Find research datasets worth reusing

opencc-by-4.0Nov 2024View details →

Air/Snow temperature vertical profiles at different sites in Livingston Island, Antarctica (2006-2023)

Air or seasonal snow temperature data collected at different heights above the ground (2.5, 5, 10, 20, 40, 80, and 160 cm), generally recorded every 3 hours between 2006 and 2023, using an array of temperature micro-loggers (iButton models by Maxim) mounted along a vertical wooden mast. These measurements were taken at various stations of the PERMATHERMAL network, managed by the University of Alcalá, Madrid, Spain, to monitor the thermal dynamics of frozen soils on Livingston Island, South Shetland Islands, Antarctica.

opencc-by-4.0Nov 2024View details →

Air/Snow temperature vertical profiles at different sites in Deception Island, Antarctica (2008-2023)

Air or seasonal snow temperature data collected at different heights above the ground (2.5, 5, 10, 20, 40, 80, and 160 cm), generally recorded every 3 hours between 2006 and 2023, using an array of temperature micro-loggers (iButton models by Maxim) mounted along a vertical wooden mast. These measurements were taken at various stations of the PERMATHERMAL network, managed by the University of Alcalá, Madrid, Spain, to monitor the thermal dynamics of frozen soils on Deception Island, South Shetland Islands, Antarctica.

opencc-by-4.0Nov 2024View details →

Air/Snow temperature vertical profiles at different nodes of the 'Crater Lake' CALM site in Deception Island, Antarctica (2012-2023)

Air or seasonal snow temperature data were collected at different heights above the ground between 2012 and 2023 using an array of temperature micro-loggers (iButton models by Maxim) mounted on vertical wooden masts. These measurements were conducted at various nodes within the 100x100 m 'Crater Lake' CALM site (A16) grid of the PERMATHERMAL network, managed by the University of Alcalá, Madrid, Spain, to monitor active layer thickness in Deception Island, South Shetland Islands, Antarctica. In 2012, nine arrays were installed at nodes with relative coordinates (00,00), (00,05), (00,10), (05,00), (05,05), (05,10), (10,00), (10,05), and (10,10). Measurements were taken at heights of 2.5, 5, 10, 15, 20, 25, 30, and 40 cm above the ground surface using DS1921G iButton loggers, which recorded air/snow temperatures every 4 hours. This experiment, referred to as 'Mini', was active until early 2021. Between 2017 and 2023, four arrays were installed at nodes (00,010), (05,05), (06,00), and (10,00). These arrays measured air/snow temperatures at heights of 2.5, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, and 160 cm above the ground surface using DS1922L iButton loggers, which recorded temperatures every 3 hours. Three of the arrays of this experiment, referred to as 'HR', has also been discontinued in early 2021, althought one of them was active until early 2024.

opencc-by-4.0Nov 2021View details →

Data set of 'Adiabatic temperature profile in the mantle, revised'

P-V-T data of the four major mantle minerals, olivine, wadselyite, ringwoodite, and bridgmanite The original data are as follows: Olivine: https://doi.org/10.1016/j.pepi.2008.08.002 Wadsleyite: https://doi.org/10.1029/2009GL038107 Ringwoodite: https://doi.org/10.1029/2004JB003094 Bridgmanite; https://doi.org/10.1029/2009GL039318 https://doi.org/10.1029/2011JB008988 The temperatures were recalculated using https://doi.org/10.1016/j.pepi.2019.106348 The pressures were recalculated using https://doi.org/10.1029/2011JB008988

opencc-by-4.0Jul 2024View details →

Temperature Profiles from the Eastern Tropical Pacific (0-300m) from January-February 2023

Vertical temperature profiles taken as part of a research expedition to Clipperton Atoll. Water column temperature profiles were measured down to 300m depth by deploying a RBRduet3 T.D. sensor (Range -5°C to 35°C; Initial accuracy ±0.002°C; Resolution <0.00005°C; time constant <1s). Data (downcast and upcast) is averaged by depth into 1 meter bins, and the standard deviation and number of measurements in each bin are included as columns in the data.   This data is supplemented locally for the shallow waters of Clipperton Atoll with 21 water column profiles measured using a Mares Puck Pro dive computer (Range: -10 °C to +50 °C; Resolution: 1°C; Accuracy: ± 2 °C) worn by one of the expedition divers.

opencc-by-4.0Dec 2022View details →

Increased egg shell temperature during incubation leads to changes in transcriptional and epigenetic profiles in chicken lungs

These RDS files contain DESeqDataSet objects subsets per broiler age and treatment. These objects are the result of DESeq2::DESeq( … ,betaPrior=FALSE).The .txt-objects contain the normalized sequencing counts per broiler age and treatment group. These objects are the result of DESeq2::counts( … , normalized=TRUE). Data was generated using STAR v2.7.10a and DESeq2 v1.36. Metadata is included as Excel file. Sequencing data is deposited at NCBI-SRA under BioProject: PRJNA949139.        Study abstract D. Schokker, J. de Vos, P.B. Stege, O. Madsen, H.J. Wijnen, S.K. Kar, and J.M.J. Rebel Health and resilience against respiratory diseases are important features for broiler chicken. In this study, epigenetic and transcriptomic changes in the lungs of broiler chickens of different ages during rearing that were either exposed to elevated egg shell temperature (HIGH) of 38.9°C during mid-incubation or normal egg shell temperature (control; CON). The objective was to better understand how environmental challenges, such as heat stress during egg incubation, affect the development of the immune system and health of broiler chicken at later age. To this end we generated both epigenetic and transcriptomic data of lung tissue of elevated HIGH and CON chicken, furthermore these chicken were challenged by introducing either an infectious E. coli or an IBV vaccination to monitor the respiratory response. Thousands of differential methylated sites were observed at days 15 and 33, when comparing HIGH vs. CON. Pathway enrichment analysis of HIGH vs. CON showed that differentially expressed genes were mainly involved in cilium, cytoskeleton, and immune processes. These findings provide insight into the underlying biological mechanisms of early life conditions, like elevated EST, and their potential role in health of broilers.

edi44/100

Mirror Lake High-Frequency Dissolved Oxygen and Temperature Profiles

This lake metabolism data was collected by HOBO Temperature loggers and miniDOT loggers that were deployed at Mirror Lake Central Buoy over the deepest part of the lake (11m) on 2023-08-23. HOBO loggers were deployed vertically at depths 0.25m, 2m, and 4m, and miniDOT loggers were deployed vertically at depths 0.5m, 1m, and 6m. Sensors were tied to nylon climbing rope at these different depths and anchored to a buoy. Loggers were later removed on 2023-09-08. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest in the White Mountains of New Hampshire, which is operated and maintained by the USDA Forest Service, Northern Research Station.

openCC (other)Jan 2026View details →

opencc-by-4.0Oct 2020View details →

Seawater temperature profiles from Expendable Bathythermograph (XBT) probe deployments during the Antarctic Circumnavigation Expedition (ACE)

Dataset abstract This data set contains vertical seawater temperature profiles measured by Expendable Bathythermograph (XBT) probes that were deployed in the Southern Ocean during the Antarctic Circumnavigation Expedition (ACE) on board the R/V Akademik Tryoshnikov. 40 XBT probes were deployed during legs 2 and 3 of the expedition in the period 25th January, 2017 to 17th March, 2017. The XBT probes are manufactured and distributed by T.S.K./Sippican Tsurumi-Seiki Co. Ltd., Yokohama, Japan (http://www.tsk-jp.com) and are of the type T-07, which is rated at a ship speed of up to 15 knots. These probes have a measuring time of 123 seconds and maximum measurement depth of about 789 m. Probes were launched from a handheld device from the stern of the ship either on the port or starboard side while the ship was moving. The deck unit recorded the temperature and the time since the probe was launched. This time was then converted to depth using the known fall rate of the probe in seawater and the coefficients provided by the manufacturer (WMO standards; Hanawa et al., 1995). The profiles were corrected for known surface biases (Kizu and Hanawa, 2002; Uehara et al., 2008). We provide the raw data, the data produced by using the coefficients provided by the manufacturer, and a corrected version in which we apply an empirical correction based on a comparison with CTD data (Henry et al., 2019), where XBT profiles were launched alongside the CTD deployment. The data has been quality controlled by comparing it to a number of CTD profiles. Data is provided at full vertical resolution and a 1-m averaged resolution. In addition, we provide derived variables such as surface mixed layer depth (temperature threshold) estimates. We are grateful to the crew of the R/V Akademik Tryoshnikov and AARI for donating these probes to our project. Their use-by date had expired, however this was not seen as an issue. This data set provides insights into the hydrography of the Southern Ocean during one austral summer season and complements the CTD temperature profiles measured during ACE by filling in the gaps between CTD stations. Dataset contents Data: <ul> <li>ace_xbt_raw/ace_xbt_YYYYMMDD_xxxx_uuuuuuuuuuuu.RAW, data file, comma-separated values</li> <li>ace_xbt_wmo_hanawa95_fullres/ace_xbt_YYYYMMDD_xxxx_uuuuuuuuuuuu.XBT, data file, comma-separated values</li> <li>ace_xbt_wmo_hanawa95_1m/ace_xbt_YYYYMMDD_xxxx_uuuuuuuuuuuu_1m.XBT, data file, comma-separated values</li> <li>ace_xbt_corrected_fullres/ace_xbt_YYYYMMDD_xxxx_uuuuuuuuuuuu.XBT, data file, comma-separated values</li> <li>ace_xbt_corrected_1m/ace_xbt_YYYYMMDD_xxxx_uuuuuuuuuuuu_1m.XBT, data file, comma-separated values</li> </ul> Auxiliary data: <ul> <li>ace_xbt_mld_tavg.csv, data file, comma-separated values</li> <li>ace_merged_ctd_xbt_mld_tsavg.csv, data file, comma-separated values</li> </ul> Figures: <ul> <li>figure1.pdf, metadata, portable document format</li> <li>ace_xbt_figures/ace_xbt_YYYYMMDD_xxxx_1m.pdf, metadata, portable document format</li> </ul> Metadata: <ul> <li>ace_xbt_deployment_summary.csv, metadata, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> Dataset license This seawater temperature profile dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/

opencc-by-4.0Mar 2020View details →

OU/NSSL CLAMPS AERIoe Temperature and Water Vapor Profile Data from LAPSE-RATE

The AERIoe algorithm (Turner and Loehnert 2014, Turner and Blumberg 2018) retrieves profiles of temperature and water vapor mixing ratio, together with cloud properties for a single-layer cloud (i.e., LWP, effective radius), from AERI-observed infrared radiance spectrum. The data can be used to characterize the evolution of the planetary boundary layer and boundary layer clouds. This dataset was collected at the Moffat Consolidated School in Moffat, CO during the LAPSE-RATE field campaign. The AERIoe retrieval was run at 15-minute resolution to match the cadence of the UAS that was colocated with the CLAMPS facility. This is a physical-iterative retrieval method. The retrieval of thermodynamic profiles from spectral infrared radiance observations is an ill-posed problem, and thus constraints need to be included in the retrieval algorithm to provide physically plausible results. Here, we use a climatology of 2022 radiosonde profiles collected from Denver during July as our prior information in an optimal estimation framework. As the method uses an optimal estimation framework, a full error covariance matrix of each solution is included in the output file. The 1-sigma uncertainty of each retrieved variable, which is derived from the error covariance matrix, is included for each scientific field and is named "sigma_X", where "X" is the name of the scientific field (e.g., 'temperature'). The information content in the AERI observations, which is in the "dfs" field, on the thermodynamic profiles is primarily concentrated in the lowest 3 km or up to cloud base; the retrieved data should not be used above that level (or used with caution).   References: Turner, D. D., and U. Löhnert, 2014: Information Content and Uncertainties in Thermodynamic Profiles and Liquid Cloud Properties Retrieved from the Ground-Based Atmospheric Emitted Radiance Interferometer (AERI). J. Appl. Meteor. Climatol., 53, 752–771, <a href="https://doi.org/10.1175/JAMC-D-13-0126.1">https://doi.org/10.1175/JAMC-D-13-0126.1</a>. Turner, D. D., and W. G. Blumberg, 2019: Improvements to the AERIoe Thermodynamic Profile Retrieval Algorithm. IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing, 12, 1339–1354, <a href="https://doi.org/10.1109/JSTARS.2018.2874968">https://doi.org/10.1109/JSTARS.2018.2874968</a>.

opencc-by-4.0Nov 2023View details →

Vertical profiles of air temperature, relative humidity, wind speed and direction observed using UAV over the Mukhrino peatland in June 2022

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.

opencc-by-4.0Apr 2024View details →

Neural Network predictions and ERA5 reference of integrated water vapour, and temperature and specific humidity profiles based on simulated microwave radiometer observations

This data set contains predictions of the Neural Network retrievals described in [1], where simulated microwave radiometer observations (brightness temperatures, TBs) from the evaluation data subset of [2] (years 2001, 2006, 2011, 2015) were used as input to the Neural Network. As described in Section 3.2 of [1], 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:  <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> The cryptic 3-digit filename suffixes represent different settings of the Neural Network retrieval. More information can be found in [3]. 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. These files can be created when running run_NN_retrieval (contained in NN_retrieval.py, see [3]) 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.   [1]: 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. [2]: 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. [3]: 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.

opencc-by-4.0Jul 2023View details →

Soil temperature profiles, measured using a coil-shaped fiber-optic distributed temperature sensor

Measurements of soil temperature temperature profile, by reference sensors and a coil-shaped fiber optic distributed temperature sensor. Retrieved at the Speulderbos measurement site, 52.251048 N, 5.690061 E.   A full description can be found in: Schilperoort, B. (2022). Heat Exchange in a Conifer Canopy: A Deep Look using Fiber Optic Sensors [Delft University of Technology]. https://doi.org/10.4233/uuid:6d18abba-a418-4870-ab19-c195364b654b

opencc-by-4.0Aug 2023View details →

WHOI prototype Vertical Temperature Profiler data from Quashnet River site QRSP28

Vertical Temperature Profiler data from the WHOI prototype instrument, acquired at the Quashnet River site QRSP28 from 17 June, 2022 to 11 July, 2022. Data sampled at 5 minute intervals. Eighteen total temperature records, with the measurement depth listed in the first (header) row of the file. Depths are in cm (e.g., T8 is the temperature record from 8 cm depth). Note that absolute depths could be in error by as much as 2 cm due to uncertainty introduced by insertion process, but relative depths are highly accurate. Data have been calibrated based on water bath tests.

opencc-by-4.0May 2019View details →

Animated DTS temperature profiles Speulderbos forest

<a href="https://www.youtube.com/playlist?list=PL0Ojbg2cPwM8u4ZaGC9WWMtCGCQ5GUTiJ">These videos are also on YouTube for easier viewing</a>. Vertical temperature profiles of the air temperature (red) and the wet-bulb temperature (blue) are animated through time. The measurement tower and an example tree is shown on the right. The time shown is in UTC+1 Different radiation shielding above canopy was available (or absent), which is shown on the right hand side of the video. From 2017/08/03 onwards, a coiled fiber measures the temperature profile in the bottom 1.0 meters. The measurement method is described in <a href="http://doi.org/10.5194/hess-22-819-2018">doi.org/10.5194/hess-22-819-2018</a> Part of the raw data (2016) is available on <a href="https://doi.org/10.4121/uuid:5c81f10a-1249-4b85-8dec-2b029dd88b99">doi.org/10.4121/uuid:5c81f10a-1249-4b85-8dec-2b029dd88b99</a>

dryad40/100

Data from: Turbulence organization and mean profile shapes in the stably stratified boundary layer: Zones of uniform momentum and air temperature

Open the record for dataset details and reuse information.

publicNov 2022View details →

openCC (other)Aug 2017View details →

University of Kansas Field Station: Water temperature profiles at Frank B. Cross Reservoir (Kansas, USA) 1993-2016

These vertical profiles of water temperature data are from Frank B. Cross Reservoir, a small freshwater impoundment in northeastern Kansas (USA). Cross Reservoir, located at the University of Kansas Field Station near Lawrence (KS), has a 3-ha surface area and a maximum depth of 12 m. Temperature data were collected at discrete depths in a vertical profile using a Water Quality Checker U-10 (Horiba Instruments, Kyoto, Japan). These temperature data were collected in conjunction with ecological studies at Cross Reservoir examining deep-dwelling, vertically-migrating phytoplankton communities that form a deep chlorophyll maxima, especially during times of thermal stratification. Therefore, the most frequent sampling dates occurred in July, August, September and October during thermal stratification when vertically migrating deep algae were most common (other months were sampled less frequently through the years). Cross Reservoir has high water clarity due to its watershed vegetation and physical (protected) setting. Thermal stratification begins each year in April, and complete mixing occurs during November. The bottom of the epilimnion is typically located at 3 m in July and usually deepens to approximately 9 m by late November. The bottom of the epilimnion is defined here as the bottom of the first 1 m depth interval that exhibited a >1 C temperature decrease per meter. The water column also consistently exhibits strong chemical stratification during the summer months. Sporadic ice cover can occur during December–February.

openCC (other)Sep 2024View details →

Mono Lake, California water temperature depth profiles (0.5-m depth intervals) collected at multiple stations from 1991-2022 with conductivity-temperature-depth profilers.

Water temperature profiles collected at buoyed stations in Mono Lake with conductivity-temperature-depth (CTD) profiles from 1991 to 2022. Data from 1991 to June 2012 collected and archived by Jellison & Melack (UCSB). Data from July 2012 to 2022 collected by the Los Angeles Department of Water and Power and are available in annual compliance reports submitted to the California State Water Resources Control Board.

openOpenFeb 2017View details →

McMurdo Dry Valleys Lake Bonney Autonomous Lake Profiler and Samplers (ALPS): Conductivity, Temperature, and Pressure

Knowledge of the McMurdo Dry Valley (MDV) lakes is limited by winter access, a period which is most relevant in understanding the habitability of other icy worlds and critical to understanding the overall function of these lakes.Â Owing to the lack of winter access, data that normally require human presence are incomplete.Â Our goal was to conduct the first year-round investigation of the biogeophysics of these unique lakes.Â An important part of the McMurdo Long Term Ecological Research (LTER) is evaluating carbon and nitrogen budgets in perennial ice-covered lakes. This data set addresses this core area of research and quantifies the vertical profile of conductivity, temperature, and pressure in Lake Bonney.

openCC (other)Sep 2021View details →

Weekly CTD profile measurements of conductivity, specific conductance, temperature, depth, density, and salinity from Lake Hoare, McMurdo Dry Valleys, Antarctica during the 2012-2013 austral summer

As part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) project, we investigated relationships between wind conditions and barotropic seiches within Lake Hoare, located in Taylor Valley, Antarctica, during the 2012-2013 austral summer. Temporal changes in the water column were measured using a rugged, handheld, CastAway CTD (conductivity-temperature-depth probe, manufactured by SonTek), deployed through the Lake Hoare Limno Hole (note, this is a separate instrument from the SeaBird CTD used as part of the MCM LTER core limnological monitoring program). The CastAway CTD directly measures temperature, electrical conductivity, and pressure at 5 Hz as it free-falls through the water column at a rate of approximately 1 m s-1 on the downward cast and approximately 0.3 m s-1 on the upward cast. The device calculates salinity and density using the International Equation of State for Seawater, called EOS-80. This data package provides 27 profiles of depth, temperature, conductivity, specific conductance, salinity, and density collected between November 25, 2012 and January 21, 2013. In general, three profiles were collected over a ten-minute-period every seven days for 57 days. Five profiles were collected prior to the arrival of spring melt, four profiles were collected during the arrival of spring melt on December 7, 2012, and 18 profiles were collected after the arrival of spring melt continuing into late summer.