcover and built-up change data from 1975 to 2022. This folder is subdivided into two subfolder which contain the raw data and the already processed data. “raw_data” contains the unprocessed datasets and “derived_data” stores the cropped built_up datasets at 5 year intervals, e.g., “Levant_built_up_1975.tif”. </p> <p><strong>Code structure</strong></p> <p>1_MODIS_NDVI_hdf_file_extraction.R </p> <p><br>This is the first code chunk that refers to the extraction of MODIS data from .hdf file format. The following packages must be installed and the raw data must be downloaded using a simple mass downloader, e.g., from google chrome. Packages: terra. Download MODIS data from after registration from: https://lpdaac.usgs.gov/products/mod13q1v061/ or https://search.earthdata.nasa.gov/search (MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061, last accessed, 09th of October 2023). The code reads a list of files, extracts the NDVI, and saves each file to a single .tif-file with the indication “NDVI”. Because the study area is quite large, we have to load three different (spatially) time series and merge them later. Note that the time series are temporally consistent.</p> <p><br>2_MERGE_MODIS_tiles.R</p> <p><br>In this code, we load and merge the three different stacks to produce large and consistent time series of NDVI imagery across the study area. We further use the package gtools to load the files in (1, 2, 3, 4, 5, 6, etc.). Here, we have three stacks from which we merge the first two (stack 1, stack 2) and store them. We then merge this stack with stack 3. We produce single files named NDVI_final_*consecutivenumber*.tif. Before saving the final output of single merged files, create a folder called “merged” and set the working directory to this folder, e.g., setwd("your directory__MODIS/merged").</p> <p><br>3_CROP_MODIS_merged_tiles.R</p> <p><br>Now we want to crop the derived MODIS tiles to our study area. We are using a mask, which is provided as .shp file in the repository, named "MERGED_LEVANT.shp". We load the merged .tif files and crop the stack with the vector. Saving to individual files, we name them “NDVI_merged_clip_*consecutivenumber*.tif. We now produced single cropped NDVI time series data from MODIS. <br>The repository provides the already clipped and merged NDVI datasets.</p> <p><br>4_TREND_analysis_NDVI.R</p> <p><br>Now, we want to perform trend analysis from the derived data. The data we load is tricky as it contains 16-days return period across a year for the period of 22 years. Growing season sums contain MAM (March-May), JJA (June-August), and SON (September-November). December is represented as a single file, which means that the period DJF (December-February) is represented by 5 images instead of 6. For the last DJF period (December 2022), the data from January and February 2023 can be added. The code selects the respective images from the stack, depending on which period is under consideration. From these stacks, individual annually resolved growing season sums are generated and the slope is calculated. We can then extract the p-values of the trend and characterize all values with high confidence level (0.05). Using the ggplot2 package and the melt function from reshape2 package, we can create a plot of the reclassified NDVI trends together with a local smoother (LOESS) of value 0.3.<br>To increase comparability and understand the amplitude of the trends, z-scores were calculated and plotted, which show the deviation of the values from the mean. This has been done for the NDVI values as well as the GLDAS climate variables as a normalization technique. </p> <p><br>5_BUILT_UP_change_raster.R</p> <p><br>Let us look at the landcover changes now. We are working with the terra package and get raster data from here: https://ghsl.jrc.ec.europa.eu/download.php?ds=bu (last accessed 03. March 2023, 100 m resolution, global coverage). Here, one can download the temporal coverage that is aimed for and reclassify it using the code after cropping to the individual study area. Here, I summed up different raster to characterize the built-up change in continuous values between 1975 and 2022. </p> <p><br>6_POPULATION_numbers_plot.R</p> <p><br>For this plot, one needs to load the .csv-file “Socio_cultural_political_development_database_FAO2023.csv” from the repository. The ggplot script provided produces the desired plot with all countries under consideration. </p> <p><br>7_YIELD_plot.R</p> <p><br>In this section, we are using the country productivity from the supplement in the repository “yield_productivity” (e.g., "Jordan_yield.csv". Each of the single country yield datasets is plotted in a ggplot and combined using the patchwork package in R. </p> <p><br>8_GLDAS_read_extract_trend</p> <p><br>The last code provides the basis for the trend analysis of the climate variables used in the paper. The raw data can be accessed https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20Noah%20Land%20Surface%20Model%20L4%20monthly&page=1 (last accessed 9th of October 2023). The raw data comes in .nc file format and various variables can be extracted using the [“^a variable name”] command from the spatraster collection. Each time you run the code, this variable name must be adjusted to meet the requirements for the variables (see this link for abbreviations: https://disc.gsfc.nasa.gov/datasets/GLDAS_CLSM025_D_2.0/summary, last accessed 09th of October 2023; or the respective code chunk when reading a .nc file with the ncdf4 package in R) or run print(nc) from the code or use names(the spatraster collection). <br>Choosing one variable, the code uses the MERGED_LEVANT.shp mask from the repository to crop and mask the data to the outline of the study area.<br>From the processed data, trend analysis are conducted and z-scores were calculated following the code described above. However, annual trends require the frequency of the time series analysis to be set to value = 12. Regarding, e.g., rainfall, which is measured as annual sums and not means, the chunk r.sum=r.sum/12 has to be removed or set to r.sum=r.sum/1 to avoid calculating annual mean values (see other variables). Seasonal subset can be calculated as described in the code. Here, 3-month subsets were chosen for growing seasons, e.g. March-May (MAM), June-July (JJA), September-November (SON), and DJF (December-February, including Jan/Feb of the consecutive year).<br>From the data, mean values of 48 consecutive years are calculated and trend analysis are performed as describe above. In the same way, p-values are extracted and 95 % confidence level values are marked with dots on the raster plot. This analysis can be performed with a much longer time series, other variables, ad different spatial extent across the globe due to the availability of the GLDAS variables. </p> <p><br>(9_workflow_diagramme) this simple code can be used to plot a workflow diagram and is detached from the actual analysis.</p> <p>___</p> <p>Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft, Writing - Review & Editing, Visualization, Supervision, Project administration, and Funding acquisition: Michael Kempf</p> <p>___</p> <p><strong>Acknowledgements</strong></p> <p><span><span><span><span>I would like to thank three </span></span></span></span><span><span><span><span><span>anonymous</span></span></span></span></span><span><span><span><span> reviewers for their constructive comments and suggestions that sharpened the paper in the Journal of Arid Environments. I am particularly grateful to the Swiss National Science Foundation (SNSF/SNF) to fund my research project </span></span></span></span><span><span><span><span><em><span>EXOCHAINS - Exploring Holocene Climate Change and Human Innovations across Eurasia</span></em></span></span></span></span><span><span><span><span> at the University of Basel under grant number </span></span></span></span><span><span><span><span>TMPFP2_217358.</span></span></span></span></p> <p> </p> <p><span><span><span><span>__</span></span></span></span></p> <p><br>All data underlying the results of this article are publicly available on the internet:</p> <p>GLDAS Noah Land Surface Model L4 data: NASA's Earth Science Data Systems (ESDS) Program, https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20Noah%20Land%20Surface%20Model%20L4%20monthly&page=1 (last accessed 09th December 2023); </p> <p><br>Country borders: https://www.geoboundaries.org (last accessed 7th of March 2023) and Natural Earth https://www.naturalearthdata.com/ (last accessed 5th of December 2023);</p> <p><br>FAOstats (Food and Agriculture Organisation of the United Nations: https://www.fao.org/faostat/en/#data/QCL (last accessed 7th of March 2023);</p> <p><br>Global Human Settlement Layer datasets (GHSL): https://ghsl.jrc.ec.europa.eu/download.php?ds=bu (last accessed 7th of March 2023);</p> <p><br>Population development: <br>FAO, https://www.fao.org/countryprofiles/index/en/?iso3=JOR (last accessed 4th of March 2023); <br>the Worldbank, https://www.worldbank.org/en/home (last accessed: 04th of March 2023); <br>Worlddata.info, https://www.worlddata.info/asia/palestine/populationgrowth.php (last accessed 4th of March 2023);</p> <p><br>Water demand and population numbers (Tab. 1): https://www.fao.org/faostat/en/#data/OA; https://databank.worldbank.org/reports.aspx?source=world-development-indicators# (last accessed 13th of December 2023);</p> <p><br>MODIS: Earthdata server of the United States Geological Survey (USGS), MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006, https://lpdaac.usgs.gov/products/mod13q1v061/ (last accessed 7th of March 2023).</p> <p><br>Competing interests statement:<br>The author declares no conflict of interest.<br>The author has no relevant financial or non-financial interests to disclose.<br>Data availability: All data underlying the analyses are freely available on the internet and where applicable, sources are cited in the text.<br>Ethical approval: This article does not contain any studies with human participants performed by any of the authors.<br>Informed consent: This article does not contain any studies with human participants performed by any of the authors.</p>
Calibration Dataset of Device for Measuring Forces and Torques in Flexible Connection Joints for Parabolic Trough Collector
<p>This dataset corresponds with the calibration tests of device for measuring forces and torques in flexible connection joints for parabolic trough collector. This work has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 823802 (SFERA-III), and it is related with the milestone number MS29 of task 10.1.B - Enhancement of sensor monitoring/calibration and measurement accuracy of laboratory test benches of RI.</p>
Replication data for "Climate change may induce connectivity loss and mountaintop extinction in Central American forests"
<p>Model code and predictor data underlying the publication "<strong>Climate change may induce connectivity loss and mountaintop extinction in Central American forests</strong>".</p>
H2020 Platone German Demonstrator - Baseline Active Power Exchange at Grid Connection Point (Medium Voltage/Low Voltage)
<p>The given data are computed values for the active power exchange at the medium (MV)/low voltage grid connecting feeder (active power). The data are provided as 15-minutes mean values in kilowatt. The computed indicate the power exchange that would have been measured, in case no use case would have been applied in the field (control of batteries).</p> <p><strong>Data Description:</strong></p> <ul> <li>p_tei_c_mean = arithmetic mean of p_tei computed in 1-minute intervals devided by number of samples available for computing within 15 minutes (p_tei_count)</li> <li>p_tei_c_min = the minimum value (1-minute mean) computed within the period of p_tei_mean (15-minutes)</li> <li>p_tei_c_max = the maximum value (1-minute mean) computed within the period of p_tei_mean period (15-minutes)</li> </ul> <p><strong>Field Test Setup</strong></p> <p>The field test setup of the demonstrator consists of a MV/LV substation, 89 households, 450kW of installed PV generation capacity, a large scale battery with 300 kW and 850 kWh capacity. </p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 864300</p>
Image sets used in the development of a connected auto-encoders based approach to separate mixed X-radiographs from double-sided paintings
<p>The following sets of images were used during the development of an algorithm (described in the publication detailed below) designed to separate the mixed X-radiographs from double-sided paintings into two hypothetical X-ray images corresponding to each side of the painting, when visible images of the two sides of the painting are available.</p> <p>The images sets are taken from a painting that is only painted on one side and were used to assess the regularization parameters associated with the separation approach. The details are taken from the visible image and the X-radiograph of Anthony van Dyck’s painting <em>Lady Elizabeth Thimbelby and Dorothy, Viscountess Andover</em> dated to about 1635 and now in the collection of the National Gallery in London (NG6437). See <a href="https://www.nationalgallery.org.uk/paintings/anthony-van-dyck-lady-elizabeth-thimbelby-and-her-sister">https://www.nationalgallery.org.uk/paintings/anthony-van-dyck-lady-elizabeth-thimbelby-and-her-sister</a> for further details of the painting.</p> <p>The code can be downloaded from: <a href="https://github.com/ART-ICT/Xray_Separation_2RGB">https://github.com/ART-ICT/Xray_Separation_2RGB</a> and the algorithm is described in W. Pu, B. Sober, N. Daly, C. Zhou, Z. Sabetsarvestani, C. Higgitt, I. Daubechies and M. Rodrigues, ‘Image Separation with Side Information: A Connected Auto-Encoders Based Approach’, <em>Transactions on Image Processing, </em>2023 </p> <p><strong>All images © The National Gallery, London</strong></p> <p> </p> <p><strong><em>Datasets available: </em></strong></p> <p><strong>NG6437_vis_800pixel_230502.tif</strong>: 800 pixel thumbnail visible image of the entire painting showing the location of the two details used for the algorithm development. This image is derived from a visible image of the whole painting acquired 25 November 2019 (Original file: N-6437-00-000041.tif; 6272 x 5940 pixels).</p> <p><strong>NG6437_xray_800pixel_230502.tif</strong>: 800 pixel thumbnail image of the X-radiograph of the entire painting showing the location of the two details used for the algorithm development. This image is derived from the composite X-radiography of the whole painting created by mosaicking digital scans of the individual sheets of film and then registering the resulting image to the high resolution visible image described above (Original file: N-6437-00-000049.tif; 36847 x 32516 pixels).</p> <p><strong>NG6437_vis_crop_01_230502.tif</strong>: 1543 x 2078 pixel detail taken from the high resolution visible image described above.</p> <p><strong>NG6437_xray_crop_01_230502.tif</strong>: 1543 x 2078 pixel detail of the X-radiograph corresponding to NG6437_vis_crop_01_230502.tif. The X-ray images were acquired using sheets of film (27 November 2019) and 16-bit digital scans were then produced (original files: N-6437-00-000047-009 and -014 (each 9539 x 7199 pixels), processed 28 January 2020). This crop is an 8-bit composite image of 2 X-ray plates that had been manually registered to the high resolution visible image described above using Adobe Photoshop.</p> <p><strong>NG6437_vis_crop_02_230502.tif</strong>: 1562 x 2023 pixel detail taken from the high resolution visible image described above.</p> <p><strong>NG6437_xray_crop_02_230502.tif</strong>: 1543 x 2078 pixel detail of the X-radiograph corresponding to NG6437_vis_crop_02_230502.tif. The X-ray images were acquired using sheets of film (27 November 2019) and 16-bit digital scans were then produced (original files: N-6437-00-000047-002 and -007 (each 9539 x 7199 pixels), processed 28 January 2020). This crop is an 8-bit composite image of 2 X-ray plates that had been manually registered to the high resolution visible image described above using Adobe Photoshop.</p> <p><strong>NG6437_vis_crop_03_230502.tif</strong>: 2088 x 2088 pixel detail taken from the high resolution visible image described above.</p> <p><strong>NG6437_xray_crop_03_230502.tif</strong>: 2088 x 2088 pixel detail taken from the composite X-radiograph described above corresponding to NG6437_vis_crop_03_230502.tif. </p> <p><strong>NG6437_vis_crop_04_230502.tif</strong>: 2088 x 2088 pixel detail taken from the high resolution visible image described above.</p> <p><strong>NG6437_xray_crop_04_230502.tif</strong>: 2088 x 2088 pixel detail taken from the composite X-radiograph described above corresponding to NG6437_vis_crop_04_230502.tif. </p> <p> </p>
Integrated freshwater abundance and connectivity clusters at the Hydrologic Unit 8 scale for the Midwest and Northeast U.S.A. – freshwater metric variables and k-means cluster assignment
This dataset includes integrated freshwater abundance and connectivity cluster output, principal component scores, and lake, wetland, and stream abundance and connectivity metrics measured at the Hydrologic Unit 8 (HU8) scale for 17 U.S. states in the Midwest and Northeast regions (appr. 1,800,000 km2). The intent of the cluster analysis is to characterize the macroscale patterns of the integrated freshwater landscape that includes lakes, wetlands, and streams and their surface connectivity attributes. We define freshwater connectivity as the permanent surface hydrologic connections that link lakes, wetlands, and streams and measure connectivity as the landscape position of systems within stream networks. Geographic data used in the analysis are in LAGOS-NE-GEO database v. 1.03 (Lake multi-scaled geospatial and temporal database), an integrated, multi-thematic geographic database (Soranno et al. 2015). The integrated freshwater clusters were created through a multi-step process as follows: 1) we quantified multiple freshwater connectivity metrics for lakes, streams, and wetlands separately, 2) we performed principal components analysis (PCA) on the connectivity metric values for each freshwater type to reduce collinearity, and 3) we performed k-means cluster analysis to group spatial units with similar freshwater connectivity characteristics. The resulting freshwater clusters are representations of the macroscale patterns of freshwater abundance and connectivity in the landscape.
Freshwater connectivity clusters for lakes, wetlands, and streams at the Hydrologic Unit 12 scale in the Midwest and Northeast U.S.A. – freshwater metric variables and K-means cluster assignment
This dataset includes freshwater connectivity cluster output and principal component scores for lakes, wetlands, and streams measured at the Hydrologic Unit 12 (HU12) scale in 17 U.S. states in the Midwest and Northeast regions (appr. 1,800,000 km2). The intent of the cluster analysis is to characterize the macroscale patterns of freshwater connectivity attributes. We define freshwater connectivity as the permanent surface hydrologic connections that link lakes, wetlands, and streams and measure connectivity as the landscape position of systems within stream networks. Geographic data used in the analysis are in LAGOS-NE-GEO database v. 1.03 (Lake multi-scaled geospatial and temporal database), an integrated, multi-thematic geographic database (Soranno et al. 2015). Freshwater connectivity clusters were created separately for lakes, wetlands, and streams through a multi-step process as follows: 1) we quantified multiple freshwater connectivity metrics, 2) we performed principal components analysis (PCA) on the connectivity metric values for each freshwater type to reduce collinearity, and 3) we performed k-means cluster analysis to group spatial units with similar freshwater connectivity characteristics. The resulting freshwater clusters are representations of the macroscale patterns of lake, wetland, and stream connectivity in the landscape.
LAGOS-US NETWORKS v1.0: Data module of surface water networks characterizing connections among lakes, streams, and rivers in the conterminous U.S
Knowing the degree of surface water connectivity among aquatic ecosystems can help scientists better understand and predict the movement of materials and biota across ecosystems. Methods to quantify surface water networks that include lake and stream connections at broad spatial scales are rare because it is difficult to balance accurate estimates of surface water connectivity and computational challenges. The LAGOS-US NETWORKS (NETS) module contains surface connectivity metrics for lake networks across the conterminous United States. We applied a graph theory approach to identify lake networks (i.e. a set of lakes connected by streams either upstream, downstream, or both) created from the medium resolution NHD lakes, streams, and rivers and subsequently derive surface water connectivity metrics for lakes and networks. Using this approach, we created a total of 898 networks that include 86,511 lakes. The NETS module includes a table with metrics for connections between lakes (both upstream and downstream), dams, network position, and whole networks. NETS also includes a flow table and bidirectional and unidirectional distance tables that provide the distances between every pair of connected lakes.
Context dependency of effect of fungal connections between plants and biocrusts
Conceptual context: Species interactions may couple the resource dynamics of different primary producers and may enhance productivity by reducing loss from the system. In low-resource systems, this biotic control may be especially important for maintaining productivity. In drylands, the activities of vascular plants and biological soil crusts can be decoupled in space because biocrusts grow on the soil surface but plant roots are underground, and decoupled in time due to biocrusts activating with smaller precipitation events than plants. Soil fungi are hypothesized to functionally couple the plants and biocrusts by transporting nutrients. We studied whether disrupting fungi between biocrusts and plants reduces nitrogen transfer and retention and decreases primary production as predicted by the fungal loop hypothesis. Additionally, we compared varying precipitation regimes that can drive different timing and depth of biological activities. Methodological approach: We used field mesocosms in which the potential for fungal connections between biocrusts and roots remained intact or were impeded by mesh. We imposed a precipitation regime of small, frequent or large, infrequent rain events. We used 15N to track fungal-mediated nitrogen (N) transfer. We quantified microbial carbon use efficiency and plant and biocrust production and N content.
Subcortical DMN functional connectivity
Open the record for dataset details and reuse information.
Time series of electricity output for large grid connected photovoltaic installations in Chile
<p>These data sets accompany the paper "Simulation of multi-annual time series of solar photovoltaic power: is the ERA5-land reanalysis the next big step?". They include capacity factors (values 0 to 1) in hourly temporal resolution of 103 large PV installations in Chile derived from official sources as well as simulated capacity factors using PV_LIB with ERA5-land and MERRA-2 reanalysis data. The data covers the period 2014-2018 and simulations were performed assuming either a "fixed" system with orientation towards north and inclination equal to the latitude (i.e. optimal inclination) or a horizontal single axis “tracking” system with backtracking. Furthermore, accuracy indicators (Pearson’s correlation, mean bias error and root mean square error) are provided, comparing the simulated time series with capacity factors derived from official sources. Capacity factors were calculated for the 103 installations and both alternative configurations (“fixed” and “tracking”) but only a subset of 23 installations has reference data of sufficient quality to allow for a validation (for a more detailed description, see the paper). Indicators were also calculated for all installations and configurations but should be only compared for installations inside a particular configuration: there are 14 systems classified as “tracking” and 9 as “fixed”. Further details are available in the paper and the entire Python and R code is available on github at <a href="https://github.com/inwe-boku/PV_from_era5">https://github.com/inwe-boku/PV_from_era5</a>. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.