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.
9
datasets available to search
ShareScore release 0.7.1
Dataset results
9 results for “czo”
CO2 Net Ecosystem Exchange (NEE) and Ecosystem Respiration (ER) + meteorological parameters in alpine grasslands at Nivolet Plain, Gran Paradiso National Park, 2020 (IGG-CNR-CZO@NIVOLET)
<p>CO2 Net Ecosystem Exchange (NEE) and Ecosystem Respiration (ER) measured at Nivolet Plain, Gran Paradiso National Park, Italy, in a high-altitude Alpine grassland environment (about 2500-2700 m.a.s.l.) using the closed portable flux chamber method during the 2020 vegetative season (July-October), approximately twice a month. NEE is measured with a transparent chamber, while ER with a dark chamber (transparent chamber shaded with a cloth). Data represent the average values and the corresponding standard deviations obtained from five sites at different altitudes and soil substrates. Each average value is obtained as a mean over a set of 20 point-measurements for each site and each sampling date. Flux data are complemented by measurements of soil temperature and soil volumetric water content, air temperature, air RH, and solar radiance.</p> <p>During the measurement, air is pumped from the chamber to an IR gas analyzer (IRGA) and then injected again in the chamber. The CO2 concentration inside the chamber is measured for about 90 seconds and then the rate of concentration change is linearly interpolated (over 60s) to obtain the flux measurements. A detailed description of the sampling method can be found in Magnani et al. (2020).</p> <p>Instrumentation used: accumulation chambers (height: 31.5 cm; area of the base: 363 cm2), LI-COR LI-840 & LI-850 IR spectrophotometers, stainless-steel collars (inserted into the soil to a depth of about 1 cm), portable meteorological stations (pyranometer LSI Lastem DPA053, thermohygrometer LSI Lastem DMA672.1), pt100 soil temperature sensors, SM150T soil moisture sensor.</p>
CO2 Net Ecosystem Exchange (NEE) and Ecosystem Respiration (ER) + meteorological parameters in alpine grasslands at Nivolet Plain, Gran Paradiso National Park, 2021 (IGG-CNR-CZO@NIVOLET)
<p>CO2 Net Ecosystem Exchange (NEE) and Ecosystem Respiration (ER) measured at Nivolet Plain, Gran Paradiso National Park, Italy, in a high-altitude Alpine grassland environment (about 2500-2700 m.a.s.l.) using the closed portable flux chamber method during the 2021 vegetative season (July-October), approximately twice a month. NEE is measured with a transparent chamber, while ER with a dark chamber (transparent chamber shaded with a cloth). Data represent the average values and the corresponding standard deviations obtained from five sites at different altitudes and soil substrates. Each average value is obtained as a mean over a set of 20 point-measurements for each site and each sampling date. Flux data are complemented by measurements of soil temperature and soil volumetric water content, air temperature, air RH, and solar radiance.</p> <p>During the measurement, air is pumped from the chamber to an IR gas analyzer (IRGA) and then injected again in the chamber. The CO2 concentration inside the chamber is measured for about 90 seconds and then the rate of concentration change is linearly interpolated (over 60s) to obtain the flux measurements. A detailed description of the sampling method can be found in Magnani et al. (2020).</p> <p>Instrumentation used: accumulation chambers (height: 31.5 cm; area of the base: 363 cm2), LI-COR LI-840 & LI-850 IR spectrophotometers, stainless-steel collars (inserted into the soil to a depth of about 1 cm), portable meteorological stations (pyranometer LSI Lastem DPA053, thermohygrometer LSI Lastem DMA672.1), pt100 soil temperature sensors, SM150T soil moisture sensor.</p>
ipaast-czo case study: Dehesa Boyal de Botija
<p>VV01 (“Cerca del cementerio” sector). Data set description.</p> <p>One single geophysical method was used:</p> <ul> <li>Electromagnetic induction with a EM38Mk2 by Geonics. Although topography obliged to split the survey area in 3 independent data sets, after correction and merging of all the data in one single data set it was observed that, considering the small time lapse between each one and homogeneous weather conditions, no significant discontinuity in measured data was observed.</li> </ul> <p>The interest on testing EMI survey in this dehesa environment within the framework of the IPAAST project was motivated with a triple objective:</p> <ul> <li>The aim of exploring off-site activities around the hillfort of Villasviejas: traces of intensive agriculture, industrial activities, dumping areas, mining etc.</li> <li>The aim of assessing the composition and depth of soils in the area in order to evaluate the representativeness of surface finds and make a regression analysis on the potential distribution of arable lands during the Iron Age in the area.</li> <li>The aim of combining geophysical methods commonly used in precision agriculture and archaeology in order to evaluate their interoperability and the complementary information they can provide.</li> </ul> <p>VV01 sector corresponds to an area of dehesa named “Cerca del cementerio” (“cemetery enclosure”): it is a land plot 70m SW of the hillfort with a surface of 5240 sq. m. Previous knowledge of the area revealed the presence of a high density of archaeological materials, but there was no clear evidence of buried structures. A particular feature of this sector was the great depth of soil deposits within the walled enclosure, in great contrast with the surrounding fields. Data sets:</p> <ul> <li> EMI04. <ul> <li>01 Vector limits of survey area</li> <li>02 Vector file of point data</li> <li>03 Raster interpolation of quad-phase (conductivity) 0,5m</li> <li>04 Raster interpolation of quad-phase (conductivity) 1m</li> <li>05 Raster interpolation of in-phase (magnetic susceptibility) 0,5m</li> <li>06 Raster interpolation of in-phase (magnetic susceptibility) 1m</li> </ul> </li> </ul> <p>VV02 (Mercadillo sector). Data set description.</p> <p>Two main methods were used:</p> <ul> <li>Magnetic survey: a dual-sensor gradiometer system (Grad602 Bartington)</li> <li>Electromagnetic induction with a EM38Mk2 by Geonics. Dense vegetation and topography obliged to split the survey area in 3 independent data sets.</li> </ul> <p>Additionally several areas were surveyed with GPR (Nogging system of Sensor&Software).</p> <p>The interest on testing different survey methods in the framework of the IPAAST project was motivated with a triple objective:</p> <ul> <li>The aim of exploring off-site activities around the hillfort of Villasviejas: traces of intensive agriculture, industrial activities, dumping areas, mining etc.</li> <li>The aim of assessing the composition and depth of soils in the area in order to evaluate the representativeness of surface finds and make a regression analysis on the potential distribution of arable lands during the Iron Age in the area.</li> <li>The aim of combining geophysical methods commonly used in precision agriculture and archaeology in order to evaluate their interoperability and the complementary information they can provide.</li> </ul> <p>VV02 sector corresponds to an area of dehesa named “El Mercadillo (“little market”). It is located 150 m south of the hillfort of Villasviejas and covers a surface of approx. 3500 sq m. In this sector was excavated in the 1980s a funerary area corresponding to the earliest period of the hillfort (IV-II centuries B.C). Analysis of LiDAR data revealed that in the same area there were several topographic features that could be linked with off-site activity during the protohistoric and early roman period. Data sets:</p> <ul> <li> EMI01. <ul> <li>01 Vector limits of survey area</li> <li>02 Vector file of point data</li> <li>03 Raster interpolation of quad-phase (conductivity) 0,5m</li> <li>04 Raster interpolation of quad-phase (conductivity) 1m</li> <li>05 Raster interpolation of in-phase (magnetic susceptibility) 0,5m</li> <li>06 Raster interpolation of in-phase (magnetic susceptibility) 1m</li> </ul> </li> <li> EMI02. <ul> <li>01 Vector limits of survey area</li> <li>02 Vector file of point data</li> <li>03 Raster interpolation of quad-phase (conductivity) 0,5m</li> <li>04 Raster interpolation of quad-phase (conductivity) 1m</li> <li>05 Raster interpolation of in-phase (magnetic susceptibility) 0,5m</li> <li>06 Raster interpolation of in-phase (magnetic susceptibility) 1m</li> </ul> </li> <li>EMI03. <ul> <li>01 Vector limits of survey area</li> <li>02 Vector file of point data</li> <li>03 Raster interpolation of quad-phase (conductivity) 0,5m</li> <li>04 Raster interpolation of quad-phase (conductivity) 1m</li> <li>05 Raster interpolation of in-phase (magnetic susceptibility) 0,5m</li> <li>06 Raster interpolation of in-phase (magnetic susceptibility) 1m</li> </ul> </li> <li>MAG01. <ul> <li>01 Vector limits of survey area.</li> <li>02 Vector point file of vertices of survey area.</li> <li>03 Grid composite.</li> <li>04 Raster interpolation of magnetic data.</li> </ul> </li> </ul> <p> </p>
ipaast-czo case study: The Rinconada Estate.
<p>The interest in testing different survey methods within the framework of the IPAAST project was motivated by three objectives:</p> <ul> <li>identifying evidence of rural life in the hinterland of the Roman colony of Augusta Emerita, with special attention to forms of resilience and diversification of agrarian activities beyond the areas of highest productivity of the alluvial plain of the Guadiana River.</li> <li>assessing the composition and depth of soils in the area in order to evaluate the representativeness of surface finds and perform a regression analysis to assess the potential distribution of arable lands in the area from Roman times to the present.</li> </ul> <p>combining geophysical methods commonly used in precision agriculture and archaeology in order to evaluate their interoperability and the complementary information they can provide.</p> <p> </p> <p>Attention was focused on a sector of the estate where two elements were coincident 1) preliminary evidence suggested the estate’s highest concentration of archaeological finds. 2) land plots within the estate used for grazing where LIFE Adapt experiments were undertaken. This area encompassed approximately. 6.5 ha.</p> <p> </p> <p>2 geophysical methods for the exploration were used:</p> <ul> <li>Magnetic survey: a 2 sensors gradiometer system was used (Grad602 Bartington). Data sets: <ul> <li>RC_MAG <ul> <li>01 Vector limits of survey area.</li> <li>02 Vector point file of vertices of survey area.</li> <li>03 Grid composite.</li> <li>04 Raster interpolation of magnetic data.</li> </ul> </li> </ul> </li> <li>Electromagnetic induction with a EM38Mk2 by Geonics. Data sets: <ul> <li>RC_EMI01. <ul> <li>01 Vector limits of survey area</li> <li>02 Vector file of point data (raw data)</li> <li>03 Raster interpolation of quad-phase (conductivity) 0,5m</li> <li>04 Raster interpolation of quad-phase (conductivity) 1m</li> <li>05 Raster interpolation of in-phase (magnetic susceptibility) 0,5m</li> <li>06 Raster interpolation of in-phase (magnetic susceptibility) 1m</li> </ul> </li> <li>RC_EMI02. <ul> <li>01 Vector limits of survey area</li> <li>02 Vector file of point data</li> <li>03 Raster interpolation of quad-phase (conductivity) 0,5m</li> <li>04 Raster interpolation of quad-phase (conductivity) 1m</li> <li>05 Raster interpolation of in-phase (magnetic susceptibility) 0,5m</li> <li>06 Raster interpolation of in-phase (magnetic susceptibility) 1m</li> </ul> </li> </ul> </li> </ul> <p> </p> <p> </p>
ipaast-czo case study OptRX data: Montalcino (SI, Italy)
<p>These data were collected as part of a case study for the ipaast project. The aim of the survey was to produce datasets interoperable for applications in archaeology and precision agriculture.</p> <p>The OptRx® Crop Sensors (AgLeader Technology, Ames, IO, USA) measure the reflectance in the 630–685 nm (red), 695–750 nm (RE red edge) and 760–850 nm (NIR—Near InfraRed) wavebands. Using those wavebands, NDVI and NDRE indexes are calculated. NDVI and NDRE are vegetative indexes obtained from the red, red-edge and NIR wavebands with formulas 1 and 2: </p> <p> </p> <p>NDVI = NIR−REDNIR+RED ; NDRE= NIR−RENIR+RE</p> <p> </p> <p>The two index values range from -1 (bare ground or water) to 1 (highly vigorous vegetation). </p> <p>To collect data, the sensor was mounted on a ground vehicle, a Kubota B2420 tractor. The sensor was paired with a GNNS receiver, GPS 6500 from AgLeader Technology (Ames, IO, USA). The instrumentation was coupled with the hardware and the rough book (Panasonic ToughPad FG-Z1, Panasonic Core. It was possible to install the sensor facing the ground using a metal bracket positioned on the front of the tractor. The sensor was positioned 1.15 m from the ground, emitting a rectangular footprint of 1.14 m in length and 20cm in width. The data were collected every 30 cm in alternate rows. 12 rows in total were analysed, covering a surface of 1.07 ha. </p> <p>Data were processed on QGIS. First, the data was interpolated with the Inverse Distance Weighting (IDW) function. The function was set up with a distance coefficient P of 4, with 40 rows and 98 columns. A Gaussian filter with a standard deviation value of 2 and a range of research of 3 was subsequently applied to create a representative raster. </p>
Catchment scale soil variably in Marshall Gulch, Santa Catalina Mountains Critical Zone Observatory (CZO), Arizona, 2012
The quantification and prediction of soil properties is fundamental to further understanding the Critical Zone (CZ). In this study we aim to quantify and predict soil properties within a forested catchment, Marshall Gulch, AZ. Input layers of soil depth (modeled), slope, Saga wetness index, remotely sensed normalized difference vegetation index (NDVI) and national agriculture imagery program (NAIP) bands 3/2 were determined to account for 95% of landscape variance and used as model predictors. Target variables including soil depth (cm), carbon (kg/m 2 ), clay (%), Na flux (kg/m 2 ), pH, and strain are predicted using multivariate linear step-wise regression models. Our results show strong correlations of soil properties with the drainage systems in the MG catchment. We observe deeper soils, higher clay content, higher carbon content, and more Na loss within the drainages of the catchment in contrast to the adjacent slopes and ridgelines.
Snow-Off Digital Terrain Model (DTM) from 2010 LiDAR for the Boulder Creek Critical Zone Observatory (CZO), Colorado
This 1m Digital Terrain Model (DTM) is a snow-off DTM derived from bare-ground Light Detection and Ranging (LiDAR) point cloud data from August 2010 for the Boulder Creek Critical Zone Observatory (CZO), near Boulder Colorado. This dataset is better suited for derived layers such as slope angle, aspect, and contours. The DTM was created from 1,375 LiDAR point cloud tiles subsampled from 10 points/m2 to 1-meter postings, acquired by the National Center for Airborne Laser Mapping (NCALM) project. This data was collected in collaboration between the Boulder Creek CZO and NCALM, both funded by the National Science Foundation (NSF). The DTM has the functionality of a map layer for use in Geographic Information Systems (GIS) or remote sensing software. Total area imaged is 598.92 km^2. The LiDAR point cloud data was acquired with an Optech Gemini Airborne Laser Terrain Mapper (ALTM) and mounted in a Piper Twin PA-31 Chieftain with Inertial Measurement Unit (IMU) at a flying height of 600 m. Data from four GPS (Global Positioning System) ground stations were used for aircraft trajectory determination. The continuous DTM surface was created by mosaicing and then kriging 0.5 x 1 km LiDAR point cloud LAS-formated tiles using Golden Software's Surfer 8 Kriging algorithm. Horizontal accuracy is at least, but usually better than, 11 cm RMSE at 1 sigma and vertical accuracy is 5-30 cm RMSE at 1 sigma. The layer is available in IMAGINE format approx. 4 GB of data. It has a UTM zone 13 projection, with a NAD83 horizonal datum and a NAVD88 vertical datum, with FGDC-compliant metadata. A shaded relief model was also generated. A similar layer, the Digital Surface Model (DSM), is a first-stop elevation layer. Accessory layers consist of index map layers for point cloud tiles and flight lines, each with detailed attribute information such as acquisition date and tile file name. The DTM is available through an unrestricted public license. Other LiDAR DSMs, DTMs, and point cloud data ava
Eddy Covariance data from ICOS-associated station IT-NIV at Nivolet Plain, Gran Paradiso National Park, 2021 (IGG-CNR-CZO@NIVOLET)
<p>Data stored here refer to Eddy Covariance (EC) data measured in 2021 at the Alpine CZO (IGG-CNR-CZO@NIVOLET), established in 2019 at the Nivolet Plain (Piani del Nivolet) in the Gran Paradiso National Park (GPNP). The EC tower was installed to deeply study CO2, H20, latent and sensible heat exchanges between soil, vegetation, and atmosphere. </p> <p>Carbon dioxide fluxes and environmental variables are recorded to estimate carbon storage and explore CO2 fluxes drivers in high-altitude grasslands.</p> <p>Measured parameters: Net vertical turbulent CO2 flux (µmol+1s-1m-2); air temperature (deg_C), air relative humidity (%), wind speed (m+1s-1), max wind speed (m+1s-1), wind direction (deg_from_north).</p> <p>Method: The EC station respects both sensors and data processing standards defined by the ICOS community. Flux processing software: EddyPro® set to default processing settings. The output variables have a 30-minutes resolution. The uploaded parameters are obtained by aggregating over a 3-hours time span. The associated measurement time (column B in the uploaded file) indicates the beginning of the 3-hours averaging period (es. Time T= [Time T; Time T+2h30 min]).</p> <p>Instrumentation: 3-Dimensional Sonic Anemometer (Manufacturer: Gill WindMaster, s.n.: W174606, Firmware: 2329-701-01, Instrument height: 4.485 m, North alignment: SPAR, North offset: 0, Wind data format: u, v, w); Enclosed path CO2/H2O Gas Analyzer (Licor Li7200RS, s.n.: 72H-0947, Firmware: 8.8.28, Tube length: 115.5 cm, Tube diameter: 5.33 cm); Weather station (Thermohygrometer LSI Lastem DMA 672). </p> <p>Quality assurance: Mauder and Foken (2004) flagging policy (0-1-2 system). Here, only high quality data are reported (qc=0).</p>
Eddy Covariance data from ICOS-associated station IT-NIV at Nivolet Plain, Gran Paradiso National Park, 2020 (IGG-CNR-CZO@NIVOLET)
<p>Data stored here refer to Eddy Covariance (EC) data measured at the Alpine CZO (IGG-CNR-CZO@NIVOLET), established in 2019 at the Nivolet Plain (Piani del Nivolet) in the Gran Paradiso National Park (GPNP). The EC tower was installed to deeply study CO2, H20, latent and sensible heat exchanges between soil, vegetation, and atmosphere. </p> <p>Carbon dioxide fluxes and environmental variables are recorded to estimate carbon storage and explore CO2 fluxes drivers in high-altitude grasslands.</p> <p>Measured parameters: Net vertical turbulent CO2 flux (µmol+1s-1m-2); air temperature (deg_C), air relative humidity (%), wind speed (m+1s-1), max wind speed (m+1s-1), wind direction (deg_from_north)</p> <p>Method: The EC station respects both sensors and data processing standards defined by the ICOS community. Flux processing software: EddyPro® set to default processing settings. The output variables have a 30-minutes resolution. The uploaded parameters are obtained by aggregating over a 3-hours time span. The associated measurement time (column B in the uploaded file) indicates the beginning of the 3-hours averaging period (es. Time T= [Time T; Time T+2h30 min])</p> <p>Instrumentation: 3-Dimensional Sonic Anemometer (Manufacturer: Gill WindMaster, s.n.: W174606, Firmware: 2329-701-01, Instrument height: 4.485 m, North alignment: SPAR, North offset: 0, Wind data format: u, v, w); Enclosed path CO2/H2O Gas Analyzer (Licor Li7200RS, s.n.: 72H-0947, Firmware: 8.8.28, Tube length: 115.5 cm, Tube diameter: 5.33 cm); Weather station (Thermohygrometer LSI Lastem DMA 672). </p> <p>Quality assurance: Mauder and Foken (2004) flagging policy (0-1-2 system). Here, only high quality data are reported (qc=0).</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.