Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
104
datasets available to search
ShareScore release 0.9.0
Dataset results
104 results for “Measurement set”
Change in marsh surface elevation measured with a Surface Elevation Table (SET) at control and fertilized plots at Spartina alterniflora, S.patens and Typha sp. marshes, Plum Island Ecosystems LTER, MA (1999-2025).
A Surface Elevation Table (SET) is used to measure changes in the elevation of the marsh surface at three long term marsh control and fertilization experimental research sites. The sites include one Typha-dominated brackish marsh, one Spartina alterniflora-dominated salt marsh, and one S. patens-dominated salt marsh. Sites are located on the Rowley and upper Parker Rivers in the Plum Island Ecosystems (PIE) LTER.
TCOM-HF : Daily global gap-free stratospheric hydrogen fluoride (HF) profile data set based on TOMCAT CTM and Occultation Measurements
<p><strong>Methodology: TOMCAT simulation is performed at T64L32 resolution for the 2000-2024 time period. Collocated hydrogen fluoride (HF) profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Initially, model-measurement differences are calculated for each zonal bins (51 height levels, 10km to 60km). Note that if enough ACE measurements are not avaliable for a particular level then data is purely based on TOMCAT simulated output field. Separate XGBoost regression models are trained for the differences between TOMCAT and measurements at each level for a given latitude bin. XGBoost model is then used to estimate error corrections for all the TOMCAT grids. TOMCAT output sampled at 1.30 pm local time at the equator. Estimated corrections for a given model grid that are added to the original TOMCAT simulated day and night time hydrogen fluoride profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values. For more details see attached presentation. Previous version use both HALOE and ACE data. Here only ACE data is used.</strong></p> <p><strong>Dataset also includes two files containing daily mean zonal mean hydrogen fluoride profiles on height (10-50 km) and pressure (300-0.1 hPa) levels:</strong></p> <p><strong>zmhf_TCOM_hlev_T2Dz_2000_2024.nc – height level data (10 to 50 km)</strong></p> <p><strong>zmhf_TCOM_plev_T2Dz_2000_2024.nc – pressure level data (300 to 0.1 hPa)</strong></p> <p><strong>Daily 3D profiles on height and pressure levels would be made available on request.</strong></p>
Improving anxiety research novel approach to reveal trait anxiety through summary measures of multiple states - raw count data set - RNAseq
<p>Raw count data of the RNAseq analysis of a project and manuscript under the title "Improving anxiety research novel approach to reveal trait anxiety through summary measures of multiple states". The header of the table includes the subject identifiers except the first column "genes". The latter column includes all assessed gene identifiers.</p>
Data set of two dual-task paradigms to measure listening effort in cochlear implant users
<p>This data set presents the data from the paper by Hendrikse, Dingemanse, & Goedegebure (2022). This study aimed to investigate the feasibility of using listening effort to measure relatively small differences in SNR, as would arise from different hearing-device settings. Listening effort was chosen, because there are indications in literature that listening effort may be more sensitive to differences between hearing-device settings than established speech intelligibility measures. Two behavioral listening effort tests were performed at two signal-to-noise ratios (SNRs) where the intelligibility was high. A sentence final word identification and recall test (SWIRT), and a sentence verification test (SVT) were compared with a group of 18 Dutch CI users. SWIRT measured the ability to recall the final words of sentences after a list of five or seven sentences was presented. The SVT measured the ability and reaction time to determine whether a sentence was true or false. Both tests were conducted in background noise at SNRs +4 dB and +8 dB above the 50% speech perception threshold. The structure of the data files is explained in the README file.</p>
PIE LTER, marsh surface marker horizon measurements at each Surface Elevation Table (SET) site, Rowley, MA.
Marker horizon depth measurements at each Surface Elevation Table (SET) site. Marker horizons are commonly used in conjunction with the SET. Marker horizons measure vertical accretion which predominantly incorporates surface processes.
PetrocShelley/Measured-solid-state-and-sub-cooled-liquid-vapour-pressures-of-nitroaromatics-using-KEMS-Data-Set: Measured-solid-state-and-sub-cooled-liquid-vapour-pressures-of-nitroaromatics-using-KEMS-Data-Set
<p>All data files for the Measured solid state and sub-cooled liquid vapour pressures of nitroaromatics using Knudsen effusion mass spectrometry by Shelley et al.</p>
Data set: Can ocean community production and respiration be determined by measuring high-frequency oxygen profiles from autonomous floats?
<p>Relevant autonomous float data for <a href="https://doi.org/10.5194/bg-17-4119-2020">Gordon et al. (2020)</a>. Following a similar structure to the Argo network's "synthetic" profile files, one file per float is produced with all relevant variables (temperature, salinity, chlorophyll, backscatter, dissolved oxygen) on a common depth and time grid. The Electro-Magnetic Autonomous Profiling Explorer (EM-APEX) floats were deployed in the northern Gulf of Mexico in May 2017 - see <a href="https://doi.org/10.1109/CWTM43797.2019.8955168">Shay et al. (2019)</a> for more information. </p> <p>The data published here contains a timestamp for each data point. Another version of this data which contains some additional variables is hosted on <a href="https://data.gulfresearchinitiative.org/data/R5.x275.281:0001">GRIIDC</a>, but does not contain a timestamp for each data point, but rather for each profile. </p>
Data set with length measurments of Machu Picchu
<p>The base of a niche was considered to be a construction level where in the past the architectural module could be applied. To check this possibility the width and the distances between niches were measured in the 3D point cloud for further cosine quantogram analysis. Thus, 11 data sets were created, each corresponding to a particular sector or distinguish part of it, from the area so-called <em>zona urbana</em> in Machu Picchu site. The size of each sample depends on the amount and size of buildings in a sector, so samples vary from 44 to 244 measurements, measured in centimetre [cm].</p>
TCOM-HCl : Daily global gap-free stratospheric hydrogen chloride profile data set based on TOMCAT CTM and Occultation Measurements
<p>Methodology: </p> <p><span>The </span><strong><span>TOMCAT simulation</span></strong><span> was conducted at a T64L32 resolution, consistent with previous work by Dhomse et al. (2021, 2022), covering the period from 2000 to 2024. These simulations utilized </span><strong><span>ERA-5 reanalysis data</span></strong><span>.</span></p> <h3><span>HCl Profile Processing and Bias Correction</span></h3> <p><strong><span>Collocated HCl profiles</span></strong><span> are organized into five distinct latitude bins:</span></p> <ul> <li> <p><strong><span>NH polar</span></strong><span>: </span><span><span><span><span><span>9</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N - </span><span><span><span><span><span>5</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N</span></p> </li> <li> <p><strong><span>NH mid-lat</span></strong><span>: </span><span><span><span><span><span>2</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N - </span><span><span><span><span><span>7</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N</span></p> </li> <li> <p><strong><span>Tropics</span></strong><span>: </span><span><span><span><span><span>4</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S - </span><span><span><span><span><span>4</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N</span></p> </li> <li> <p><strong><span>SH mid-lat</span></strong><span>: </span><span><span><span><span><span>7</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S - </span><span><span><span><span><span>2</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S</span></p> </li> <li> <p><strong><span>SH polar</span></strong><span>: </span><span><span><span><span><span>9</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S - </span><span><span><span><span><span>5</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S</span></p> </li> </ul> <p><span>Initially, </span><strong><span>differences between TOMCAT and satellite measurements</span></strong><span> (primarily ACE-FTS data) are calculated for each zonal bin across 51 height levels (ranging from </span><span><span><span><span><span>10</span><span>,</span><span><span>km</span></span></span></span></span></span><span> to </span><span><span><span><span><span>60</span><span>,</span><span><span>km</span></span></span></span></span></span><span>).</span></p> <p><strong><span>Separate XGBoost regression models</span></strong><span> are then trained for these HCl differences at each height level within a given latitude bin. These trained models are subsequently used to estimate </span><strong><span>HCl bias corrections</span></strong><span> for all daytime TOMCAT grids (9132 days), specifically sampled at 1:30 PM local time at the equator. This yields grid-specific bias corrections that are applied to the original TOMCAT profiles.</span></p> <p><strong><span>Height-resolved HCl profile data</span></strong><span> are then interpolated onto 28 standard pressure levels (from </span><span><span><span><span><span>300</span><span>,</span><span><span>hPa</span></span></span></span></span></span><span> to </span><span><span><span><span><span>0.1</span><span>,</span><span><span>hPa</span></span></span></span></span></span><span>), using pressure levels directly from the TOMCAT grids. For overlapping latitude bins, values are averaged to ensure smoother fields near boundary regions.</span></p> <h3><span>Data Files</span></h3> <p><span>The dataset includes two files containing daily mean zonal mean HCl profiles:</span></p> <ul> <li> <p><code><span>zmhcl_TCOM_hlev_T2Dz_2000-2024_V1.1.nc</span></code><span>: Contains </span><strong><span>height level data</span></strong><span> (</span><span><span><span><span><span>10</span><span>,</span><span><span>km</span></span></span></span></span></span><span> to </span><span><span><span><span><span>60</span><span>,</span><span><span>km</span></span></span></span></span></span><span>).</span></p> </li> <li> <p><code><span>zmhcl_TCOM_plev_T2Dz_2000-2024_V1.1.nc</span></code><span>: Contains </span><strong><span>pressure level data</span></strong><span> (</span><span><span><span><span><span>300</span><span>,</span><span><span>hPa</span></span></span></span></span></span><span> to </span><span><span><span><span><span>0.1</span><span>,</span><span><span>hPa</span></span></span></span></span></span><span>).</span></p> </li> </ul> <h3><span>Reference Publication</span></h3> <p><span>This methodology, incorporating only ACE-FTS data and various minor algorithmic developments, is based on the following publication:</span></p> <p><span>Dhomse, S. S. and Chipperfield, M. P.: Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets, Earth Syst. Sci. Data, 15, 5105–5120, </span><a title="null" href="https://doi.org/10.5194/essd-15-5105-2023"><span>https://doi.org/10.5194/essd-15-5105-2023</span></a><span>, 2023</span></p>
TOMCAT CTM and Occultation measurement-based Stratospheric CFC12 (TCOM-CFC12) profile data set
<p>TOMCAT CTM and Occultation measurement-based Stratospheric CFC12 (TCOM-CFC12) profile data set </p> <p>Sandip S. Dhomse </p> <p>School of Earth and Enviro, University of Leeds, Leeds, UK</p> <p>National Centre for Earth Observations, University of Leeds, Leeds, UK</p> <p> email: s.s.dhomse@leeds.ac.uk</p> <p> Methodology: TOMCAT simulation is performed at T64L32 resolution for the 2000-2024 time period. Collocated CFC12 (CF2Cl2) profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Initially, model-measurement differences are calculated for each zonal bins (51 height levels, 10km to 60km). Separate XGBoost regression models are trained for the differences between TOMCAT and measurements at each level for a given latitude bin. XGBoost model is then used to estimate error corrections for all the TOMCAT grids. Estimated corrections for a given model grid that are added to the original TOMCAT simulated daily (at 1.30 local time) CFC-12 profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values. For more details see attached presentation.</p> <p>Dataset also includes two files containing daily mean zonal mean CFC-12 profiles on height (10-60 km) and pressure (300-0.1 hPa) levels (9132 days/64 latitudes):</p> <p>zmcfc12_TCOM_hlev_T2Dz_2000-2024_V1.1.nc – height level data (10 to 60 km)</p> <p>zmcfc12_TCOM_plev_T2Dz_2000-2024_V1.1.nc – pressure level data (300 to 0.1 hPa)</p> <p>Daily 3D profiles on height and pressure levels would be made available on request. Xarrays “resample” can be used to get monthly means.</p>
Identifying the interplay between protective measures and settings on the SARS-CoV-2 transmission using a Bayesian network [Dataset]
<p>data07B.csv: dataset for the study of the SARS-CoV-2 transmission.</p> <p>CPTNetica.txt: conditional probabilities tables of each variable given through Netica once the BN obtained in R code is loaded.</p> <p>code01.R: code to learn structure and parameters of the SARS-CoV-2 BN model.</p>
TCOM-COF2: TOMCAT CTM and Occultation measurement-based Stratospheric COF2 profile data set
<p>TCOM-COF2: TOMCAT CTM and Occultation measurement-based Stratospheric TCOM-COF2 profile data set </p> <p>Sandip S. Dhomse </p> <p>School of Earth and Enviro, University of Leeds, Leeds, UK</p> <p>National Centre for Earth Observations, University of Leeds, Leeds, UK</p> <p> email: s.s.dhomse@leeds.ac.uk</p> <p> </p> <p>Methodology: TOMCAT simulation is performed at T64L32 resolution for the 2000-2023 time period. Collocated COF2 profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Initially, model-measurement differences are calculated for each zonal bins (51 height levels, 10km to 60km). Separate XGBoost regression models are trained for the differences between TOMCAT and measurements at each level for a given latitude bin. XGBoost model is then used to estimate error corrections for all the TOMCAT grids. Estimated corrections for a given model grid that are added to the original TOMCAT simulated daily (at 1.30 local time) COF2 profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values. For more details see attached presentation.</p> <p>Dataset also includes two files containing daily mean zonal mean COF2 profiles on height (10-60 km) and pressure (300-0.1 hPa) levels (8766 days/64 latitudes):</p> <p>zmcof2_TCOM_hlev_T2Dz_2000_2023.nc – height level data (10 to 60 km)</p> <p>zmcof2_TCOM_plev_T2Dz_2020_2023.nc – pressure level data (300 to 0.1 hPa)</p> <p>Daily 3D profiles on height and pressure levels would be made available on request. Xarrays “resample” can be used to get monthly means.</p>
Data sets used for: Urban runoff velocity measurement with consumer-grade surveillance cameras and surface structure image velocimetry
<p>Original videos and reference bulk velocity and water depth data sets used to develop the study: <em>Urban runoff velocity measurement with consumer-grade surveillance cameras and surface structure image velocimetry.</em></p> <p>The reference bulk velocity and water depth data sets were obtained with the Nivus OFR Radar and Nivus NivuCompact sensors, respectively.</p>
A ferrofluid-based sensor to measure bottom shear stresses under currents and waves. Data set: Ferrofluids_Opt_2018_DiDonFranceesco
<p>The experimental calibration of the system for measuring bed shear stresses under currents was carried out at the Hydraulic Laboratory of the University of Catania.</p> <p>In this experimental campaign the magnet S0805 and S0808 were used. The tests were conducted for several bottom configurations (smooth bottom; thin sand d<sub>50</sub>=0.24 mm; coarse sand d<sub>50</sub>=0.56 mm; and mixed sand 70% thin sand and 30% coarse sand). The goals of such tests were: to study the effects of the type of magnets and to carry out a preliminary analysis the ferrofluid behavior over sandy bottom.</p>
A ferrofluid-based sensor to measure bottom shear stresses under currents and waves. Data set: Ferrofluids_Opt_2017_Privitera
<p>The experimental calibration of the system for measuring bed shear stresses under currents was carried out at the Hydraulic Laboratory of the University of Catania.</p> <p>In this experimental campaign magnet type S0805 and a number of magnets equal to 2,3 and 4 were used. The tests were conducted both over a fixed bed (Perspex<sup>©</sup>) and in the presence of mobile beds. The goals of such tests were: to study of the velocity profiles for some fixed and mobile bottoms; to study the effects of the number of magnets on the ferrofluid behavior; preliminary analysis of the bed shear stress over sandy bottom.</p>
Data Set - Laboratory measurement of the wave–induced plastic particles motion: The influence of wave period, plastic size and plastic density
<p><strong>Data set - Laboratory measurement of the wave–induced plastic particles motion: The influence of wave period, plastic size and plastic density</strong></p> <p>This data set describes the wave flume experimental data on the wave-induced plastic particles motion induced by different wave conditions and different plastic particles density and size. A manuscript is currently under review describing the analysis of the data.</p> <p>The data set is divided in two parts:</p> <p>- <strong>Wave flume hydrodynamics</strong>. With measured water surface elevation at different locations within the wave flume. These data are stored in txt files with headings describing the type of measurement, i.e. wave paddle motion, water surface elevation at different sensors, synchronization signal for the video-cameras.</p> <p>- <strong>Lagrangian trajectories. </strong>hdf5 files with information of the particles position, velocity and time (with respect to the synchronization signal in the respective hydrodynamic file) for each experiment. Two tar.gx files have been uploaded with trajectories information:</p> <p> - TOPIOS_Trajectories_FloatingParticles.tar.xz, with information of floating plastic particles and,</p> <p> - TOPIOS_Trajectories_NonfloatingParticles.tar.xz, with information of non-floating plastc particles.</p> <p>An excel file with information of filenames, cross-shore locations of sensors, plastic particles and wave conditions is also uploaded (TOPIOS_Control_exp.xlsx).</p> <p>Any question regarding the data can be addressed at jose.alsina@upc.edu</p>
Data set with length measurments of niches in Chachabamba
<p>For the metrological analysis, this type of data was obtained from a 3D point cloud and all measurements for Chachabamba architectural remains were collected in two data sets.The first group belongs to the central part of the sanctuary, called sector A, and consists of 39 measurements. The second group of measurements was collected from the water fountains located in the four corners of sector A, where 31 measurements were collected.</p>
Coupled High Frequency Measurements of Swash Sediment Transport and Morphodynamic data set produced at the CIEM flume, Hydralab IV
<p>The data set here presented aims to increase the understanding of the nearshore sediment dynamics. The experiments aimed at obtaining high quality data of hydrodynamics, sediment concentration and beach-face evolution with an intra-wave time scale. The specific objectives of CoSSedM access project were i) to obtain information of the effect of the wave group periods on the beach morphological evolution; ii) To obtain detailed sediment transport information at the inner surf and swash zones with different bi-chromatic wave conditions and iii) To obtain intra-wave measurements of beach-face evolution.</p> <p>The present work was developed in the framework of the HYDRALAB IV Transnational Access projects. The experiments were carried out in the large scale wave flume CIEM at Universitat Politècnica de Catalunya (UPC), Barcelona. This is a wave flume 100 m long, 3 m wide, and 4.5 m deep. The working water depth was at around 2.5 m over the horizontal flume section and was varied slightly depending on the wave test. A beach was installed made of commercial well-sorted sand (d50 = 0.25 mm) with an overall mean beach gradient of approximately 1:15.</p> <p>Due to its size, the data set can not be placed on this repository and will be provided on demand. Please contact with the authors or with the data manager of the CIEM installation.</p> <p>More information can be found on the published papers:</p> <p>Alsina, J.M., Padilla, E.M. and Cáceres, I., 2016. Sediment transport and beach profile evolution induced by bi-chromatic wave groups with different group periods. Coastal Engineering, Vol. 114, 325-340.</p> <p>Van der Zanden, J.; Alsina, J.; Caceres, I.; Buijsrogge, R. H.; Ribberink, J. , 2015. Bed level motions and sheet flow processes in the swash zone : observations with a new conductivity-based concentration measuring technique (CCM+) . Coastal Engineering, Vol. 105, 47-65.</p>
A ferrofluid-based sensor to measure bottom shear stresses under currents and waves. Data set: VelocityProfilies_2018_Musumarra
<p>The experimental campaign was devoted to study the velocity profile inside the small scale flume for several bottom configurations. In particular, the following configurations were considered: thin sand (D<sub>50</sub>=0.25 mm); coarse sand (D<sub>50</sub>=0.56 mm); mixed sand: 10% coarse sand and 90% thin sand; mixed sand: 20% coarse sand and 80% thin sand; mixed sand: 30% coarse sand and 70% thin sand; mixed sand: 40% coarse sand and 60% thin sand; small gravel (diameter between 3 and 5 mm); gravel (diameter between 9 and 14 mm); small gravel and thin sand; gravel and thin sand.</p>
Data set for "Measuring synchronization and anticipation between individual investors from their daily performance"
<p>The data stored here is used as a support of the paper "Measuring<br> synchronization and anticipation between individual investors from their<br> daily performance" where a measure based on Mutual Information and<br> Transfer of Entropy is used in order to map investors' behaviour and which<br> ones are following same behavioural patterns.</p> <p>The study linked to this data is published on pre-print Arxiv.org<br> with the following citation:</p> <p><br> Mario Gutiérrez-Roig, Javier Borge-Holthoeffer, Alex Arenas and<br> Josep Perelló. Measuring synchronization and anticipation between<br> individual investors from their daily performance (2018)</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.