Find research datasets worth reusing

opencc-by-4.0Jan 2021View details →

Extreme Precipitation Potential and Slow-moving Extreme Precipitation Potential

Extreme Precipitation Potential (EPP) and Slow-moving Extreme Precipitation Potential (SEPP) are described in Kahraman et al. paper "Quasi-stationary intense rainstorms spread across Europe under climate change".   File names as "identifier+YYYY+MM+.nc".   EPP count per month for current (identifier=hvpraj) and future (identifier=hvprak) climate. SEPP count per month for current (identifier=hvprslowaj) and future (identifier=hvprslowak) climate. YYYY=simulation year MM=simulation month ".nc"=netcdf extension

opengpl-3.0May 2025View details →

GCMMA-MMA-Python: Python implementation of the Method of Moving Asymptotes

This record contains the Python implementation of the Method of Moving Asymptotes (MMA), originally developed and written in MATLAB by Krister Svanberg. The MMA algorithm is used for solving non-linear programming problems. Users of this code are encouraged to inform Krister Svanberg of their application and intentions via email, as provided on his website. When publishing work that uses this code, please cite Krister Svanberg's original academic work.

opencc-by-4.0Sep 2022View details →

Dataset of "Moving hands feel stimuli before stationary hands"

In the flash lag effect (FLE), a moving object is seen to be ahead of a brief flash that is presented at the same spatial location; a haptic analogue of the FLE has also been observed. Some accounts of the FLE relate the effect to temporal delays in the processing of the stationary stimulus as compared to that of the moving stimulus [3–5]. We tested for movement-related processing effects in haptics. People judged the temporal order of two vibrotactile stimuli at the two hands: One hand was stationary, the other hand was executing a fast, medium, or slow hand movement. Stimuli at the moving hand had to be presented around 36 ms later, to be perceived to be simultaneous with stimuli at the stationary hand. In a control condition, where both hands were stationary, perceived simultaneity corresponded to physical simultaneity. We conclude that the processing of haptic stimuli at moving hands is accelerated as compared to stationary ones–in line with assumptions derived from the FLE. The dataset contains individual points of subjective simultaneity and just noticeable differences for each movement condition (stationary, fast , medium, slow) and individual response frequencies as a function of stimulus onset asynchrony (SOA) and movement condition.

opencc-by-4.0Jun 2018View details →

Network Measurements while Uploading 5.6 KB Files from Moving Buses to Cellular Networks in Varmland, Sweden.

The dataset and the collection methodology are described and used in the following papers: <ul> <li>Ben Abdesslem, Fehmi, Henrik Abrahamsson, and Bengt Ahlgren. "Measuring Mobile Network Multi-Access for Time-Critical C-ITS Applications"  Network Traffic Measurement and Analysis Conference (TMA'18), Vienna, Austria (2018).</li> <li>Ben Abdesslem, Fehmi, Henrik Abrahamsson, and Bengt Ahlgren. "Cellular Network Multi-Access Measurements on the Roads of Värmland, Sweden."  arXiv preprint arXiv:1805.06814 (2018).</li> <li>Henrik Abrahamsson, Ben Abdesslem, Fehmi, Bengt Ahlgren, Anna Brunstrom, Ian Marsh and Mats Björkman. "Connected Vehicles in Cellular Networks: Multi-access versus Single-access Performance"  2nd Workshop on Mobile Network Measurement (MNM’18), Vienna, Austria (2018).</li> </ul> The CSV file has the following columns: <ul> <li>Index: Unique number for the transaction</li> <li>Timestamp: Time and date of the transaction</li> <li>Interface: Interface used by the transaction [op0, op1 or op2]</li> <li>TransactionTime: Duration of the transaction (in sec)</li> <li>Status: Result of the transaction [failed, senderror, timeout, or number of received bytes acknowledged]</li> <li>GpsTimestamp: Time and date of the GPS coordinates</li> <li>GpsLatitude: Last GPS latitude known</li> <li>GpsLongitude: Last GPS longitude known</li> <li>ModemTimestamp: Time and date of the modem properties</li> <li>ModemOperator: Name of the operator [op0, op1, op2]. The original names (Telia, Telenor, 3) have been replaced in a different order.</li> <li>ModemRSSI: RSSI (in dBm)</li> <li>ModemCID: Cell ID</li> <li>ModemDeviceMode: <ul> <li>UNKNOWN (0).</li> <li>DISCONNECTED (1).</li> <li>NO_SERVICE (2).</li> <li>2G (3).</li> <li>3G (4).</li> <li>LTE (5).</li> </ul> </li> <li>ModemDeviceSubmode:  <ul> <li>UNKNOWN (0).</li> <li>UMTS (1).</li> <li>WCDMA (2).</li> <li>EVDO (3).</li> <li>HSPA (4).</li> <li>HSPA+ (5).</li> <li>DC HSPA (6).</li> <li>DC HSPA+ (7).</li> <li>HSDPA (8).</li> <li>HSUPA (9).</li> <li>HSDPA+HSUPA (10).</li> <li>HSDPA+ (11).</li> <li>HSDPA+HSUPA (12).</li> <li>DC HSDPA+ (13).</li> <li>DC HSDPA + HSUPA (14).</li> </ul> </li> <li>ModemLAC: Location Area Code</li> <li>ModemRSRP: RSRP (in dBm)</li> <li>ModemFrequency: Frequency in Mhz</li> <li>ModemRSRQ: RSRQ  (in dBm)</li> <li>ModemBand: LTE band</li> <li>ModemPCI: LTE Physical Cell ID</li> <li>ModemECIO: Ec/Io</li> <li>ModemENODEBID: eNodeB ID</li> <li>ModemRSCP: RSCP (in dBm)</li> <li>bus: Bus number (head node number in Monroe)</li> <li>country: Country of operation [Sweden]</li> <li>protocol: protocol used [UDP, TCP or HTTPS]</li> <li>experiment: Experiment ID (one hour experiments)</li> <li>diff: Max time difference between the three simultaneous uploads (in ms)</li> <li>TransactionTime200: Transaction duration if timeout=200ms</li> <li>TransactionTime1000:Transaction duration if timeout=1000ms</li> <li>TransactionTime6000: Transaction duration if timeout=6000ms</li> <li>bestAvailability: Best availability over the whole experiment ID (%)</li> <li>bestAvailability200: Best availability over the whole experiment ID (%) if timeout=200ms</li> <li>bestAvailability1000: Best availability over the whole experiment ID (%) if timeout=1000ms</li> <li>best: Best duration (in sec)</li> <li>best1000: Best duration (in sec) if timeout=1000ms</li> <li>availability: Availability over the whole experiment ID (%)</li> <li>availability200: Availability over the whole experiment ID (%) if timeout=200ms</li> <li>availability1000: Availability over the whole experiment ID (%) if timeout=1000ms</li> <li>DayOfWeek: Day of the Week [Monday, ..., Sunday]</li> </ul>

opencc-by-4.0Nov 2023View details →

Auralization of virtual microphone array sensors considering coherence loss by atmospheric turbulence for two moving monopole sources

Open the record for dataset details and reuse information.

opencc-by-4.0Mar 2024View details →

Figures datasets for "Wave momentum shaping for moving objects in heterogeneous and dynamic media"

Source data for Figures used in the manuscript "Wave momentum shaping for moving objects in heterogeneous and dynamic media".

opencc-by-4.0Jan 2022View details →

How does moving Public Engagement with Research Online Change Audience Diversity? Comparing Inclusion Indicators for 2019 & 2020 European Researchers' Night events

Taking place annually in more than 400 cities, European Researchers’ Night is a pan- European synchronized event that aims to bring researchers closer to the public. In this paper audience profiles are compared from events in 2019 and 2020. In 2019, face-to-face events reached an estimated 1.6 million attendees, while in 2020, events shifted online due to the COVID-19 pandemic and reached an estimated 2.3 million attendees. Focusing on social inclusion metrics, survey data is analyzed across two national contexts (Ireland and Malta) in 2019 (n=656) and 2020 (n=506). The results from this exploratory, descriptive study shed light on how moving public engagement with research online shifted audience profiles. Based on prior research about the digital divide in access and use of online media, hypotheses were proposed that online European Researchers’ Night events would attract audiences with higher educational attainment levels and greater self-reported, subjective economic well-being. While changes were observed from 2019 to 2020, results for each hypothesis show a mixed picture. The first hypothesis was upheld for the highest education levels but failed for the lowest levels suggesting that the pivot to online events simultaneously attracted participants with no formal education and those with postgraduate qualifications, while attracting less of those with undergraduate or lower levels of education. The second hypothesis was not upheld, with online European Researchers’ Night events attracting audiences with slightly higher levels of economic well-being compared to face-to-face events. The findings of this study indicate that European Researchers’ Night events present a clear opportunity to measure the effects of the digital divide in relation to public engagement with research across Europe.

opencc-by-4.0Apr 2019View details →

Moving window optimization IHTP

bulk.txt - bulk temperature at times given in time.txt hflux.txt - calculated values of heat flux on rocket skin hflux0.txt - initial (guess) values of heat flux on rocket skin movingwindow.py - Python 2.7 script performing moving window optimization solving Inverse Heat Transfer Problem rx.mac - Ansys APDL script solving Direct Heat Transfer Problem temp_bulkhead.csv - raw temperature data temperatura.txt - calculated values of temperature from Ansys time.txt - time stamps for heat flux

opencc-by-4.0Apr 2019View details →

Initial evaluation of the MOVING platform

This resource contains information about the laboratory study carried out as an initial evaluation of the MOVING platform. It contains the information gathered from the questionnaires, and qualitative notes taken during the study for two different use cases. The design of the study and the results of the analysis can be found in the deliverable 1.3 "Initial evaluation, updated requirements and specifications" <a href="http://moving-project.eu/deliverables/">http://moving-project.eu/deliverables/</a>

opencc-by-4.0Mar 2024View details →

Data and supplementary files of the publication: "What Goes Around Should Not Move Around: Immobilizing Microplastics as a New Approach for Analytical Ring Trials"

Accompanying materials for the publication: <a href="https://doi.org/10.1021/acs.est.4c09427">https://doi.org/10.1021/acs.est.4c09427</a>

opencc-by-4.0Oct 2023View details →

Data supporting Comparison of feeding niches between Arctic and northward moving sub-Arctic marine mammals in Greenland

Data supporting the paper: <blockquote> Land-Miller, H., A. Roos, M. Simon, R. Dietz, C. Sonne, S. Pedro, A. Rosing-Asvid, F. Rigét, and M. McKinney. 2023. Comparison of feeding niches between Arctic and northward moving sub-Arctic marine mammals in Greenland. Marine Ecology Progress Series. </blockquote> This data is in five files: 1. greenland_marmam_metadata.csv contains metadata for all samples used in this project, including sample identifiers: <ul> <li>Sample: unique sample ID per individual animal</li> <li>Species</li> </ul> and details of collection, including Year, Location (general area), Lat, Long, and Date. It also includes other data on the animal (Sex, Age, Length), when available, as well as the co-author who provided the sample to the project (Sample sender) and the tissues available/analyzed for each individual (Tissues received). 2. all_sample_locations.csv includes latitude/longitude of each sample for mapping. Latitude and longitude are consistent with the full metadata file when coordinates were available, and estimated based on general sampling area (Location or Area) when not. The variable estimate denotes samples for which coordinates were estimated. 3. fatty_acids_greenland_marmams.csv contains fatty acid data for all samples. Variables 8:00 to 24:1n9 represent the proportion of each individual fatty acid, out of total fatty acids in that sample. Data are represented as whole number percents (i.e., 10 = 10% and all fatty acids sum to 100 for each sample).  4. CNS_greenland_McGill.csv contains bulk stable isotope data for all samples analyzed at McGill. In addition to Sample and Species, this includes: <ul> <li>treatment: whether a sample was lipid-extracted (LE) or non-lipid-extracted (nLE) prior to analysis</li> <li>d15N: stable isotope ratio δ15N</li> <li>d13C: stable isotope ratio δ13C</li> <li>d34S: stable isotope ratio δ34S</li> <li>perc.C: mass percent of carbon in the sample</li> <li>perc.N: mass percent of nitrogen in the sample</li> <li>perc.S: mass percent of sulfur in the sample</li> <li>C.N.ratio: mass ratio of carbon to nitrogen in the sample </li> </ul> 5. CN_greenland_nLE_copenhagen.csv contains stable isotope data for non-lipid-extracted samples analyzed at the University of Copenhagen for δ13C and δ15N. Variables treatment, d13C, and d15N are consistent with CNS_greenland_McGill.csv.

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Year 2007, meteorological data, 15 minute intervals, from weather station located at the Governor's Academy, Byfield, MA then moved to MBL Marshview Farm, Newbury, MA.

Year 2007 meteorological measurements at Governor's Academy and MBL Marshview Farm of air temperature, humidity, precipitation, solar radiation, photosynthetically active radiation (PAR), wind speed and direction and barometric pressure. Sensors conduct measurements every 5 secs and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.

openCustomJan 2020View details →

opencc-by-4.0Jan 2020View details →

Images of Slow Moving Landslide in Panajachel Guatemala

Images taken with a DJI Phantom 4 Pro in two different flight modes, safe and continuous, divided into two different .rar files. Included are the coordinates for the 4 ground control points, marked with white calc x's in the images, as well as the orthomosaic created with the images in Agisoft Metashape. The site is next to the highway 1 between Panajachel and San Andres Semetabaj in Solola Guatemala. Coordinate system used is UTM Zone 15N based on WGS84. The coordinates of points 2 - 5 are the four GCPs in the study area, with 5 being the take off and landing site. Point 1 represents the reference point approximately 1 km away, which was a previously placed permanent GCP. All points include the elevation ASL.

opencc-by-4.0Jun 2021View details →

4DMRI moving lung meshes

Moving lung meshes extracted from 4DMRIs The newest version of the dataset and the documentation can be found on <a href="http://gitlab.psi.ch/duetschler_a/4DMRI_moving_lung_meshes">https://gitlab.psi.ch/duetschler_a/4DMRI_moving_lung_meshes</a>. The moving lung meshes can for example be used to generate synthetic 4DCT(MRI)s. The code for generating 4DCT(MRI)s using a reference CT and the moving lung mesh data can be found on <a href="http://gitlab.psi.ch/duetschler_a/4DMRI_moving_lung_meshes">https://gitlab.psi.ch/duetschler_a/4DCT-MRI</a>. Please cite the following publication when using the data: Duetschler, A., Bauman, G., Bieri, O., Cattin, P.C., Ehrbar, S., Engin-Deniz, G., Giger, A., Josipovic, M., Jud, C., Krieger, M., Nguyen, D., Persson, G.F., Salomir, R., Weber, D.C., Lomax, A.J. and Zhang, Y. (2022), Synthetic 4DCT(MRI) lung phantom generation for 4D radiotherapy and image guidance investigations. Med. Phys.. <a href="https://doi.org/10.1002/mp.15591">https://doi.org/10.1002/mp.15591</a>

openmit-licenseOct 2014View details →

Replication Data for: Continuously moving table MRI with golden angle radial sampling

Continuously moving table (CMT) MRI is a high throughput technique that has multiple applications in whole-body imaging. In this work, CMT MRI based on golden angle (GA, 111.246° azimuthal step) radial sampling is developed at 3 Tesla (T), with the goal of increased flexibility in image reconstruction using arbitrary profile groupings.

opencc-by-4.0Jul 2024View details →

MOVING: a Multi-MOdal dataset of EEG signals and VIrtual Glove hand trackING

A new Multi-modal dataset comprising neural EEG signals and kinematic data associated with three hand movements — open/close, finger tapping, and wrist rotation — along with a rest period. The dataset, obtained from eleven subjects using a 32-channel dry wireless EEG system, also includes synchronized kinematic data captured by a Virtual Glove (VG) system equipped with two orthogonal Leap Motion Controllers. The use of these two devices allows for fast assembly (~ 1 minute) while introducing more noise than the gold standard devices for data acquisition. The data set, obtained from 11 subjects using a 32-channel dry wireless EEG system, also includes synchronized kinematic data captured by a Virtual Glove (VG) system equipped with two orthogonal Leap Motion Controllers.  For citation please refer to the paper: Mattei, E.; Lozzi, D.; Di Matteo, A.; Cipriani, A.; Manes, C.; Placidi, G. MOVING: A Multi-Modal Dataset of EEG Signals and Virtual Glove Hand Tracking. Sensors 2024,24, 5207.  https://doi.org/10.3390/s24165207  References: Placidi, Giuseppe. "A smart virtual glove for the hand telerehabilitation." Computers in Biology and Medicine 37.8 (2007): 1100-1107. Placidi, Giuseppe, et al. "Measurements by a LEAP-based virtual glove for the hand rehabilitation." Sensors 18.3 (2018): 834. Placidi, Giuseppe, et al. "Patient–therapist cooperative hand telerehabilitation through a novel framework involving the virtual glove system." Sensors 23.7 (2023): 3463.

opencc-zeroMay 2022View details →

Moving vessel profiler (MVP) transects from PolarFront 2022-05 cruise

MVP transects 2022-05 The Moving Vessel Profiler (MVP) was deployed to collect data with high-spatial resolution along transects aiming to cross the front. Instruments contained in the profiling unit ("fish") of the winch were a CTD (AML µCTD), a fluorescence sensor (WETLABS) and a laser optical plankton counter (LOPC). Together the instruments yield data on the physical environment, phytoplankton, zooplankton and other particle distributions. Weather conditions during the cruise allowed sampling along two transects (S1 and parts of S2). However, S1 did not cross the polar front due to too thick ice cover in the North. The south (waves to high) and north (ice) of transect S2 were sampled by a different platform, data elsewhere, together resulting in a complete transect crossing the front. Coverage Transect S1 start: 75 N, 29 30' E, 20 May at 06:57 UTC Transect S1 end: 75 55' N, 29 54' E, 20 May at 16:16 UTC Transect S2 start: 75 24' N, 29 E, 23 May at 16:03 UTC Transect S2 end: 76 43' N, 29 30' E, 24 May at 03:43 UTC S1: 2022-05-20T06:57Z/2022-05-20T06:57Z S2: 2022-05-23T16:16Z/2022-05-24T03:43Z

opencc-by-4.0Feb 2024View details →

ERA5 overviews complementing temperature measurements of ground-based Rayleigh lidars for the investigation of gravity waves generated by moving sources

ERA5 overviews to associate stratospheric gravity waves in temperature measurements from vertically staring (zenith-pointing) ground-based Rayleigh lidars with atmospheric processes. Animations are for a virtual lidar location over the Southern Ocean during research flight RF25 of the DEEPWAVE campaign (July 17 to 19, 2014) and for the location of the COmpact Rayleigh Autonomous Lidar (CORAL) in the lee of the southern Andes. Here, the first overview is for the CORAL measurement from June 22 to 23, 2018. The second one is for the nightly measurements between August 7 and 9, 2020. (a) and (b) emulate the measurement of a vertically staring ground-based lidar and show temperature perturbations after subtracting a temporal running mean of 12h (a) and the mean absolute temperature profile (b). Panels (c) and (d) are vertical sections of stratospheric 𝑇′ along sectors of the latitude circle (c) and meridian (d) of the virtual lidar location. (e) and (f) are corresponding vertical sections of thermal stability 𝑁2 (10−4 s−2, color-coded), potential temperature (K, thin grey lines), and potential vorticity (1, 2, 4 PVU: black, 2 PVU: green). Thin black lines in the vertical sections are zonal (d, f) and meridional (c, e) wind components (solid: positive, dashed: negative). Panel (g) is a horizontal section of the height of the 2 PVU surface (km, color-coded), geopotential height (m, solid lines) and wind barbs at the 850 hPa level. The black vertical line in (a) marks the time for (c)-(g) and dashed lines in (c)-(g) highlight the location of the virtual lidar and profiles in (a) and (b). The provided NETCDF files contain the corresponding CORAL temperature measurements for the two periods with CORAL measurements in 2018 and 2020.

opencc-by-4.0Dec 2024View details →

AI-Based Tracking of Fast-Moving Alpine Landforms Using High Frequency Monoscopic Time-Lapse Imagery

This repository contains data and scripts used in the study titled 'AI-Based Tracking of Fast-Moving Alpine Landforms Using High Frequency Monoscopic Time-Lapse Imagery' published as a <a href="https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2570/" target="_blank" rel="noopener">preprint </a>in Earth Surface Dynamcis (EGU) . Please check the README.docx for folder structure with descriptions of each folder and file.