Find research datasets worth reusing

opencc-by-4.0Dec 2023View details →

Data of monthly climate variables and drought indices within continental Chile for 1981-2023

The dataset contains derived climatic data from ERA-5 at monthly frequency for continental Chile. The variables are:<ul><li>Precipitation (pre)</li><li>Minimum temperature (tas_min)</li><li>Mean temperature (tas)</li><li>Maximum temperature (tas_max)</li><li>Reference evapotranspiration (pet)</li><li>Snow water eqivalent (swe)</li><li>Soil volumetric water content at 1m depth (sm)</li></ul>Besides, the dataset contains the follwoing derived drough indices:<ul><li>Standardized Precipitation Index (SPI) for 1, 3, 6, 12, 24, and 36 months (spi_1 to spi_36)</li><li>Standardized Precipitation Evapotranspiration Index (SPEI) for 1, 3, 6, 12, 24, and 36 months (spei_1 to spei_36)</li><li>Evaporative Demand Drought Index (EDDI) for 1, 3, 6, 12, 24, and 36 months (eddi_1 to eddi_36)</li><li>Anomaly of cumulative soil moisture at 1m depth (zcSM) for 1, 3, 6, 12, 24, and 36 months (zcsm_1 to zcsm_36)</li><li>Anomaly of cumulative NDVI (zcNDVI) for 1, 3, and 6 months (zcndvi1 to zcndvi6)</li><li>Snow Water Equivalent Index (SWEI)</li></ul>

opencc-by-4.0Apr 2024View details →

Daily flood discharge dataset for 10 basins in the Third Pole during 1981‒2100

This dataset describes the daily discharge during each river flood event in the 10 Third Pole basins (Indus, Yamuna, Upper Ganges, MahaKali, Karnali, Gandaki, Koshi, Brahmaputra, Salween, and Mekong) in the historical (1981‒2020) and future (2021‒2100). This was achieved using a hybrid model encompassing a validated physical model (Water and Energy Budget-based Distributed Hydrological Model, WEB-DHM) and deep-learning model (<a name="_Hlk153977673"></a>Long Short-Term Memory model, LSTM) with the latest climate projections. River flood events are defined using both the annual-maximum approach and peak-over-threshold (POT) approach. Details for identification of the annual-maximum and POT flood events are described in the file “readme.txt”. Future data was generated based on the climate projections from five climate models (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL) in phase 6 of the Coupled Model Intercomparison Project (CMIP6) under two shared socio-economic pathway scenarios (SSP) (a high-emission scenario of SSP585 and a low-emission scenario of SSP245). The unit of discharge is m3/s.

opencc-by-4.0Oct 2022View details →

A global historical twice-daily (daytime and nighttime) land surface temperature dataset produced by AVHRR observations from 1981 to 2021 (1981–2000)

<ul> <li>Land surface temperature (LST) is a key variable for monitoring and evaluating global long-term climate change. However, existing satellite-based twice-daily LST products only date back to 2000, which makes it difficult to obtain robust long-term temperature variations. We developed the first global historical twice-daily LST dataset (GT-LST), with a spatial resolution of 0.05°, using Advanced Very High Resolution Radiometer Level-1b Global Area Coverage data from 1981 to 2021.</li> <li>Validation with in situ measurements from Surface Radiation Budget sites showed that the overall root-mean-square errors of GT-LST varied from 2.0 K to 3.9 K. Inter-comparison with a common LST product (i.e., MYD11A1) revealed that the overall root-mean-square-difference was approximately 3.2 K.</li> <li>More details of this dataset can be seen in readme.pdf.</li> <li>This dataset provides GT-LST product from 1981 to 2000.</li> </ul>

opencc-by-4.0Oct 2022View details →

A global historical twice-daily (daytime and nighttime) land surface temperature dataset produced by AVHRR observations from 1981 to 2021 (2001–2005)

Figs. 5–10 in Life history of Ameletus longulus Sinichenkova, 1981 (Ephemeroptera: Ameletidae) in a small stream in vicinity of Vladivostok

Figs. 5–10. Gradations the pads of wings of the different age groups of Ameletus longulus

Fig. 4 in Life history of Ameletus longulus Sinichenkova, 1981 (Ephemeroptera: Ameletidae) in a small stream in vicinity of Vladivostok

Fig. 4. The age structure of the Ameletus longulus population in the spring of Rybachii

Fig. 2 in Life history of Ameletus longulus Sinichenkova, 1981 (Ephemeroptera: Ameletidae) in a small stream in vicinity of Vladivostok

Fig. 2. The sampling site on the Rybachii village spring.

Fig. 3 in Life history of Ameletus longulus Sinichenkova, 1981 (Ephemeroptera: Ameletidae) in a small stream in vicinity of Vladivostok

Fig. 3. Dynamics of water temperature and body length range of Ameletus longulus in the

Fig. 1 in Life history of Ameletus longulus Sinichenkova, 1981 (Ephemeroptera: Ameletidae) in a small stream in vicinity of Vladivostok

Fig. 1. The map of the distribution of the Ameletus longulus in the Russian Far East (● –

opencc-byOct 2020View details →

Placa en honor a Largo Caballero (1981), J. Noja

La placa de la Plaza de Chamberí en homenaje a Largo Caballero fue colocada en marzo de 1981, siendo alcalde Enrique Tierno Galván, y está situada en la fachada de la Junta Municipal de Distrito, inmueble que sustituyó a la casa natal del histórico dirigente defensor de las clases trabajadoras. La obra fue realizada por el escultor José Noja Ortega, también autor de la estatua del propio Largo en Nuevos Ministerios, y de otras placas, como las dedicadas a Julián Besteiro o a Pablo Neruda. Francisco Largo Caballero fue ministro de Trabajo entre 1931 y 1933, secretario general de UGT entre 1918 y 1937 y presidente de este sindicato en 1934. También fue presidente del Gobierno y estuvo al frente del Ministerio de Guerra al inicio de la Guerra Civil. Murió exiliado en París, en el año 1946. Hoy, día 15 de octubre, la placa ha sido retirada por el Ayuntamiento de Madrid: https://www.publico.es/politica/ayuntamiento-madrid-retira-placa-honor-caballero-151-aniversario-nacimiento.html Source: Objaverse 1.0 / Sketchfab

Fig. 3 in Lyonsia vniroi Scarlato, 1981 (Bivalvia: Lyonsiidae)

Fig. 3. Geographic distribution of L. vniroi after Scarlato [1981, p. 62, fig. 44] with additions.

Fig. 1. A in Lyonsia vniroi Scarlato, 1981 (Bivalvia: Lyonsiidae)

Fig. 1. A catalogue card of the ZIN with data on the type series of Lyonsia vniroi Scarlato, 1981.

opencc-by-4.0Sep 2021View details →

Data and Code for "Climate impacts and adaptation in US dairy systems 1981-2018"

This data and code archive provides all the files that are necessary to replicate the empirical analyses that are presented in the paper "Climate impacts and adaptation in US dairy systems 1981-2018" authored by Maria Gisbert-Queral, Arne Henningsen, Bo Markussen, Meredith T. Niles, Ermias Kebreab, Angela J. Rigden, and Nathaniel D. Mueller and published in 'Nature Food' (2021, DOI: <a href="https://doi.org/10.1038/s43016-021-00372-z">10.1038/s43016-021-00372-z</a>). The empirical analyses are entirely conducted with the "R" statistical software using the add-on packages "car", "data.table", "dplyr", "ggplot2", "grid", "gridExtra", "lmtest", "lubridate", "magrittr", "nlme", "OneR", "plyr", "pracma", "quadprog", "readxl", "sandwich", "tidyr", "usfertilizer", and "usmap". The R code was written by Maria Gisbert-Queral and Arne Henningsen with assistance from Bo Markussen. Some parts of the data preparation and the analyses require substantial amounts of memory (RAM) and computational power (CPU). Running the entire analysis (all R scripts consecutively) on a laptop computer with 32 GB physical memory (RAM), 16 GB swap memory, an 8-core Intel Xeon CPU E3-1505M @ 3.00 GHz, and a GNU/Linux/Ubuntu operating system takes around 11 hours. Running some parts in parallel can speed up the computations but bears the risk that the computations terminate when two or more memory-demanding computations are executed at the same time. This data and code archive contains the following files and folders: * README Description: text file with this description * flowchart.pdf Description: a PDF file with a flow chart that illustrates how R scripts transform the raw data files to files that contain generated data sets and intermediate results and, finally, to the tables and figures that are presented in the paper. * runAll.sh Description: a (bash) shell script that runs all R scripts in this data and code archive sequentially and in a suitable order (on computers with a "bash" shell such as most computers with MacOS, GNU/Linux, or Unix operating systems) * Folder "DataRaw" Description: folder for raw data files This folder contains the following files: - DataRaw/COWS.xlsx Description: MS-Excel file with the number of cows per county Source: USDA NASS Quickstats Observations: All available counties and years from 2002 to 2012 - DataRaw/milk_state.xlsx Description: MS-Excel file with average monthly milk yields per cow Source: USDA NASS Quickstats Observations: All available states from 1981 to 2018 - DataRaw/TMAX.csv Description: CSV file with daily maximum temperatures Source: PRISM Climate Group (spatially averaged) Observations: All counties from 1981 to 2018 - DataRaw/VPD.csv Description: CSV file with daily maximum vapor pressure deficits Source: PRISM Climate Group (spatially averaged) Observations: All counties from 1981 to 2018 - DataRaw/countynamesandID.csv Description: CSV file with county names, state FIPS codes, and county FIPS codes Source: US Census Bureau Observations: All counties - DataRaw/statecentroids.csv Descriptions: CSV file with latitudes and longitudes of state centroids Source: Generated by Nathan Mueller from Matlab state shapefiles using the Matlab "centroid" function Observations: All states * Folder "DataGenerated" Description: folder for data sets that are generated by the R scripts in this data and code archive. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these generated data files so that parts of the analysis can be replicated (e.g., on computers with insufficient memory to run all parts of the analysis). * Folder "Results" Description: folder for intermediate results that are generated by the R scripts in this data and code archive. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these intermediate results so that parts of the analysis can be replicated (e.g., on computers with insufficient memory to run all parts of the analysis). * Folder "Figures" Description: folder for the figures that are generated by the R scripts in this data and code archive and that are presented in our paper. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these figures so that people who replicate our analysis can more easily compare the figures that they get with the figures that are presented in our paper. Additionally, this folder contains CSV files with the data that are required to reproduce the figures. * Folder "Tables" Description: folder for the tables that are generated by the R scripts in this data and code archive and that are presented in our paper. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these tables so that people who replicate our analysis can more easily compare the tables that they get with the tables that are presented in our paper. * Folder "logFiles" Description: the shell script runAll.sh writes the output of each R script that it runs into this folder. We provide these log files so that people who replicate our analysis can more easily compare the R output that they get with the R output that we got. * PrepareCowsData.R Description: R script that imports the raw data set COWS.xlsx and prepares it for the further analyses * PrepareWeatherData.R Description: R script that imports the raw data sets TMAX.csv, VPD.csv, and countynamesandID.csv, merges these three data sets, and prepares the data for the further analyses * PrepareMilkData.R Description: R script that imports the raw data set milk_state.xlsx and prepares it for the further analyses * CalcFrequenciesTHI_Temp.R Description: R script that calculates the frequencies of days with the different THI bins and the different temperature bins in each month for each state * CalcAvgTHI.R Description: R script that calculates the average THI in each state * PreparePanelTHI.R Description: R script that creates a state-month panel/longitudinal data set with exposure to the different THI bins * PreparePanelTemp.R Description: R script that creates a state-month panel/longitudinal data set with exposure to the different temperature bins * PreparePanelFinal.R Description: R script that creates the state-month panel/longitudinal data set with all variables (e.g., THI bins, temperature bins, milk yield) that are used in our statistical analyses * EstimateTrendsTHI.R Description: R script that estimates the trends of the frequencies of the different THI bins within our sampling period for each state in our data set * EstimateModels.R Description: R script that estimates all model specifications that are used for generating results that are presented in the paper or for comparing or testing different model specifications * CalcCoefStateYear.R Description: R script that calculates the effects of each THI bin on the milk yield for all combinations of states and years based on our 'final' model specification * SearchWeightMonths.R Description: R script that estimates our 'final' model specification with different values of the weight of the temporal component relative to the weight of the spatial component in the temporally and spatially correlated error term * TestModelSpec.R Description: R script that applies Wald tests and Likelihood-Ratio tests to compare different model specifications and creates Table S10 * CreateFigure1a.R Description: R script that creates subfigure a of Figure 1 * CreateFigure1b.R Description: R script that creates subfigure b of Figure 1 * CreateFigure2a.R Description: R script that creates subfigure a of Figure 2 * CreateFigure2b.R Description: R script that creates subfigure b of Figure 2 * CreateFigure2c.R Description: R script that creates subfigure c of Figure 2 * CreateFigure3.R Description: R script that creates the subfigures of Figure 3 * CreateFigure4.R Description: R script that creates the subfigures of Figure 4 * CreateFigure5_TableS6.R Description: R script that creates the subfigures of Figure 5 and Table S6 * CreateFigureS1.R Description: R script that creates Figure S1 * CreateFigureS2.R Description: R script that creates Figure S2 * CreateTableS2_S3_S7.R Description: R script that creates Tables S2, S3, and S7 * CreateTableS4_S5.R Description: R script that creates Tables S4 and S5 * CreateTableS8.R Description: R script that creates Table S8 * CreateTableS9.R Description: R script that creates Table S9

opencc-by-4.0Apr 2021View details →

GLOBMAP global Leaf Area Index since 1981

GLOBMAP LAI (Version 3) provides a consistent long-term global leaf area index (LAI) product (1981-2020, continuously updated) at 8km resolution on Geographic grid by quantitative fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and historical Advanced Very High Resolution Radiometer (AVHRR) data. The long-term LAI series was made up by combination of AVHRR LAI (1981–2000) and MODIS LAI (2001–). MODIS LAI series was generated from MODIS land surface reflectance data (MOD09A1 C6) based on the GLOBCARBON LAI algorithm (Deng et al., 2006). The relationships between AVHRR observations (GIMMS NDVI (Tucker et al., 2005)) and MODIS LAI were established pixel by pixel using two data series during overlapped period (2000–2006). Then the AVHRR LAI back to 1981 was estimated from historical AVHRR observations based on these pixel-level relationships. Detailed descriptions of algorithm and evaluation of the algorithm see Liu et al. (2012, JGR-B). Several changes have been made compared with the JGR paper: <ol> <li>The MODIS C6 land surface reflectance products MOD09A1 was used to generate MODIS LAI in this GLOBMAP V3 products instead of C5 products.</li> <li>The clumping effects was considered at the pixel level by employing global clumping index map at 500m resolution (He et al., 2012) instead of land cover-specific clumping index in generation of MODIS LAI. And the new pixel-based AVHRR SR-MODIS LAI relationships were established based on these MODIS LAI series and used for AVHRR LAI retrieval.</li> <li>The cloud mask for MOD09A1 data were generated by a new cloud detection algorithm based on time series surface reflectance observations (Liu and Liu, 2013). And the contaminated pixels were filled by locally adjusted cubic spline capping approach (Chen et al., 2006).</li> </ol> Dataset Characteristics:  Spatial Coverage: 180ºW~180ºE, 63ºS~90ºN;  Temporal Coverage: July, 1981-Dec. 2020 (continuously updated);  Spatial Resolution: 0.0727273º;  Temporal Resolution: Half month (1981-2000), 8-day (2001-);  Projection: Geographic;  Data Format: HDF/Geotiff;  Scale: 0.01;  Valid Range: 0-1000. Citation (Please cite this paper whenever these data are used): Liu, Y., R. Liu, and J. M. Chen (2012), Retrospective retrieval of long-term consistent global leaf area index (1981–2011) from combined AVHRR and MODIS data, J. Geophys. Res., 117, G04003, doi:10.1029/2012JG002084. If you have any questions, please contact Prof. Ronggao Liu (liurg@igsnrr.ac.cn) or Dr. Yang Liu (liuyang@igsnrr.ac.cn). Related publications with this dataset:  <ol> <li>Chen, J. M., F. Deng, and M. Chen (2006), Locally adjusted cubic-spline capping for reconstructing seasonal trajectories of a satellite-derived surface parameter, IEEE Trans. Geosci. Remote Sens., 44, 2230-2238</li> <li>Deng, F., J. M. Chen, S. Plummer, M. Z. Chen, and J. Pisek (2006), Algorithm for global leaf area index retrieval using satellite imagery, IEEE Trans. Geosci. Remote Sens., 44(8), 2219–2229.</li> <li>He, L. M., J. M. Chen, J. Pisek, C. B. Schaaf, and A. H. Strahler (2012), Global clumping index map derived from the MODIS BRDF product, Remote Sens. Environ., 119, 118-130.</li> <li>Liu, R. G., and Y. Liu (2013), Generation of new cloud masks from MODIS land surface reflectance products, Remote Sens. Environ., 133, 21-37.</li> <li>Tucker, C. J., J. E. Pinzon, M. E. Brown, D. A. Slayback, E. W. Pak,R. Mahoney, E. F. Vermote, and N. El Saleous (2005), An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data, Int. J. Remote Sens., 26(20), 4485–4498.</li> </ol>

Рис. 1. КаталожнаЯ карточка ЗИН РАН типовой серии Lyonsia vniroi Scarlato, 1981. in Lyonsia vniroi Scarlato, 1981 (Bivalvia: Lyonsiidae)

Рис. 1. КаталожнаЯ карточка ЗИН РАН типовой серии Lyonsia vniroi Scarlato, 1981.

Рис. 3. Ареал L. vniroi по О.А. Скарлато [1981, с. 62, рис. 44] с дополнениЯми. in Lyonsia vniroi Scarlato, 1981 (Bivalvia: Lyonsiidae)

Рис. 3. Ареал L. vniroi по О.А. Скарлато [1981, с. 62, рис. 44] с дополнениЯми.

opencc-by-4.0Nov 2022View details →

Figs. 20 in Nuevos datos sobre Stenosis oteroi Español, 1981 (Coleoptera: Tenebrionidae) en Galicia

Figs. 20.- Hábitat de Stenosis oteroi en la playa de Area Maior (Muros).

opencc-by-4.0Nov 2022View details →

Figs. 7-8 in Nuevos datos sobre Stenosis oteroi Español, 1981 (Coleoptera: Tenebrionidae) en Galicia

Figs. 7-8.- Stenosis oteroi.

opencc-by-4.0Nov 2022View details →

Fig. 18 in Nuevos datos sobre Stenosis oteroi Español, 1981 (Coleoptera: Tenebrionidae) en Galicia

Fig. 18.- Stenosis oteroi depositado en el MCNB.