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9,140 results for “networking”
COSN paper data (The Chinese Open Science Network (COSN): Building an Open Science community from scratch)
<p>This is the dataset for generating figure1 and figure 3 in the manuscript <em>The Chinese Open Science Network (COSN): Building an Open Science community from scratch </em>(Accepted by AMPPS). Preprint at: <a href="https://doi.org/10.31234/osf.io/ac9by">https://doi.org/10.31234/osf.io/ac9by</a>.</p> <p>All the data and codes are available in repo: <a href="https://github.com/OpenSci-CN/COSN_AMPPS_Paper">COSN_AMPPS_Paper</a> Accepted Version.</p>
A Non-parametric Discrete Fracture Network Model
<p>Database used to build discrete fracture networks through a non-parametric approach from Gómez et al. 2023 (DOI: 10.1007/s00603-022-03194-y). The data is structured in twelve.csv files, each with an array of size n-by-3, containing the orientation of the discontinuity (dip direction and dip of the pole) and its pseudo-trace length in meters, with n being the number of fractures in each file.</p>
Reference data and documentation for Skills4EOSC Deliverable D6.1 Mapping of existing professional networks
<p>This record presents the data underlying <strong>Skills4EOSC Deliverable D6.1 Mapping of existing professional networks</strong> and relevant documentation of the search string.</p>
Global Meteor Network observations of Crew-5 Dragon trunk re-entry 2023-04-27
<p>This dataset contains video observations by some stations of the Global Meteor Network of the re-entry of the Crew-5 dragon trunk above Arizona on 2023-04-27 around 08:52 UTC.</p> <p>There are several types of files:</p> <ul> <li>FF files: these are 10.24 second videos compressed in the four-frame format. They are just FITS files with four frames, containing per pixel 1) the maximum value over 256 frames 2) the frame nr (between 0 and 255) where the maximum occurred 3) the mean value of all 256 frames and 4) the RMS of the 256 values.</li> <li>FR files: compressed video recordings of detected fireballs. These can be read with the RMS software.</li> <li>MP4 files: rendered movies of combined FF and FR files for one station (more can be made with FR_binviewer from RMS software).</li> <li>Platepar-files: these contain astrometry corresponding to the FITS files. These can be interpreted by the RMS software.</li> <li>ECSV files: these contain manually picked points (with SkyFit2.py from RMS) along the track of the reentry. For each point, time and apparent coordinates are recorded. These files can be interpreted by the WesternMeteorPyLib trajectory solver.</li> <li>trajectory-points.txt: solutions from the trajectory solver.</li> <li>reentry-map-v4.png: a rendered map of the trajectory (made in QGIS).</li> <li>compilation.png: rendered version of the FF-files of most stations.</li> </ul> <p>The files can be processed with the software in https://github.com/CroatianMeteorNetwork/RMS and https://github.com/wmpg/WesternMeteorPyLib.</p>
Data set discussed in "Beyond Fortune 500: Women in a Global Network of Directors"
<p>Bipartite graph of directors and companies. Generated from information on the Financial Times website (<a href="https://markets.ft.com/data/equities/results">https://markets.ft.com/data/equities/results</a>), retrieved on 17 September 2016.</p> <p>Blank fields are used for missing data.</p> <p><strong>comp_nodes.csv:</strong></p> <ul> <li>id: unique identifier</li> <li>ft_country: name of the country</li> <li>ft_sector: segment of the economy in which a company operates</li> <li>ft_industry: specific business (i.e., subset of sector) in which a company operates</li> <li>ft_employees_num: number of company's employees. "NA" if the vertex represents a person or if the company's number of employees is unknown.</li> </ul> <p><strong>comp_people_edges.csv:</strong></p> <ul> <li>person_id:</li> <li>comp_id: company identifier. It matches the identifier in comp_nodes.csv</li> </ul> <p><strong>people_one_mode_edges.csv:</strong></p> <p>Edges in the one-mode projection, in which two directors are connected if and only if they sit together on at least one board. Numbers correspond to the identifiers in unique_people_nodes.csv.</p> <p><strong>unique_people_nodes.csv:</strong></p> <ul> <li>ID: unique identifier</li> <li>age: years of age</li> <li>gender_base: "Male" or "Female"</li> </ul>
Soil moisture sensor network, design, location attributes and soil properties, Hainich, Germany, project AquaDiva
<p>This dataset contains information of the small scale highly resolved soil moisture measurement network that is part of the of the AquaDiva Critical Zone exploratory, Hainich National Park, Germany. The dataset contains information on soil measurement locations, as well as attributes to the location, the design type (random locations vs transects), as well as locations attributes like distance to the next tree and soil properties. Measurement design was first introduced by Metzger et al., (2017), and used in Fischer et al., 2023. See there for more information.</p> <p><strong>References</strong></p> <p>Fischer-Bedtke, C., Metzger, J. C., Demir, G., Wutzler, T., and Hildebrandt, A.: Throughfall spatial patterns translate into spatial patterns of soil moisture dynamics – empirical evidence, Hydrology and Earth System Sciences, https://doi.org/10.5194/hess-2022-418, 2023.</p> <p>Metzger, J. C., Wutzler, T., Dalla Valle, N., Filipzik, J., Grauer, C., Lehmann, R., Roggenbuck, M., Schelhorn, D., Weckmüller, J., Küsel, K., Totsche, K. U., Trumbore, S., and Hildebrandt, A.: Vegetation impacts soil water content patterns by shaping canopy water fluxes and soil properties, Hydrological Processes, 31, 3783–3795, https://doi.org/10.1002/hyp.11274, 2017.</p>
Polarized and nonpolarized Twitter networks from the 2019 Finnish Parliamentary Elections
<p><strong>Polarized and nonpolarized Twitter networks from the 2019 Finnish Parliamentary Elections</strong></p> <p>This dataset includes 183 Twitter retweet networks collected during the 2019 Finnish Parliamentary Elections.</p> <p>The first 150 networks are built around single hashtags, such as #police, #nature, and #immigration. The remaining 33 networks are constructed using a combination of hashtags focused on specific topics like climate change and economic policy.</p> <p>Each filename consists of two parts: the first part indicates whether the network is based on a single hashtag (in lowercase) or a set of hashtags (in uppercase). The second part represents the tweet period.</p> <ul> <li> <p>"p1" corresponds to the pre-election period (March 1 to April 14).</p> </li> <li> <p>"p2" corresponds to the inter-election period (April 15 to May 26).</p> </li> <li> <p>"p3" corresponds to the post-election period (May 27 to July 31).</p> </li> </ul> <p>The nodes in the networks represent anonymized Twitter accounts, and directed ties indicate retweet endorsements on specific topics. Each file contains three columns: retweeter, retweeted, and weight.</p> <p>Please see the references for more details.</p> <p>Network labels, whether they are labeled as controversial, and whether they are based on single or multiple hashtags, can be found in the "networks_info.csv" file.</p> <p>Importantly, the dataset does not contain any identifying information or original raw data from the Twitter platform. Anonymization was achieved by shuffling the order of unique nodes across all networks and assigning each node a new identifier (ID). These new IDs were then applied to the edgelists to obtain the anonymized version.</p> <p>Kindly ensure to reference the original article(s) when utilizing this dataset.</p> <p>Chen, T. H. Y., Salloum, A., Gronow, A., Ylä-Anttila, T., & Kivelä, M. (2021). Polarization of climate politics results from partisan sorting: Evidence from Finnish Twittersphere. <em>Global Environmental Change</em>, <em>71</em>, 102348. <a href="https://doi.org/10.1016/j.gloenvcha.2021.102348">https://doi.org/10.1016/j.gloenvcha.2021.102348</a></p> <p>Salloum, A., Chen, T. H. Y., & Kivelä, M. (2022). Separating polarization from noise: comparison and normalization of structural polarization measures. <em>Proceedings of the ACM on human-computer interaction</em>, <em>6</em>(CSCW1), 1-33. <a href="https://doi.org/10.1145/3512962">https://doi.org/10.1145/3512962</a></p>
International Soil Carbon Network version 3 Database (ISCN3)
The ISCN is an international scientific community devoted to the advancement of soil carbon research. The ISCN manages an open-access, community-driven soil carbon database. This is version 3-1 of the ISCN Database, released in December 2015. It gathers 38 separate data set contributions, totaling 67,112 sites with data from 71,198 soil profiles and 431,324 soil layers. For more information about the ISCN, its scientific community and resources, data policies and partner networks visit: http://iscn.fluxdata.org/. For information about processes used to construct the DB: https://iscn.fluxdata.org/data/data-information/.
Long-term (1935-2019) tree population data from remeasurements of a large network of permanent study plots in old-growth forest, Dukes Research Natural Area, Marquette Co., MI, USA
The Dukes Research Natural Area (Hiawatha National Forest, Marquette Co., MI) amounts to ca. 100 ha of minimally disturbed original forests, including a mix of mesic 'hemlock-northern hardwood' types and peaty wetlands dominated by several species of swamp conifers and black ash (Fraxinus nigra). The RNA hosts a regular grid of 250 0.2-acre (~0.08 ha) permanent monitoring (CFI) plots. This package includes tree censuses for subsets of CFI plots conducted in 1935, 1948, and 1974-1980, and repeated censuses with mapped stems from 1989 to 2019. This 84-year record constitutes one of the longest repeated-measurement, permanent-plot data-sets for old-growth temperate forest.
Metabolism dataset: one year of high-frequency temperature, dissolved oxygen, wind, photosynthetically active radiation observations and low-frequency nutrient data for 58 lakes in the Global Lake Ecological Observatory Network
Understanding controls on primary productivity is essential for describing ecosystems and their responses to environmental change. Lake primary production is strongly controlled by inputs of nutrients and colored dissolved organic matter. While past studies have developed mathematical models of this nutrient-color paradigm, broad empirical tests of these models are scarce. We compiled data from 58 diverse and globally distributed and mostly temperate lakes to test such a model and improve understanding and prediction of the controls on lake primary production. These lakes varied widely in size (0.02-2300 km2), pelagic gross primary production (20-8000 mg C m-2 d-1), and other characteristics. The data package includes high-frequency dissolved oxygen, water temperature, wind speed, and solar radiation data as well as daily estimates of GPP and ER derived from those data. In addition, the data package includes median in-lake and stream concentrations of dissolved organic carbon and total phosphorus for a subset of 18 of those lakes.
New Hampshire Soil Sensor Network: Soil CO2 Fluxes
The goal of the New Hampshire Soil Sensor Network is to examine spatial and temporal changes in soil properties and processes as the climate changes. Data collected can also calibrate and validate models that examine how ecosystems may respond to changing climate and land use. To determine how soil processes are affected by climate change and land management, this soil sensor network measures snow depth, air temperature, soil temperature, soil volumetric water content, and soil electrical conductivity, as well as soil CO2 fluxes. This data package includes air temperature, soil temperature at 5 cm, and soil volumetric water content at 5 cm, and soil CO2 flux at the time of sampling, as well as gap-filled soil CO2 fluxes using non-linear least squares regression. Data were collected at the following sites: BRT = Bartlett Experimental Forest, Bartlett, NH; BDF = Burley-Demmerit Farm, Lee, NH; DCF = Dowst Cate Forest, Deerfield, NH; HUB = Hubbard Brook Experimental Forest, Woodstock, NH; SBM = Saddleback Mountain, Deerfield, NH; THF = Thompson Farm, Durham, NH; and Trout Pond Brook, Strafford, NH.
National Park Service - South Florida/Caribbean Inventory & Monitoring Network - BISC1 SET Surface Water level data from in Biscayne National Park, Florida, USA (2016-2025)
Surface water level data (m) was collected in Biscayne National Park (BISC) by the South Florida/Caribbean Inventory and Monitoring Network (SFCN) as part of the Soil Elevation Table (SET) vital sign monitoring program. Water level data collected from 2016 to 2025 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This dataset belongs to Site 1, known as BISC-SET-1 or BISC1. This data-package is complete.
National Park Service - South Florida/Caribbean Inventory & Monitoring Network - BISC2 SET Surface Water level data from in Biscayne National Park, Florida, USA (2017-2025)
Water level data (m) was collected in Biscayne National Park (BISC) by the National Park Service - South Florida/Caribbean Inventory and Monitoring Network (SFCN) as part of the Soil Elevation Table (SET) vital sign monitoring program. Water level data collected from 2017-2025 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This dataset belongs to Site 2, known as BISC-SET-2 or BISC2. This data-package is complete.
National Park Service - South Florida/Caribbean Inventory & Monitoring Network - SARI SET Surface Water level data from Salt River Bay National Historical Park and Ecological Preserve, St. Croix, US Virgin Islands.
Surface water level data (m) was collected in Salt River Bay National Historic Park and Ecological Preserve (SARI) by the South Florida/Caribbean Inventory and Monitoring Network (SFCN) as part of the Soil Elevation Table (SET) vital sign monitoring program. Water level data collected from 2017 to 2024 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This data-package is complete.
National Park Service - South Florida/Caribbean Inventory & Monitoring Network - Mary's Point SET Surface Water level data from Virgin Islands National Park, St. John, US Virgin Islands
Surface water level data (m) was collected in Virgin Islands National Park, Mary's Point (MARY) by the South Florida/Caribbean Inventory and Monitoring Network (SFCN) as part of the Soil Elevation Table (SET) vital sign monitoring program. Water level data collected from 2017 to 2024 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This data-package is complete.
National Park Service - South Florida/Caribbean Inventory & Monitoring Network - Water Creek SET Surface Water level data from Virgin Islands National Park, St. John, US Virgin Islands
Surface water level data (m) was collected in Virgin Islands National Park, Water Creek (WACR) by the South Florida/Caribbean Inventory and Monitoring Network (SFCN) as part of the Soil Elevation Table (SET) vital sign monitoring program. Water level data collected from 2017 to 2024 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This data-package is complete.
Oak litter decomposition parameters across 19 Nutrient Network grassland sites in response to NPK and herbivore exclusion treatments
These data are from a study examining how herbivory and nutrient supply affect long-term aboveground decomposition. A novel oak litter was decomposed across 19 grassland sites of the Nutrient Network distributed experiment. At each site, a full-factorial experiment of combined nitrogen, phosphorus, and potassium plus micronutrients ('control' or 'NPK') and mammalian herbivore exclusion ('fencing') was carried out in a randomized block design. The duration of the decomposition experiment varied by site but litter bags were harvested at approximately annual intervals for up to seven years. Litter decay parameters were calculated using four alternative statistical models of litter decomposition. Covariate data describing site and plot-level abiotic and biotic characteristics were also measured, including climate, atmospheric nitrogen deposition, live and dead aboveground biomass, and percent cover.
Dissolved CO2, CH4, and ions in groundwater from five sites in the NEON network (CARI, COMO, KING, MART, WALK), U.S., 2021-2024.
This package contains groundwater chemistry measurements collected from groundwater wells between June 2021 – May 2024 at five sites in the U.S. National Ecological Observatory Network (NEON): CARI- Caribou Creek, AK; COMO- Como Creek, CO; KING- Kings Creek, KS; MART- Martha Creek, WA; and WALK- Walker Branch, TN. The dataset includes concentrations of dissolved gases (CO2 and CH4), ions (F, Cl, NO2, Br, NO3, PO4, SO4, Na, NH4, K, Mg, Ca), silica (Si), and nutrients measured by colorimetric methods (NO3, NH4, PO4). When available, we also report measurements of water temperature, specific conductivity (SpC), pH, dissolved O2, and barometric pressure. Sample collection and analysis was conducted across three labs with additional assistance from the NEON Research Support Services program. Whenever possible, we matched our field sampling methods to NEON’s protocols for groundwater sampling to ensure samples would be comparable to preexisting data from these sites. Any deviations from these protocols are described in the methods.
Bonanza Creek LTER: Active Layer Depth or Permafrost Presence for the Regional Site Network
The initial goal (2000-2013) of these data was to define the presence/absence of permafrost within 2.5m of the surface in the regional site network. Efforts were focused mainly on sites where this was not easily deduced. The final subset of sites (2015 � present) are distributed across the 3 ecoregions of the RSN and primarily in older aged wet sites. The permafrost distribution in interior Alaska is discontinuous and dynamic; susceptible to fire and climate disturbances. Therefore, sites included in this long-term monitoring dataset may cease to be monitored as permafrost degrades and disappears or may be monitored again if permafrost is reestablished.
Organic Horizon Depth in the Regional Site Network
This dataset contains the organic depth from a subset of sites in the regional site network. Each sample was taken from near one of the 20 plots markes located every 10 meters within each site for a total of 20 samples.
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.