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.
396
datasets available to search
ShareScore release 0.7.1
Dataset results
396 results for “Humidity”
Air Temperature and Relative Humidity at Long Term Tree Growth Sites; 1989-Present: Hourly
This dataset contains the hourly output for Air Temperature and Relative Humidity sensors located at various Long Term Tree Growth Plots (LTTG).
Air Temperature and Relative Humidity at Long Term Tree Growth Sites; 2001-Present: Hourly
This dataset contains the hourly output for Air Temperature and Relative Humidity sensors located at various Long Term Tree Growth Plots (LTTG). In addition to expanding the number of sites being monitored, some sites contain Omnidata Easy Loggers in which ES-110 sensors used to measure AT/RH have exceeded their lifespan . This dataset is to expand on and replace those older sensors to ensure a continueing dataset.
Soil moisture, temperature and relative humidity for subalpine forest permanent plots, 2017 - 2021.
We collected microclimate data for 12 permanent forest plots in subalpine forests and at alpine treeline at Niwot Ridge, Colorado, USA. We collected soil temperature (2015-2020), air temperature (2015-2020), air relative humidity (2015-2020), and soil moisture data (2015-2019). Soil temperature, air temperature, and air relative humidity data were collected year-round, but they are not necessarily continuous during the sample period due to instrument failure. The goal of winter soil temperature data collection was to estimate snow duration. Soil moisture was collected every two weeks from 2015-2019 and continuously from June to October in three sites in 2018 and 2019. See data for periods of sampling and methods for details on sampling interval and instrumentation.
Dataset for "Toward closure between predicted and observed particle viscosity over a wide range temperature and relative humidity"
<p>Raw data and processing scripts associated with the paper "Toward closure between predicted and observed particle viscosity over a wide range temperature and relative humidity". Please see README.md for details. </p>
Vertical profiles of air temperature, relative humidity, wind speed and direction observed using UAV over the Mukhrino peatland in June 2022
<p>Vertical profiles of air temperature and relative humidity were measured using the iMetXQ2 sensor onboard DJI Phantom 4 quad-copter; vertical profiles of wind speed and direction were obtained from the Phantom 4 flight logs as produced by the DJI proprietary algorithm. </p>
Temperature and Relative Humidity monitoring
<p>Temperature and relative humidity has been monitored before and after the installation of nature-based solutions during the proGIreg project. Specifically, NBS3 in Dortmund, NBS2, NBS3 and NBS5 in Turin, and NBS5 in Zagreb have been monitored. Continuous measurements have been made inside the NBS and in a control site over three years; for each monitoring site, 6 temperature sensors are used (3 for the NBS site and 3 for the control site).<br>For NBS5 outdoor green walls in Turin and Zagreb, the temperature has been measured inside the building and only for one year, and the temperature measured by a reference weather station in the same district has been used as control.</p> <p>More details are reported in Baldacchini, C. (2019): Monitoring and Assessment Plan, Deliverable No. 4.1, proGIreg. Horizon 2020 Grant Agreement No 776528, European Commission, 124.</p>
Neural Network predictions and ERA5 reference of integrated water vapour, and temperature and specific humidity profiles based on simulated microwave radiometer observations
<p>This data set contains predictions of the Neural Network retrievals described in <strong>[1]</strong>, where simulated microwave radiometer observations (brightness temperatures, TBs) from the evaluation data subset of <strong>[2]</strong> (years 2001, 2006, 2011, 2015) were used as input to the Neural Network. As described in Section 3.2 of <strong>[1]</strong>, we trained an ensemble of 20 Neural Networks for each retrieved atmospheric quantity and applied them to the ERA5 evaluation data set to estimate the robustness of the retrievals with respect to random perturbations. The following atmospheric quantities were retrieved: </p> <ul> <li>temperature profile (variable name 'temp_p', filename suffix 'temp_test_417'),</li> <li>boundary layer temperature profile (variable name 'temp_p', filename suffix 'temp_test_424'),</li> <li>specific humidity profile (variable name 'q_p', filename suffix 'q_test_472'),</li> <li>integrated water vapour (variable name 'iwv_p', filename suffix 'iwv_test_126')</li> </ul> <p>The cryptic 3-digit filename suffixes represent different settings of the Neural Network retrieval. More information can be found in <strong>[3]</strong>. Variables that do not have the "_p" suffix are ERA5 data and used as reference to estimate errors of the retrievals by comparing them with the predictions. The dimension 'n_s' represents the ERA5 data sample number while the dimension 'n_rand' designates the ensemble of Neural Networks.</p> <p>These files can be created when running run_NN_retrieval (contained in NN_retrieval.py, see <strong>[3]</strong>) with exec_type='20_runs' and eval_mode=True and test_id either "126", "417", "424" or "472". However, as this might take some hours, we provide them here.</p> <p> </p> <p><strong>[1]:</strong> Walbröl, A., Griesche, H. J., Mech, M., Crewell, S., and Ebell, K.: Combining low- and high-frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products, Atmospheric Measurement Techniques, 17, 6223-6245, https://doi.org/10.5194/amt-17-6223-2024, 2024.</p> <p><strong>[2]:</strong> Walbröl, A., and Mech, M.: ERA5 based training, validation and evaluation data for retrievals combining 22-58 GHz with 175-340 GHz microwave radiometer measurements during MOSAiC (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.10997365, 2024.</p> <p><strong>[3]: </strong>Walbröl, A.: Codes for: Combining low and high frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products (1.0.1). Zenodo. <a href="https://doi.org/10.5281/zenodo.11123136" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11123136</a>, 2024.</p>
Dataset from: "Global labor loss due to humid heat exposure underestimated for outdoor workers"
<p>Data from Environmental Research Letters manuscript 'Global labor loss due to humid heat exposure underestimated for outdoor workers', DOI: https://doi.org/10.1088/1748-9326/ac3dae.</p> <p>Associated Python (Jupyter Lab) scripts and working environment to load and plot data can be found at: https://github.com/LukeAParsons/erfs_comparison</p> <p>Abstract:</p> <p>'Humid heat impacts a large portion of the world’s population that works outdoors. Previous studies have quantified humid heat impacts on labor productivity by relying on exposure response functions that are based on uncontrolled experiments under a limited range of heat and humidity. Here we use the latest empirical model, based on a wider range of temperatures and humidity, for studying the impact of humid heat and recent climate change on labor productivity. We show that globally, humid heat may currently be associated with over 650 billion hours of annual lost labor (148 million full time equivalent jobs lost), 400 billion hours more than previous estimates. These differences in labor loss estimates are comparable to losses caused by the COVID-19 pandemic. Globally, annual heat-induced labor productivity losses are estimated at 2.1 trillion in 2017 PPP$, and in several countries are equivalent to more than 10% of gross domestic product. Over the last four decades, global heat-related labor losses increased by at least 9% (>60 billion hours annually using the new empirical model) highlighting that relatively small changes in climate (<0.5 ◦C) can have large impacts on global labor and the economy.'</p>
Streamer propagation in humid air
<p>This dataset includes the input and output files for the paper: Streamer propagation in humid air.</p> <p><strong>Input files</strong>:</p> <p># <em>Plasma-chemistry and transport coefficients</em></p> <p>chemistry_files/*.txt</p> <p># <em>Configuration files</em></p> <p>config_files/*.cfg</p> <p># <em>Initial conditions (densities)</em></p> <p>config_files/m_user.f90</p> <p><strong>Output files (output_files)</strong>:</p> <p>*.silo</p> <p>*.txt</p> <p>Output data generated with the software afivo-streamer (https://gitlab.com/MD-CWI-NL/afivo-streamer) corresponding to the commit 1ff2676ba48a5eb568f06c7b11a548629a5ff20c</p>
Streamer propagation in humid air
<p>This dataset includes the input and output files for the paper: Streamer propagation in humid air.</p> <p><strong>Input files</strong>:</p> <p># <em>Plasma-chemistry and transport coefficients</em></p> <p>chemistry_files/*.txt</p> <p># <em>Configuration files</em></p> <p>config_files/*.cfg</p> <p># <em>Initial conditions (densities)</em></p> <p>config_files/m_user.f90</p> <p><strong>Output files (output_files)</strong>:</p> <p>*.silo</p> <p>*.txt</p> <p>Output data generated with the software afivo-streamer (https://gitlab.com/MD-CWI-NL/afivo-streamer) corresponding to the commit 1ff2676ba48a5eb568f06c7b11a548629a5ff20c</p> <p> </p>
Frictional Properties of Opalinus Clay: Influence of Humidity, Normal Stress and Grain-size on Frictional Stability
<p>We designed frictional experiments to characterize the effect exerted by humidity, grain size and normal stress on frictional behaviour of the Opalinus clay fault gouge. We explored a wide range of normal stresses, ranging from 5 to 70 MPa performing velocity up-steps from 1 to 300 μm/s and slide-hold-slide from 1 to 3000s. Our experiments confirms that the OPA clay is weak, with friction coefficients at steady-state of ~0.35 and ~0.41, for 100% RH and 25% RH experiments, respectively. The OPA clay is velocity strengthening over the entire range of applied normal stress. We observe a direct relationship between frictional parameter <em>(a-b)</em> and slip velocity up to 35 MPa where, from there on, <em>(a-b)</em> parameter seems to be velocity independent. As evidenced by the microstructural analysis, we suggest that this behaviour is due to the progressive transition with normal stress, from strain localization and grain size reduction to distributed deformation on well-developed phyllosilicate networks. The amount of relative humidity does not affect deformation mechanisms (i.e. localized or distributed), whereas decreases fault strength and increases fault stability. We hypothesize that this is due to a possible interplay of OPA clay swelling and lubrication, caused by the weakening of chemical bonds between phyllosilicate foliae. Notably, the initial grain size (< 63 µm or 63 < g.s. < 125 µm) does not affect either the frictional strength or stability, with similar values of dilation upon velocity up-step. Collectively, our mechanical and microstructural observations have allowed us to build a conceptual model that summarizes the main mechanical features of the OPA clay fault gouge. In the context of deep geological repositories (DGR), our results confirm that slow aseismic slip is the most likely slip behaviour for a fault gouge hosted in the OPA clay, with similar mineralogical composition and clay fabric as our samples. Beyond the context of deep geological repositories, this study has also implications for carbon capture and geological storage in the deep subsurface. Indeed, OPA has the characteristics of a low permeability caprock, but faulted, and the integrity of a sealing caprock overlying a storage reservoir can evolve after fault reactivation, potentially generating undesired seismicity and new hydraulic pathways.</p> <p>The data are uploaded are structured as follow:</p> <p>1) A .txt file of the datafile that is recorded from the machine (raw data)</p> <p>2) A file in .txt format containing the elaborated data (data_rp) </p> <p>The data are analyzed using rawPy that can be found at <a href="https://github.com/marcoscuderi/rawPy">https://github.com/marcoscuderi/rawPy</a></p> <p>For any additional information please do not hesitate to contact the corresponding author Nico Bigaroni at nico.bigaroni@uniroma1.it</p> <p> </p>
Impacts of pressure, temperature, and rel. humidity on NDIR CO2 sensors (DATA)
<p>These files support the experiments demonstrated in the peer-reviewed article "Low-complexity methods to mitigate the impact of environmental variables on low-cost UAS-based atmospheric carbon dioxide measurements", available via open access at the European Geophysical Union's Atmospheric Measurements Techniques journal.</p>
Text-fig. 7. Stereomicroscope microphotographs of plant remains sieved out of a sediment bulk sample (C3X) from bed GLA10 of Govone. a: Tetraclinis salicornioides (UNGER) KVAČEK, shoot fragment, MGPT-PU141083). b: Toddalia latisiliquata (R.LUDW.) H.-J. GREGOR, seed, MGPT-PU141084. c: Toddalia rhenana H.-J.GREGOR, seed, MGPT-PU141085. d: Eurya stigmosa (R.LUDW.) MAI, small seed with piths filled by organic remains and sediment, MGPT-PU141086. e: Eurya stigmosa (R.LUDW.) MAI, fragmentary seed, MGPT-PU141087. f: Visnea germanica MENZEL, fruit from two opposite sides, MGPT-PU141088. g: Symplocos casparyi R.LUDW., endocarp in lateral view from two opposite sides, MGPT-PU141089. Scale bar 1 mm. in Remains Of A Subtropical Humid Forest In A Messinian Evaporitebearing Succession At Govone, Northwestern Italy - Preliminary Results
Text-fig. 7. Stereomicroscope microphotographs of plant remains sieved out of a sediment bulk sample (C3X) from bed GLA10 of Govone. a: Tetraclinis salicornioides (UNGER) KVAČEK, shoot fragment, MGPT-PU141083). b: Toddalia latisiliquata (R.LUDW.) H.-J. GREGOR, seed, MGPT-PU141084. c: Toddalia rhenana H.-J.GREGOR, seed, MGPT-PU141085. d: Eurya stigmosa (R.LUDW.) MAI, small seed with piths filled by organic remains and sediment, MGPT-PU141086. e: Eurya stigmosa (R.LUDW.) MAI, fragmentary seed, MGPT-PU141087. f: Visnea germanica MENZEL, fruit from two opposite sides, MGPT-PU141088. g: Symplocos casparyi R.LUDW., endocarp in lateral view from two opposite sides, MGPT-PU141089. Scale bar 1 mm.
Text-fig. 5. Plant fragments from Govone with evidence of preserved cuticle. a: Decussate pair of leaves of "Thuja" saviana (C.T.GAUDIN) C.T.GAUDIN with a window (arrow) opened in the brownish cuticle, showing the yellowish mesophyll cells and some possible resin canals (dark), MGPT-PU141094. b: Angiosperm leaf fragment (from sample MGPT-PU141017) under the stereomicroscope, showing the blackish compressed mesophyll on the right and patches of cleaned, yellowish cuticle at the top (arrow). Scale bar 1 mm. in Remains Of A Subtropical Humid Forest In A Messinian Evaporitebearing Succession At Govone, Northwestern Italy - Preliminary Results
Text-fig. 5. Plant fragments from Govone with evidence of preserved cuticle. a: Decussate pair of leaves of "Thuja" saviana (C.T.GAUDIN) C.T.GAUDIN with a window (arrow) opened in the brownish cuticle, showing the yellowish mesophyll cells and some possible resin canals (dark), MGPT-PU141094. b: Angiosperm leaf fragment (from sample MGPT-PU141017) under the stereomicroscope, showing the blackish compressed mesophyll on the right and patches of cleaned, yellowish cuticle at the top (arrow). Scale bar 1 mm.
Text-fig. 4. Laminated silts with almost complete leaves at the top of bed GLA20: Dicotylophyllum sp. 3 (left: MGPTPU141032) and Laurophyllum sp. 2 (right: MGPT-PU141082). Scale bar 10 mm. in Remains Of A Subtropical Humid Forest In A Messinian Evaporitebearing Succession At Govone, Northwestern Italy - Preliminary Results
Text-fig. 4. Laminated silts with almost complete leaves at the top of bed GLA20: Dicotylophyllum sp. 3 (left: MGPTPU141032) and Laurophyllum sp. 2 (right: MGPT-PU141082). Scale bar 10 mm.
Text-fig. 6. Transmitted light microphotographs of permineralized wood from Govone. a, b: cf. Cupressinoxylon sp., radial section, MGPT-PU141105, a – nodular end of ray parenchyma (arrow), b – thick and pitted horizontal walls of ray parenchyma (arrow). c–f: Pinaceae gen. et sp. indet., MGPT-PU141107, c – abnormal discoloration due to ecological disruptions (radial section), d – rays up to 10 cells high, uniseriate, partly biseriate (black arrow), intercellular spaces observed (white arrows) (tangential section), e – large, thick-walled axial resin canal with more than 9 epithelial cells observed, axial resin canal diameter>60 Μm (transverse section), f – spiral thickenings due to compression (white arrow) (radial section). in Remains Of A Subtropical Humid Forest In A Messinian Evaporitebearing Succession At Govone, Northwestern Italy - Preliminary Results
Text-fig. 6. Transmitted light microphotographs of permineralized wood from Govone. a, b: cf. Cupressinoxylon sp., radial section, MGPT-PU141105, a – nodular end of ray parenchyma (arrow), b – thick and pitted horizontal walls of ray parenchyma (arrow). c–f: Pinaceae gen. et sp. indet., MGPT-PU141107, c – abnormal discoloration due to ecological disruptions (radial section), d – rays up to 10 cells high, uniseriate, partly biseriate (black arrow), intercellular spaces observed (white arrows) (tangential section), e – large, thick-walled axial resin canal with more than 9 epithelial cells observed, axial resin canal diameter>60 Μm (transverse section), f – spiral thickenings due to compression (white arrow) (radial section).
Text-fig. 3. a: Panoramic reconstruction of the portion of the Govone outcrop from intervals GLA10 to GLA20 in condition of low river level. b: Transported leaf assemblage in the bottom part of bed GLA20. c: Detail of the outcrop of the leaf-bearing bed GLA20 and the underlying wood-rich layer GLA19. in Remains Of A Subtropical Humid Forest In A Messinian Evaporitebearing Succession At Govone, Northwestern Italy - Preliminary Results
Text-fig. 3. a: Panoramic reconstruction of the portion of the Govone outcrop from intervals GLA10 to GLA20 in condition of low river level. b: Transported leaf assemblage in the bottom part of bed GLA20. c: Detail of the outcrop of the leaf-bearing bed GLA20 and the underlying wood-rich layer GLA19.
Text-fig. 2. Stratigraphic column of the Govone section with a detail of the sampled intervals on the right. CCS – Cassano Spinola Conglomerates; Gm and Gg – sedimentary cycles, respectively marl-dominated or gypsum-dominated; MES – Messinian Erosional Surface; MSC – Messinian Salinity Crisis; PLG – Primary Lower Gypsum; RLG – Resedimented Lower Gypsum. in Remains Of A Subtropical Humid Forest In A Messinian Evaporitebearing Succession At Govone, Northwestern Italy - Preliminary Results
Text-fig. 2. Stratigraphic column of the Govone section with a detail of the sampled intervals on the right. CCS – Cassano Spinola Conglomerates; Gm and Gg – sedimentary cycles, respectively marl-dominated or gypsum-dominated; MES – Messinian Erosional Surface; MSC – Messinian Salinity Crisis; PLG – Primary Lower Gypsum; RLG – Resedimented Lower Gypsum.
Text-fig. 1. Context and location of the Govone outcrop. a: Location of the Piedmont Basin at the northern margin of the Mediterranean Basin and distribution of Messinian evaporites. b: Simplified geological map of the Piedmont Basin showing the location of the Govone outcrop close to the town of Alba. in Remains Of A Subtropical Humid Forest In A Messinian Evaporitebearing Succession At Govone, Northwestern Italy - Preliminary Results
Text-fig. 1. Context and location of the Govone outcrop. a: Location of the Piedmont Basin at the northern margin of the Mediterranean Basin and distribution of Messinian evaporites. b: Simplified geological map of the Piedmont Basin showing the location of the Govone outcrop close to the town of Alba.
Air temperature and humidity effects on the performance of conservation detection dogs
<p>This is the dataset which underlies the submitted manuscript entitled " <strong>Air temperature and humidity effects on the performance of conservation detection dogs</strong>".</p> <p>The uploaded data contain an xlsx file,which includes the data, as well as a txt readme file, which explains the header information in the data file.</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.