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Nutrient Network (NutNet) basic sampling at the Georgia Coastal Ecosystems LTER in summer 2019.
NutNet is a distributed ecological experiment with a sampling component and an experiment. The goal of this work was to conduct the NutNet sampling protocol at the GCE site. Comparisons can be made 1) within the GCE site, and 2) between GCE and other NutNet sites, most of which are terrestrial. More about Nutnet here: http://www.nutnet.umn.edu/.
Air and soil temperature data from the Reference Stand network at the Andrews Experimental Forest, 1971 to present
The current network of temperature measurement sites are designed to represent spatial variability of air and soil temperature in rugged mountain topography, and serve as second-level stations to capture specific microclimate temperatures in conjunction with a network of Benchmark Meteorological Stations (MS001). The air and soil thermograph network has been reduced from the historical network of 37 sites originally established. Currently there are 10 measurement sites with two of these sites measuring relative humidity in addition to air and soil temperature. An original network of 19 sites (RS01-RS19) were established during the International Biome Program in the early 1970's. Emphasis on phenology, plant moisture stress, and leaf nutrient content led to extending this network of air and soil temperature measurement. A plant community classification system (Dyrness et al., 1971) was used as a primary means of stratification, and a set of permanent vegetation plots (Reference Stands) was installed to represent forest communities with distinct vegetation and hypothesized different environments (Dyrness et al., 1974). A thermograph network was installed within the reference stands in the early 1970's (Zobel et al., 1974), and vegetation standing crop, tree growth and mortality, and plant succession were also measured. The majority of these sites were established to monitor micro-meteorological data under the canopy. The purpose of this network was to provide air and soil temperature data for modeling photosynthesis, respiration, phenology, and decomposition, and to measure environmental gradients.
Social Network Survey of Forest Landowners in New Hampshire and Vermont 2010
Forests provide invaluable services, and nationally a significant portion of them are owned by millions of individual, private decision makers. Butler (2008) reports that the vast majority of owners (92%, representing 87% of all family forest lands) make management decisions for their land on their own, with a very small minority relying on the advice of professional foresters. Nationally, Butler (2008) goes on to report that 4 % of family forest owners (representing 17% of family forest land) have a professionally prepared management plan for their lands. It is clear that family forests are important, yet millions of owners are apparently not making decisions on the basis of professional advice. Our goal was to improve our understanding of who landowners seek information from when making decisions about their forestland. More specifically, our objective was to explore the possible role of egocentric social networks that landowners may rely upon for information when making a decision. Can we estimate their composition, the possible role of professionals, and the nature of the relationships, in terms of involvement, helpfulness, and trust? Finally, we explored landowner egocentric networks in two similar and adjacent states (Vermont and New Hampshire) that have different programmatic approaches to reaching landowners. Do these result in different ways that landowners acquire information? Using two different approaches through a mail survey methodology, we assessed the extent to which private woodland owners are connected to other people, the degree to which they are considered information sources, and the nature of their decision making behavior. Respondents consistently report valuable connections to non-professionals. They similarly report nominal contact to public foresters, whose role is to inspire prudent forest stewardship on privately held lands. This minimal contact is consistent in two different states with rather different programmatic goals (e.g., educatio
Belowground Carbon Observatory Network (BeCON) at Harvard Forest since 2020
A central goal of the recent Harvard Forest C Synthesis activity was to identify high-priority needs for C-cycle research at the forest. While there were a number of areas needing follow-up research, belowground C pools and fluxes remain the single largest gap in the C budget and therefore our understanding of the C cycle of the forest. To this end, we developed the Belowground Carbon Observatory Network (BeCON) as a part of the LTER-VI grant proposal. The long-term goal of BeCON is to understand temporal and spatial variations in belowground C pools in response to the various environmental changes the forest now faces [e.g., rising CO2, changes in atmospheric N, S and O3 chemistry, invading insects, land-use change, immigrating (large) herbivores]. There are two great challenges in achieving this goal: (1) excessive spatial heterogeneity in belowground C pools; and (2) lack of automated systems for analysis. These characteristics make it very difficult to quantify changes because the work is intrinsically labor intensive with low replication and the noisy data lead to low statistical power. Here, we measured soil carbon content, root biomass and carbon content, and annual root productivity following the protocols for BeCON.
Climate data for saddle catchment sensor network, 2017 - ongoing.
Spatial and temporal variability characterizes virtually all ecosystems, with resource supply changing over the course of growing season and across years due to climate variation. To better understand spatial heterogeneity in ecological response across landscape positions, we established a 16-node sensor array within a 45 hectare catchment landscape that measures temporal variability of important biogeochemical and hydrological controls on ecosystem processes. The array was established at the Niwot Saddle catchment in order to accompany long term water quality and discharge records taken at the top and bottom of this catchment. The region forms an important ecological linkage between the the terrestrial areas of the Niwot Ridge LTER and the aquatic component in the Green Lakes Valley.
Soil Organic Matter Mechanisms of Stabilization (SOMMOS) - enhanced soil characterization data from 40 National Ecological Observatory Network (NEON) sites
Soil organic matter (SOM) is a critical linkage among many ecosystem services that sustain our society and life on Earth. It is the primary energy source for microbes and the principal storehouse of water necessary for plant growth. SOM also stores nutrients for plants and sorbs pollutants that otherwise could contaminate food and water supplies. Soils also help regulate climate by storing carbon that would otherwise be released to the atmosphere and contribute to climate change. The SOMMOS project investigated processes in the soil that protect SOM from being decomposed by microbes, processes that increase its sensitivity to environmental changes, and how changes in climate and land management influence the amount and stability of SOM. The project, which was a collaboration between scientists from the National Ecological Observatory Network (NEON), University of Colorado, University of Michigan, Oregon State University, Virginia Polytechnic Institute and State University, and the USDA-Forest Service, took advantage of soil samples collected across NEON, a major NSF investment in environmental monitoring that covers the entire United States. This continental-scale soil sample set was analyzed for a wide array of physical and chemical properties, well beyond those typically measured on such a large-scale sample set, including radiocarbon, extractable metals, organic matter chemistry by pyrolysis-GCMS, liquid extract fluorescence spectroscopy, and more. In addition to this dataset, archived samples are available from the project for sharing with interested researchers.
National Phenology Network tree phenology at Crosby Farm Adaptive Silviculture for Climate Change study, 2021-2025
Phenology is the study of relations between climate and periodic biological phenomena, such as bud break or leaf drop in deciduous trees. Phenology is a leading indicator of climate change, and the response of urban tree species to climate can help inform how to manage for a more resilient, and adaptive urban tree canopy. This dataset contains tree phenology data from the The Mississippi National River and Recreation Area (MNRRA) Urban Affiliate Adaptive Silviculture for Climate Change (ASCC) project located at Crosby Farm Regional Park. This dataset includes Individual Phenometrics, Site Phenomentrics, Status and Intensity, and Magnitude Phenometrics. This data was collected through mobile app submissions to Nature's Notebook and downloaded from the National Phenology Network Observation Portal, filtered by date range 01/01/2021 to 02/26/2024 and for Crosby Farm ASCC. Data Attribution: USA National Phenology Network. 2024. Plant and Animal Phenology Data. Data type: Status & Intensity, Individual Phenometrics, Site Phenometrics, Magnitude Phenometricts. 01/01/2021-02/26/2024 for Region: 45.221627°, -92.554965° (UR); 44.599185°, -93.5712° (LL). USA-NPN, St. Paul, Minnesota, USA. Data set accessed 03/19/2024 at http://doi.org/10.5066/F78S4N1
Plant species composition for sensor network array, 2017 - ongoing.
Above-ground plant species cover was recorded for vegetation plots in the sensor network, starting in 2017. Cover was measured annually at peak biomass using a 100 point-intercept method.
Time-lapse camera (phenocam) imagery of sensor network plots, 2017 - ongoing.
Images from time-lapse cameras were analyzed to track the greenness curves of 16 plots in the Sensor Network at Niwot Ridge. Images were taken every 30 minutes during daylight hours throughout the growing season. Cameras were angled to view 1m^2 vegetation plots located at each sensor node. Pixels in the portion of the image capturing the vegetation plot were used to calculate the green chromatic coordinate (GCC). The change in GCC over the growing season represents the growth and phenology of the plant communities captured.
Reconstructing Faces from fMRI Patterns using Deep Generative Neural Networks.
Open the record for dataset details and reuse information.
The language network reemerges during recovery from severe traumatic brain injury
Open the record for dataset details and reuse information.
Data archive for "Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network"
<p>This datasets supports the paper "Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network" submitted to IEEE Transactions in Geoscience and Remote Sensing. A preprint of the paper can be found here: <a href="https://arxiv.org/abs/2005.10374">https://arxiv.org/abs/2005.10374</a>. The code that uses these data is available at <a href="https://github.com/jleinonen/downscaling-rnn-gan">https://github.com/jleinonen/downscaling-rnn-gan</a>.</p> <p>The file "goes-samples-2019-128x128.nc" contains the training dataset called "GOES-COT" in the paper, consisting of cloud optical depth measurements from the GOES-16 satellite. The files "gen_weights*.nc" contain the generator weights saved at different time steps during training for the two different datasets described in the paper.<br> </p>
Expression-based polygenic score from the amygdala 5HTT gene network
<p>This pipeline intends to facilitate the calculation of biologically informed polygenic scores from collected genomic data. This template can be adapted to create other expression-based polygenic risk scores. Data is 1) step by step description and 2) a list of genes that compose the gene network.</p>
Energy Cycle Characteristics for 5G/6G Networks Supported by RES, UAVs, and RISs
<h2><strong>Overview</strong></h2> <p>The following dataset presents the energy cycle characteristics for 5G/6G mobile systems supported by Renewable Energy Sources (RES) and/or Unmanned Aerial Vehicles (UAVs) and Reconfigurable Intelligent Surfaces (RISs). In addition, within the dataset, the energy gain related to the engagement of RES within the Radio Access Network (RAN) has also been distinguished.</p> <h2><strong>Scenario</strong></h2> <p>The considered network scenario includes 8 three- (<em>_results_gcas.csv</em>) or one-cell (<em>_results_scas.csv</em> & <em>_results_kras.csv</em>) base stations (BSs) placed within the Poznan city (surroundings of the old market) and supported by Renewable Energy Sources — photovoltaic panels (PVs) and/or wind turbines (WTs). The aforementioned base stations can be treated as stationary towers or mobile access points (e.g., drones/UAVs). Those latter have been additionally equipped with RIS devices, which are able to reflect and manipulate a radio signal to influence occurrences such as interferences, coverage, or human exposure. However, the use of RISs has been taken into account only to evaluate the impact of the engagement of such devices on the energy side of the mobile system, omitting the changes in radio characteristics. The network traffic has been assumed to be fixed (64 mobile users (UEs) with 100 Mbps downlink — DL, and 25 Mbps uplink — UL, per each), however, its density in specific parts of the city is modeled randomly for each simulation run. The simulation runs have been performed for 4 dates (vernal equinox, summer solstice, autumn equinox, winter solstice), each one from a different season of the year. The aim of such an approach was to highlight the impact of the time of the day and the year on the energy gain obtained thanks to enabling RES generators. The weather conditions assumed within the simulation are typical for the climate in Poland. </p> <h2><strong>Methodology</strong></h2> <p>The energy-cycle calculations (system's power consumption, renewable energy production, and excessive energy storage) have been based on the mathematical formulas from the scientific literature and performed within the digital simulation runs by using the Green Radio Access Network Design (GRAND) tool (developed by teams from the Ghent University & Poznan University of Technology). The UE-BS association process within the mobile system has been done by doing multi-objective optimization using the Gurobi software, which has taken into account parameters like path loss, predicted power consumption of BSs, and guaranteed DL & UL bit rates for UEs.</p> <h2><strong>Simulation setup</strong></h2> <p>The setup of the input parameters for used mathematical models (power consumption, energy generation, energy storage) has been done in accordance with the values attached within the delivered literature positions (cited within the publications included in the <em>Related works</em> section of the following dataset) and adjusted to the considered study. Furthermore, the data used to model the network environment (building distribution, coverage area, base stations' locations) as well as to predict weather conditions are the real data (for the year 2022) collected by the city hall of Poznan, one of the Polish mobile operators, and weather stations placed in Poznan, respectively. The number of simulation runs performed has been equal to 10 (each run has included energy-cycle calculations for 4 seasons of the year), with the time step of a single run set to 1 hour of the day.</p> <h2><strong>Results</strong></h2> <p>The results of the aforementioned investigations have been included in the attached files, which can be described as follows:</p> <h3><strong>File <em>_results_gcas.csv</em></strong></h3> <p>The first column denotes the date (season of the year), for which the values have been obtained. The columns from second to fifth present observed values of the State of Charge (SoC) of a battery system (in %) for a single network cell on average in a time step. Those columns are the obtained values for the RAN, in which no RES, only PVs, only WTs, and both types of RES generators have been enabled, respectively. </p> <h3><strong>Files <em>_results_scas.csv</em> & <em>_results_kras.csv</em></strong></h3> <p>The first column denotes the date (season of the year), for which the values have been obtained. The second and third columns denote the number of drone base station (DBS) exchanges within the wireless system on average in a particular time step, where no RES and only PVs are enabled, respectively. The fourth and fifth columns present the conventional (fossil-fuels-based) energy consumption (in kWh) for the whole system in a specific time step, in which no RES and only PVs are engaged for all the access nodes. The sixth column is the energy savings (in kWh) related to the use of RES generators within the mobile network. Furthermore, the seventh and eighth columns represent the amount of renewable energy harvested from the solar radiation in total and the peak value of this amount observed during the entire day, respectively.</p> <h2><strong>Acknowledgment</strong></h2> <p>More details about the conducted studies have been described within the attached papers (<em>Related works</em> section). The data has been collected within the COST CA10210 INTERACT. M. Deruyck is a Post-Doctoral Fellow of the FWO-V (Research Foundation – Flanders, ref: 12Z5621N). The work (including the following dataset preparation) by A. Samorzewski and A. Kliks was realized within project no. 2021/43/B/ST7/01365 funded by the National Science Center in Poland.</p>
On-the-Fly Syntax Highlighting Using Neural Networks - Replication Package (Data)
<p>This dataset includes the data to replicate the study for the paper <em>On-the-Fly Syntax Highlighting Using Neural Networks</em>. It can be reused for future research in the field. We also include the detailed results obtained by executing our approach.</p> <p>HLNN-Resources.zip includes the input data already formatted to be directly used with the shared source code.</p> <p>The paper is published in the proceeding of the <em>30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE)</em>.</p>
Liquid Chromatography - Tandem Mass Spectrometry (LC-MS/MS) and Gas Chromatography - Mass Spectrometry (GC-MS) Reference Libraries from Global Natural Products Social Molecular Networking (GNPS) and National Institute of Standards and Technology (NIST) WebBook Processed for Spectral Library Matching
<div>In order to obtain a high-quality LC-MS/MS reference database for spectral library matching, we selected 22 high-quality GNPS tandem mass spectrometry databases generated under the positive ion mode. Further preprocessing similar to Huber et al involving mass-to-charge (m/z) and intensity filtering yields the database found in the file LCMS_GNPS_reference_library.csv which contains 14,705 electrospray ionization (ESI) mass spectra, each of which corresponds to a unique compound. The NIST WebBook database was used to construct GC-MS database contained in the file GCMS_NIST_WebBook.csv. This database contains 23,721 electron ionization (EI) mass spectra, each of which corresponds to a unique non-hyphenated Chemical Abstract Service (CAS) Registry Number.</div> <div> </div> <div>Both LC-MS/MS and GC-MS databases are organized into three columns: one for the identifier, one for the m/z values, and one for the intensity values. For example, if spectrum A has 20 ion fragments, then there will be 20 rows corresponding to spectrum A in the corresponding database with the identifier A repeated 20 times with the corresponding m/z and intensity values.</div>
Fast and long-term super-resolution imaging of ER nano-structural dynamics in living cells using a neural network
<p>Datasets acquired and generated for the manuscript "Fast and long-term super-resolution imaging of ER nano-structural dynamics in living cells using a neural network". The datasets include test, training and time series datasets each containing the raw data and the predicted data where it applies. </p>
Dataset of "Smart Grids Transmission Network Testbed: Design, Deployment, and Beyond"
<p>Our test environment incorporates a unique blend of physical, emulated, and virtualized<br>components, spanning from electrical substations to SCADA systems,<br>thereby offering a versatile platform for testing against cyber threats, facilitating<br>educational programs, and supporting advanced traffic simulation. Key findings<br>from our deployment highlight the testbed’s effectiveness in identifying vulnerabilities,<br>enhancing cybersecurity measures, and providing valuable hands-on<br>learning experiences. The integration of such diverse components not only exemplifies<br>a significant step forward in testbed design but also showcases its potential<br>in fostering innovation and security in the power sector. Through detailed comparisons<br>with existing testbeds, we underscore our testbed’s distinct features<br>and its contribution to bridging the gap in current methodologies, setting a new<br>benchmark for future developments in smart grid testing and education.</p>
Dataset of "Anomaly Detection in Industrial Networks: Current State, Classification, and Key Challenges"
<p>Industrial networks are adapted to their specific requirements, especially in terms of industrial processes. To ensure sufficient security in these networks, it is necessary to set and use security policies that complement government regulations, recommendations, and relevant security standards. This paper aims to provide an in-depth analysis of the anomalies occurring within the networks and propose a structure for collecting valuable data from the experimental site based on dividing anomalies into three main categories:<br>security, operational, and service anomalies (and regular traffic recognition). We present a proof-of-concept solution/design aggregating data in industrial networks for advanced anomaly classification. Multiple data sources such as industrial communication, sensor data (additional sensors controlling device behavior), and HW status data are used as data sources. A total of three scenarios (using a physical testbed) were implemented, where we achieved an accuracy of 0.8540/0.9972 in advanced anomaly classification.</p>
Incriminations in the inquisition register of Bologna (1291-1310): network data and code
<p>Cross-sectional (synchronic) projection of network data on incriminations (nominations of people in the criminal context of heresy trials) in the medieval inquisition register of Bologna, 1291–1310 in TSV format (tabulator-separated values), and R code for the article: Zbíral, David, Katia Riccardo, Tomáš Hampejs, and Zoltán Brys. ‘Gender, Kinship, and Other Social Predictors of Incrimination in the Inquisition Register of Bologna (1291–1310): Results from an Exponential Random Graph Model’. PLOS One 20, no. 2 (11 February 2025): e0315467. https://doi.org/10.1371/journal.pone.0315467.</p> <p>The data and analysis are described in the related article.</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.