Find research datasets worth reusing

opencc-by-4.0Mar 2022View details →

Global daily high-resolution surface carbon monoxide concentrations (2019-2020)

A brand-new framework is developed to estimate daily globally distributed surface carbon monoxide (CO) concentrations (2019-2020) at a high spatial resolution (0.05º) through data-driven fusion. Evaluation results (historical) show that the proposed framework presents a desired estimation accuracy over the globe, with the Rs/RMSEs of 0.73/0.273 ppm and 0.77/0.215 ppm at daily and monthly scales, respectively. Compared to GEOS Composition Forecasting replay  product, the proposed framework also performs distinctly better, of which the R increases by 0.3 and RMSE decreases by 0.185 ppm.

opencc-by-4.0Oct 2021View details →

Incubation data, CO2 and CH4 flux data and soil properties of thaw slump soils on Kurungnakh, Lena Delta in July 2016 and July 2019

CO2 and CH4 rates from incubations and potential fluxes: This dataset contains rates of CO2 and CH4 production and the potential CO2 and CH4 emission rates calculated from these incubation fluxes in situ CO2 and CH4 chamber fluxes: This dataset contains CO2 and CH4 fluxes measured with closed chambers from different sites on Kurungnakh in July 2016 and July 2019 simulated soil temperature and modelled CO2 fluxes: This dataset contains daily mean soil temperature data simulated with JSBACH for 2016 and the annual CO2 fluxes simulated with a Q10 model and the Introductory Carbon Balance Model (ICBM) thaw depth, TOC in active layer, soil temperature 2016: This dataset contains the thaw depth, TOC pools in the active layer and the soil temperature during the measurement period in July 2016 thaw depth, TOC in active layer, soil temperature 2019: This dataset contains the thaw depth, TOC pools in the active layer and the soil temperature during the measurement period in July 2019

opencc-by-4.0Nov 2023View details →

Agronomic performance of cultivar mixtures of winter wheat varieties, obtained from mixture field trials at 5 locations in Switzerland from 2019 to 2020, together with yield data from the varieties in pure stand obtained from the national variety testing trial network

This dataset contains agronomic parameters of 32 winter wheat variety mixtures tested during 2 growing seasons (2019-2020) at 5 locations in Switzerland, as well as yield data of these varieties in pure stands originating from the Swiss national variety testing network. The dataset has been used to investigate the links between asynchrony and yield stability, published in <a href="https://doi.org/10.1002/csc2.21151">https://doi.org/10.1002/csc2.21151</a>.   The field trials were performed under the Swiss Extenso (low input) conditions, conducted by Agroscope and DSP.  <h2>Methods </h2> Field trials  The experiment took place in five sites across Switzerland, in 2019 and 2020. The sites were located in Nyon (1260), Delley (1567), Utzenstorf (3428), Zurich (8046), and Ellighausen (8566). Experimental communities consisted of 32 different two-variety mixtures grown in 7.1-m2 plots (1.5 × 4.7 m). We replicated the mixture experiment three times per site with the exact same variety composition. We used a randomized block design, with plots being randomized at each site within each block. Density of sowing was 350 seeds/m2, and seeds were mixed beforehand at a 50:50 ratio in terms of mass. We used the 50:50 mass ratio as this is what is generally done in practice by farmers and seed suppliers. Plots were sown mechanically each autumn. The plots were mechanically fertilized according to the Principles of Agricultural Crop Fertilisation in Switzerland (Federal Office for Agriculture) with an average of 140 kg N/ha (ammonium nitrate), applied in three splits (40 at the tillering stage—60 at stem elongation stage—40 when the flag leaf is visible). The experimental trials were conducted following the extenso Swiss scheme, which means that there was no application of any fungicide, insecticide, or plant growth regulator.  The performances of single varieties were obtained by going through the trials of the national variety testing program. We gathered the data for the years 2018/2019 and 2019/2020. The data regarding single varieties could be obtained for three out of the five sites used for the mixtures: 1260, 1567, and 8566. Because there were no national variety trials at the two other sites (8046, 3428), we could not get any data for single varieties in these sites. Thus, all further analyses including single variety data were only done for the three sites mentioned above. At each of these sites, the variety trials were located on the same plot as the mixture trials, even though a little further apart. Therefore, soil parameters and crop precedents were the same between the mixture and variety testing trials. Furthermore, we only selected the national variety testing trials that respected the extenso conditions, that is, no fungicide, pesticide, or growth regulator application, and that received the same amount of fertilization as the mixture trials. In 8566 and 1567, sowing and harvesting dates were identical between the two trials; in 1260, sowing and harvesting dates could vary but remained within a week of each other.   Data collection  For each plot, heading dates were monitored, and average height at BBCH 59–75 was measured. The prevalence of diseases was scored twice in the growing season. Specifically, the severity of brown rust, yellow rust, powdery mildew, and Septoria tritici blotch was assessed. This was performed by grading each individual plot from 1 to 9 for each disease, with 1 representing no disease and 9 a complete infection. The scoring scale follows a logistic progression based on the symptoms of the top three leaves. We used the data from the final scoring for statistical analysis, as the disease severity was usually more important then. At maturity, we harvested each plot with a combine harvester. The harvested grains were dried when needed, weighed a first time, then sorted and cleaned by air and with a sieve cleaner, and subsequently weighted again. We measured specific weight and water content at the plot level using a Dickey-John machine (GAC 2100). Grain yield was subsequently standardized to 15% of humidity. Protein content was measured at the site level with a near-infrared instrument (ProxiMate; Büchi instruments).

opencc-by-4.0May 2022View details →

Data-set of the partial pressure of CO2, dissolved concentrations of CH4, N2O, NO3-, NO2- and NH4+, specific conductivity and water temperature in the rivers and streams of the Napo River basin in Ecuador (2018, 2019, 2020, 2021)

Data-set consists of two files: - data_ghgs.xlsx : Time-stamped and georeferenced data-set of the partial pressure of CO2 (pCO2 in ppm), dissolved CH4 concentration (CH4 in nmol/L), dissolved N2O concentration (N2O in nmol/L), specific (Sp.) conductivity (in µS/cm), water temperature (in °C), dissolved nitrate concentration (NO3- in µmol/L), dissolved nitrite concentration (NO2- in µmol/L), and dissolved ammonia concentration (NH4+ in µmol/L) in the rivers and streams of the Napo River basin in Ecuador (October 2018 and 2019, January 2019 and 2020, April 2019 and 2021, July 2019 and 2020). Gas measurements were made by headspace equilibration directly in the field with a infra-red gas analyser for CO2 and in the lab with a gas chromatograph for CH4 and N2O. NO3-, NO2- and NH4+ were measured with standard colometric procedures. Sampling and analytical protocols are provided here <a href="https://doi.org/10.5194/bg-16-3801-2019">https://doi.org/10.5194/bg-16-3801-2019</a> - RiverATLAS.xlsx: hydro-environmental data for the sampled streams extracted from RiverATLAS (https://www.nature.com/articles/s41597-019-0300-6). Data codes and units are available here: https://data.hydrosheds.org/file/technical-documentation/HydroATLAS_TechDoc_v10_1.pdf First column of each of the two files provides station ID allowing to merge both data-sets.

Inventories of landslides triggered by the 2019 Cotabato - Davao del Sur (Philippines) seismic sequence

These files are related to the paper “Environmental effects following a seismic sequence: the 2019 Cotabato - Davao del Sur (Philippines) earthquakes” by Ferrario M. F., Perez J., Livio F., Rimando J., Michetti A. M., currently under review. Files include a multi-temporal inventory of landslides triggered during a seismic sequence. In October – December 2019, the Cotabato and Davao del Sur Provinces (Philippine) were hit by four Mw > 6.0 earthquakes. The sequence started with a Mw 6.4 on 16 October (EQ1), then three earthquakes with Mw of 6.6, 6.5 and 6.8 occurred on 29 October (EQ2), 31 October (EQ3) and 15 December 2019 (EQ4). Landslides were manually mapped on 3-m resolution PlanetScope images. Shapefiles are in WGS84 UTM Zone 51n and include: <ul> <li>Area study: shapefile of the study area (inventories 2 and 3)</li> <li>Area study_October 2019: shapefile of the study area for inventory 1</li> <li>Landslides_ott2019: polygonal inventory of landslides mapped on images acquired on 24 and 28/10/2019, that is after EQ1</li> <li>Landslides_nov2019: polygonal inventory of landslides mapped on images acquired on 15 and 16/11/2019, that is after EQ3</li> <li>Landslides_dic2019: polygonal inventory of landslides mapped on images acquired on 05/02/2020, that is after EQ4</li> </ul>

List of crowd accidents from 1900 to 2019

This list contains information about crowd accidents that occurred worldwide between 1900 and 2019. The files included in this dataset are described as follows: <ul> <li>accident_data_raw.csv - CSV file containing information for all accidents as given in the header. Date, country or location are provided using commonly used standard. Information such as fatalities or the number of people injured are reported using expressions found in the employed references (for example "a dozens of people" is given as "dozens"). References are given in the form of text files as reported below.</li> <li>references.zip - ZIP file containing the references used to collect information on each accident. The date of the accident is used as filename (and reported in the last column of the list above). Sources for each accident are given on each line of the relative text file.</li> <li>accident_data_numeric.csv - CSV file containing only numeric values corresponding to word expressions. The conversion scheme used here is provided below. Alternative approaches are possible.</li> <li>number_conversion.csv - CSV file providing the conversion scheme used to convert word expressions into number.</li> </ul>

opencc-by-4.0Dec 2022View details →

Text-fig. 1. Lithostratigraphic scheme for Most Basin (edited according to Kvaček et al. 2019 and Hilgen et al. 2012) with fossiliferous horizons of Holešice and Libkovice members in its west part. Stratigraphic position of fish remnants is highlighted. in New Ichthyofauna From The Holešice And Libkovice Members In The Western Part Of Most Basin (Early Miocene), The Czech Republic

Text-fig. 1. Lithostratigraphic scheme for Most Basin (edited according to Kvaček et al. 2019 and Hilgen et al. 2012) with fossiliferous horizons of Holešice and Libkovice members in its west part. Stratigraphic position of fish remnants is highlighted.

Normalized daily average Schumann resonance (SR) intensity data from 13 to 31 January, 2019

Data are provided for the whole globe (T), South America (SA), Africa (AF) and Asia (AS). File prepared by Tamás Bozóki (20 January, 2023) Contact: bozoki.tamas@epss.hu

Dataset associated with Senf et al. (2023): "How the extreme 2019-2020 Australian wildfire affected global circulation and adjustments"

This contains data aggregates derived from global ECHAM-HAM simulation for the study for effects due to the extreme Australian wildfire event 2019/2020. This data build the basis for analysis and figures in Senf et al. (2023) submitted to ACP.   Simulation Data are <ul> <li>available for freely running ensembles (36 member) and nudged simulations</li> <li>conducted for fire emissions artificially scaled with factors 0, 1, 2, 3, 5.</li> <li>stored for Jan - Mar 2020</li> </ul>

VLT/MUSE observations of Neptune from 2019.

Reduced cubes of Neptune for VLT/MUSE observations made in 2019. Data set includes both the raw reduced cubes and also the deconvolved observations, which have better spatial resolution and include the geometric projection, i.e., lat/long/zenith angles are assigned.

opencc-by-4.0Feb 2023View details →

Dissolved trace metal depth profile concentrations from the North Pacific Ocean Gradients 3 (KM1906) spring 2019 cruise

This dataset contains dissolved trace metal depth profiles collected in the North Pacific Ocean in spring of 2019 on the KM1906 Gradients 3 cruise (Chief Scientist E. Virginia Armbrust). The project was funded by the Simons Collaboration on Ocean Processes and Ecology Gradients program (SCOPE award 426570SP to SGJ). The samples were collected by trace metal clean rosette. Data were produced at the University of Southern California Marine Trace Elements Lab run by Prof. Seth G. John. Briefly, the metals were preconcentrated using an Elemental Scientific Inc. SeaFast automated robot, detected by Element 2 ICP-MS and quantified using isotope dilution as described in Hawco et al. 2020. GCA <a href="https://doi.org/10.1016/j.gca.2020.05.005">https://doi.org/10.1016/j.gca.2020.05.005</a>. All concentrations are dissolved (<0.2um SUPOR filter) and reported as nmol/L.

opencc-by-4.0Jun 2019View details →

FIGURE 1 in ANATOLY BOBROV, YANGMIN QIN & RICHARD J. PAYNE (2019) A new testate amoebae species Planhoogenraadia wuchanica sp. nov. from subtropical forest soils in Wuhan, central China Zootaxa, 4550: 289-294.

FIGURE 1. Outline of Planhoogenraadia wuchanica sp. nov. (1A, 1B) in ventral and lateral views. (1–6 characters of the shell measured (see Table 1). Scale bar is 50 µm.

opencc-by-4.0Mar 2023View details →

Data for: Gray Catbird (Dumetella carolinensis) and Common Yellowthroat (Geothlypis trichas) arrival in NE Pennsylvania, 2004 - 2019, 2021

Three datasets used to look for long-term changes in precipitation and temperature, arrival timing by species, sex and age as well as capture rates and rates of mass change by age or sex in Gray Catbirds and Common Yellowthroats using habitat in and around Lackawanna State Park, northeastern Pennsylvania during the spring migratory period (April and May), The file called meteorological_data_share.csv contains daily precipitation and average temperature data (2004 - 2021) from the Scranton/Wilkes Barre Airport (in Avoca, Pennsylvania, USA), the file called bird_data_share.csv contains first capture date within year, band number, minutes between sunrise and weigh time, mass, age and sex for Gray Catbirds and Common Yellowthroats (2004 - 2019, 2021) and a file called capture_rates_share.csv contains daily capture rates (birds captured per 100 net hours) by species, age and sex for both Gray Catbirds and Common Yellowthroats for (2004 - 2019, 2021).

opencc-by-4.0Mar 2023View details →

Investigating the geometrical and optical properties of the persistent stratospheric aerosol layer observed over a Southern European lidar station during 2019

Simulted aerosol extinction at 550nm profiles over Thessaloniki by the IFS-CB05-BASCOE-GLOMAP system.

opencc-by-4.0Apr 2023View details →

Change in Antarctic Ice Shelf Area from 2009 to 2019

Antarctic Ice Shelves provide buttressing support to the ice sheet, stabilising the flow of grounded ice and its contribution to global sea levels. Over the past 50-years satellite observations have shown ice shelves collapse, thin and retreat, however, there are few measurements of the Antarctic wide change in ice shelf area. Here, we use MODIS satellite data to measure the change in ice shelf calving front position and area on 34 ice shelves in Antarctica, from 2009 to 2019. Over the last decade, a reduction in area on the Antarctic Peninsula (6,692.5 km2) and West Antarctica (5,563.1 km2), has been outweighed by area growth in East Antarctica (3,532.1 km2) and the large Ross and Ronne-Filchner Ice Shelves (14,027.9 km2). The largest retreat was observed on Larsen-C Ice Shelf where 5,916.6 km2 of ice was lost during an individual calving event in 2017, and the largest area increase was observed on Ronne Ice Shelf in East Antarctica, where gradual advance over the past decade (535.3 km2/yr) led to a 5,888.6 km2 area gain from 2009–2019. Overall, the Antarctic Ice Shelf area has grown by 5,304.5 km2 since 2009, with 18 ice shelves retreating and 16 larger shelves growing in area. Our observations show that Antarctic Ice Shelves gained 660.6 Gt of ice mass over the decade whereas the steady state approach would estimate substantial ice loss over the same period, demonstrating the importance of using time-variable calving flux observations to measure change.

SuperDARN Grid data in netCDF format (2019-Nov)

2019-Nov SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)Recommended form of acknowledgement for the use of SuperDARN data:'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'

SuperDARN Grid data in netCDF format (2019-Sep)

2019-Sep SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)Recommended form of acknowledgement for the use of SuperDARN data:'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'

SuperDARN Grid data in netCDF format (2019-May)

2019-May SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)Recommended form of acknowledgement for the use of SuperDARN data:'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'

SuperDARN Grid data in netCDF format (2019-Mar)

2019-Mar SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)Recommended form of acknowledgement for the use of SuperDARN data:'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'

SuperDARN Grid data in netCDF format (2019-Apr)

2019-Apr SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)Recommended form of acknowledgement for the use of SuperDARN data:'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'