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6,766 results for “project”
Pulse-Press Project (P3): Continuous soil temperature and volumetric water content (VWC) measurements, McMurdo Dry Valleys, Antarctica (2012-2021, ongoing)
Climate warming in polar regions is associated with thawing of permafrost, resulting in significant changes in soil hydrology, biogeochemical cycling, and in the activity and composition of soil communities. While ongoing directional climate warming presses can elicit such responses over decadal time scales, their manifestation typically occurs as discrete thawing pulses. Indeed, in the McMurdo Dry Valleys of Antarctica, abrupt changes in community structure and biogeochemical cycling in terrestrial and aquatic ecosystems following a summer warming event (Jan. 2002) exceeded the influences of a decadal cooling trend in both magnitude and rate of response. Thus, we anticipate that climate-mediated permafrost changes and their associated impacts on soil communities and biogeochemical cycles may occur over seasonal time scales. The Pulse-Press Project (P3) experiment was established in 2012 as part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) program to investigate impacts of seasonal wetting on ecosystem structure and functioning by simulating different frequencies of permafrost thawing events in Antarctic permafrost soils. Since the top horizons of most Antarctic soils are dry permafrost (i.e., there is insufficient water content to generate ice-cement), with ice-cement or massive ice typically below 30 cm, permafrost thawing events are likely to result in subsurface movements of water that may manifest as groundwater seeps down gradient. The P3 experiment consists of three permanent plots situated on the south-facing hillslope above Many Glaciers Pond in Taylor Valley. Each plot is 15 m by 7.5 m with a trench on the upslope end that is used for experimental wetting events. The Press plot receives water every austral summer, the Pulse plot receives water every other austral summer, and the Control plot never receives water, serving as the ambient treament. Each plot is instrumented with a network of soil moisture and temperature sensors, positioned
Cascade Project at North Temperate Lakes LTER High Frequency Sonde Data from Spatial Dynamics Experiment 2018 - 2019
High-frequency continuous data for temperature, dissolved oxygen, pH, chlorophyll-a, and phycocyanin in Paul Peter lakes from mid-May to early September for the years 2018 and 2019. Inorganic nitrogen and phosphorus were added to Peter in 2019, while Paul Lake was an unfertilized reference.
1:10,000 Vegetation, Niwot Ridge LTER Project Area, Colorado
Vegetation coverage digitized from 1:10,000 map. Vegetation classification follows Komarkova's (1979) classification system (Braun-Blanquet) units. See Peddle and Duguay (1995) for additional information on digitization and map production for this vegetation coverage. See Braun-Blanquet (1964), Komarkova and Webber (1978), and Komarkova (1979, 1980) for additional information on vegetation classification used in this dataset.
Landmarks, Niwot Ridge LTER Project Area, Colorado
Point shapefile of commonly referenced locations at Niwot Ridge, Green Lakes Valley, and surrounding area.
Water Balance Modeling Project at the Sevilleta National Wildlife Refuge, New Mexico: Vegetation Plot Data (1995-1998)
The water balance vegetation plots were part of a larger water balance monitoring project at the Sevilleta LTER. The plots were designed to measure the percent cover of photosynthetic/transpiring (green) plant species at specific sites where time domain reflectometry (TDR) probes and weather stations were already installed. In 1995, there were three sites (Field Station, Deep Well and Rio Salado). A 30m x 30m plot was installed at each site, and collection of vegetation data commenced in July 1995. Percent cover (green) and species identities were recorded monthly at a representative sample of 1m square quadrats within each plot.
PROTECT project RAW inertial data for pedestrian inertial localisation (ORDP initiative)
<p><strong>Content</strong></p> <p>Inertial Measurement Unit raw data in TXT open and readable format, to be used for processing and testing the pedestrian dead reckoning algorithms by the inertial and indoor tracking scientific community.</p> <p>The raw inertial data have been collected and made publicly available in the frame of the SME Phase 2 project PROTECT (820867), co-funded by the European Commission</p> <p> </p> <p><strong>Experimental data</strong></p> <p>The publicly shared archive contains the following, distinct datasets:</p> <ul> <li>RawData_5_2_20191113_180029.decod: collected in Rome (Italy) with the DUNE foot-mounted sensor unit n. 5 on November 13, 2019, in the frame of the project WP3 activities (system scale-up design); aprox. Duration, 30 mins.</li> <li>RawData_6_1_20191112_170005.decod; collected in Roma (Italy)with the DUNE foot-mounted sensor unit n. 6 on November 12, 2019, in the frame of the WP3 activities (system scale-up design), aprox. Duration, 30 mins.</li> <li>RawData_008_01_20191130_162759.decod: collected in Beijing (China) with the DUNE foot-mounted sensor unit n. 8 on November 30, 2019, in the frame of the WP4 activities (demonstration), aprox. Duration, 30 mins.</li> <li>RawData_008_02_20191201_125122.decod: collected in Beijing (China) with the DUNE foot-mounted sensor unit n. 8 on December 1<sup>st</sup>, 2019, in the frame of the WP4 activities (demonstration), aprox. Duration, 30 mins.</li> <li>RawData_008_03_20191201_153053.decod: collected in Beijing (China) with the DUNE foot-mounted sensor unit n. 8 on December 1<sup>st</sup>, 2019, in the frame of the WP4 activities (demonstration), aprox. Duration, 30 mins.</li> <li>RawData_008_06_20191120_182949.decod: collected in Roma (Italy)with the DUNE foot-mounted sensor unit n. 8 on November 20, 2019, in the frame of the WP3 activities (system scale-up design), aprox. Duration, 30 mins. The experiment has a mix of walk and fast run.</li> </ul> <p> </p> <p><strong>Open and Accessible Data format</strong></p> <p>The data format is the following</p> <p>gyro(x) gyro(y) gyro(z) acc(x) acc(y) acc(z) mag(x) mag(y) mag(z) temperature altitude</p> <p> </p> <p>x, y, z indicate the axes of the Inertial Measurement Unit</p> <p>gyro stands for the angular velocity and is in rad/s</p> <p>acc stands for the acceleration and is in m/s^2</p> <p>mag is the magnetic field and is in milligauss</p> <p>temperature is in °C</p> <p>altitude is the output of the altimeter and is expressed in meters</p> <p>All the samples, in all dataset have been recorded with a 200 Hz sampling frequency.</p>
Dataset for the study of Potential Code Borrowing and License Violations in Java Projects on GitHub
<p>This is the dataset for the study of Potential Code Borrowing and License Violations in Java Projects on GitHub. The dataset is based on the Public Git Archive and consists of projects on GitHub that have at least 50 stars and have at least one line in Java. A total of 23,378 projects are listed here that we downloaded for analysis on June 1st, 2019.</p>
List of licenses discovered in the study of Potential Code Borrowing and License Violations in Java Projects on GitHub
<p>This is the list of licenses discovered in the study of Potential Code Borrowing and License Violations in Java Projects on GitHub. The licenses are ranged by the amount of files that they cover, there are a total of 94 different licenses. Where possible, the names are presented as identifiers at https://spdx.org/licenses/. "GitHub" stands for no license in the file or the project.</p>
Results of the MECinFire experiment in Fed4FIRE+ EU project
<p><strong>Multi-access Edge Computing (MEC)</strong> has been proposed as the means to drastically minimize the service access latency, by bringing computational resources and services closer to the wireless network edge. Edge resources are planned to be extensively used in the 5G network deployments, as they are able to meet stiff latency demands required from services being developed around this ecosystem (e.g. AR/VR, e-Health, Industry 4.0, etc.), by providing computational capabilities where such services can be executed close to the network edge. At the same time, 5G networks redefine the operation of traditional base station units, by disaggregating them and operating part of them in the Cloud, thus creating Cloud-RANs. These Cloud-RANs can also be heterogeneous, allowing users to access the network through multiple wireless technologies (e.g. dual access through 5G-NR and LTE). In this project, we blend the novel disaggregated and heterogeneous base station architecture with the MEC concept, and develop and experiment with the deployment of the edge computing services even closer to the network edge. In MECinFIRE we developed a software prototype that allows services to be executed close or over the machines hosting the radio access services for the network access. Our experiment provides several proof-of-concept experiments that illustrate the applicability and benefits of our solution in real 5G networks. The experiment was evaluated in a real testbed environment, while measuring KPIs regarding the end-to-end user to service latency.</p> <p>This repository contains the dataset of the experimental results produced by the MECinFIRE Project within the FED4FIRE+.</p>
PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2020 Week 13
<p>These data were collected from FIDIA machine tool controller during milling operation.</p> <p> </p>
PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2020 Week 11
<p>These data were collected from FIDIA machine tool controller during milling operation.</p> <p> </p>
PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2020 Week 09
<p>These data were collected from FIDIA machine tool controller during milling operation.</p> <p> </p>
PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2019 Week 43
<p>These data were collected from FIDIA machine tool controller during milling operation.</p> <p> </p>
PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2019 Week 52
<p>These data were collected from FIDIA machine tool controller during milling operation.</p> <p> </p>
PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2019 Week 38
<p>These data were collected from FIDIA machine tool controller during milling operation.</p>
PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2019 Week 48
<p>These data were collected from FIDIA machine tool controller during milling operation.</p> <p> </p>
PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2019 Week 51
<p>These data were collected from FIDIA machine tool controller during milling operation.</p> <p> </p>
PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2020 Week 04
<p>These data were collected from FIDIA machine tool controller during milling operation.</p> <p> </p>
PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2019 Week 50
<p>These data were collected from FIDIA machine tool controller during milling operation.</p> <p> </p>
PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 12
<p>These data were collected from robot controller during solar tanks welding operation.</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.