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225 results for “Global database”
Merged Global Pluvial Floods DataBase (Input Data)
<p>This repository contains the input data used in the Jupyter notebook downloadable from Github <a href="https://github.com/FatimaPillosu/Merged_Global_Pluvial_Floods_DataBase">here</a>.</p> <p>Such input data consists in two main datasets:</p> <ul> <li>A raw (global and regional) flood reports from four different databases.</li> <li>A global daily rainfall analysis from NOAA (CPC_GLOBAL_PRCP_v1.0).</li> </ul> <p>The Jupyter notebook runs a Python code that post-processes the raw flood reports, using information extracted from other datasets, to select some reports of interest (mainly regarding pluvial and flash floods). At a later stage, such reports are merged into a single database for global pluvial/flash flood reports. The Jupyter notebook also runs a Metview-Python code to visualize partial and final results as map plots.</p> <p>The four original databases are:</p> <ol> <li>FloodList, FL (Global domain): <a href="http://floodlist.com/">http://floodlist.com/</a></li> <li>Emergency Events Database, EMDAT (Global domain): <a href="https://www.emdat.be/">https://www.emdat.be/</a></li> <li>European Severe Weather Database, ESWD (Europe): <a href="https://www.essl.org/cms/european-severe-weather-database/">https://www.essl.org/cms/european-severe-weather-database/</a></li> <li>Storm Events Database, SED (USA): <a href="https://www.ncdc.noaa.gov/stormevents/">https://www.ncdc.noaa.gov/stormevents/</a></li> </ol> <p><em>NOTE: </em>For more details about these databases (documentation, licenses, etc.), look at the README.md file.</p> <p><em>NOTE: </em>The data in this repository is intended for an exclusive NON-COMMERCIAL academic or personal use, and it is released under the Creative Commons Attribution-ShareAlike 4.0 International Public License. For more information, look at the LICENSE.md file.</p>
The BenBioDen database, a global database for meio-, macro- and megabenthic biomass and densities
<p>Benthic fauna refers to all fauna that live in or on the seafloor, which researchers typically divide into size classes meiobenthos (32/ 64 µm – 0.5/ 1 mm), macrobenthos (250 µm – 1 cm), and megabenthos (> 1 cm). Benthic fauna play important roles in bioturbation activity, mineralization of organic matter, and in marine food webs. Evaluating their role in these ecosystem functions requires knowledge of their global distribution and biomass. We therefore established the BenBioDen database, the largest open-access database for marine benthic biomass and density data compiled so far. In total, it includes 11,792 georeferenced benthic biomass and 51,559 benthic density records from 384 and 600 studies, respectively. We selected all references following the procedure for systematic reviews and meta-analyses, and report biomass records as grams of wet mass, dry mass, or ash-free dry mass, or carbon per m<sup>2</sup> and as abundance records as individuals per m<sup>2</sup>. This database provides a point of reference for future studies on the distribution and biomass of benthic fauna.</p>
SAPFLUXNET: A global database of sap flow measurements
<p><strong>General description</strong></p> <p>SAPFLUXNET contains a global database of sap flow and environmental data, together with metadata at different levels.<br> SAPFLUXNET is a harmonised database, compiled from contributions from researchers worldwide. </p> <p>The SAPFLUXNET version 0.1.5 database harbours 202 globally distributed datasets, from 121 geographical locations. SAPFLUXNET contains sap flow data for 2714 individual plants (1584 angiosperms and 1130 gymnosperms), belonging to 174 species (141 angiosperms and 33 gymnosperms), 95 different genera and 45 different families. More information on the database coverage can be found here: <a href="http://sapfluxnet.creaf.cat/shiny/sfn_progress_dashboard/">http://sapfluxnet.creaf.cat/shiny/sfn_progress_dashboard/</a>. </p> <p><br> The SAPFLUXNET project has been developed by researchers at CREAF and other institutions (<a href="http://sapfluxnet.creaf.cat/team/">http://sapfluxnet.creaf.cat/#team</a>), coordinated by Rafael Poyatos (CREAF, <a href="http://www.creaf.cat/staff/rafael-poyatos-lopez">http://www.creaf.cat/staff/rafael-poyatos-lopez</a>), and funded by two Spanish Young Researcher's Grants (SAPFLUXNET, CGL2014-55883-JIN; DATAFORUSE, RTI2018-095297-J-I00 ) and an Alexander von Humboldt Research Fellowship for Experienced Researchers).</p> <p><strong>Changelog </strong></p> <p>Compared to version 0.1.4, this version includes some changes in the metadata, but all time series data (sap flow, environmental) remain the same. </p> <ul> <li>For all datasets, climate metadata (temperature and precipitation, ‘si_mat’ and ‘si_map’) have been extracted from CHELSA (https://chelsa-climate.org/), replacing the previous climate data obtained with Wordclim. This change has modified the biome classification of the datasets in ‘si_biome’.</li> <li>In ‘species’ metadata, the percentage of basal area with sap flow measurements for each species (‘sp_basal_area_perc’) is now assigned a value of 0 if species are in the understorey. This affects two datasets: AUS_MAR_UBD and AUS_MAR_UBW, where, previously, the sum of species basal area percentages could add up to more than 100%.</li> <li>In ‘species’ metadata, the percentage of basal area with sap flow measurements for each species (‘sp_basal_area_perc’) has been corrected for datasets USA_SIL_OAK_POS, USA_SIL_OAK_1PR, USA_SIL_OAK_2PR.</li> <li>In ‘site’ metadata, the vegetation type (‘si_igbp’) has been changed to SAV for datasets CHN_ARG_GWD and CHN_ARG_GWS.</li> </ul> <p><strong>Variables and units</strong></p> <p>SAPFLUXNET contains whole-plant sap flow and environmental variables at sub-daily temporal resolution. Both sap flow and environmental time series have accompanying flags in a data frame, one for sap flow and another for environmental<br> variables. These flags store quality issues detected during the quality control process and can be used to add further quality flags. </p> <p>Metadata contain relevant variables informing about site conditions, stand characteristics, tree and species attributes, sap flow methodology and details on environmental measurements. The description and units of all data and metadata variables can be found here: <a href="http://sapfluxnet.creaf.cat/sapfluxnetr/articles/metadata-and-data-units.html">Metadata and data units</a>.</p> <p>To learn more about variables, units and data flags please use the functionalities implemented in the sapfluxnetr package (<a href="https://github.com/sapfluxnet/sapfluxnetr">https://github.com/sapfluxnet/sapfluxnetr</a>). In particular, have a look at the package vignettes using R:</p> <pre><code># remotes::install_github( # 'sapfluxnet/sapfluxnetr', # build_opts = c("--no-resave-data", "--no-manual", "--build-vignettes") # ) library(sapfluxnetr) # to list all vignettes vignette(package='sapfluxnetr') # variables and units vignette('metadata-and-data-units', package='sapfluxnetr') # data flags vignette('data-flags', package='sapfluxnetr')</code></pre> <p><strong>Data formats</strong></p> <p>SAPFLUXNET data can be found in two formats: 1) RData files belonging to the custom-built 'sfn_data' class and 2) Text files in .csv format. We recommend using the sfn_data objects together with the sapfluxnetr package, although we also provide the text files for convenience. For each dataset, text files are structured in the same way as the slots of sfn_data objects; if working with text files, we recommend that you check the data structure of 'sfn_data' objects in the <a href="http://sapfluxnet.creaf.cat/sapfluxnetr/articles/sfn-data-classes.html">corresponding vignette</a>.</p> <p><strong>Working with sfn_data files</strong></p> <p>To work with SAPFLUXNET data, first they have to be downloaded from Zenodo, maintaining the folder structure. A first level in the folder hierarchy corresponds to file format, either RData files or csv's. A second level corresponds to how sap flow is expressed: per plant, per sapwood area or per leaf area. Please note that interconversions among the magnitudes have been performed whenever possible. Below this level, data have been organised per dataset. In the case of RData files, each dataset is contained in a sfn_data object, which stores all data and metadata in different slots (see the vignette 'sfn-data-classes'). In the case of csv files, each dataset has 9 individual files, corresponding to metadata (5), sap flow and environmental data (2) and their corresponding data flags (2).</p> <p>After downloading the entire database, the sapfluxnetr package can be used to:<br> - Work with data from a single site: data access, plotting and time aggregation.<br> - Select the subset datasets to work with.<br> - Work with data from multiple sites: data access, plotting and time aggregation.</p> <p>Please check the following package vignettes to learn more about how to work with sfn_data files:</p> <p><a href="http://sapfluxnet.creaf.cat/sapfluxnetr/articles/sapfluxnetr-quick-guide.html">Quick guide</a></p> <p><a href="http://sapfluxnet.creaf.cat/sapfluxnetr/articles/metadata-and-data-units.html">Metadata and data units</a></p> <p><a href="http://sapfluxnet.creaf.cat/sapfluxnetr/articles/sfn-data-classes.html">sfn_data classes</a></p> <p><a href="http://sapfluxnet.creaf.cat/sapfluxnetr/articles/custom-aggregation.html">Custom aggregation</a></p> <p><a href="http://sapfluxnet.creaf.cat/sapfluxnetr/articles/memory-and-parallelization.html">Memory and parallelization</a></p> <p><strong>Working with text files</strong></p> <p>We recommend to work with sfn_data objects using R and the sapfluxnetr package and we do not currently provide code to work with text files. </p> <p><strong>Data issues and reporting</strong></p> <p>Please report any issue you may find in the database by sending us an email: sapfluxnet@creaf.uab.cat.</p> <p>Temporary data fixes, detected but not yet included in released versions will be published in SAPFLUXNET main web page ('Known data errors').</p> <p><strong>Data access, use and citation</strong></p> <p>This version of the SAPFLUXNET database is open access and corresponds to the data paper submitted to Earth System Science Data in August 2020.</p> <p>When using SAPFLUXNET data in an academic work, please cite the data paper, when available, or alternatively, the Zenodo dataset (see the ‘Cite as’ section on the right panels of this web page).</p>
Database of global glendonite and ikaite records throughout the Phanerozoic
<p>Here database of Phanerozoic occurrences and isotopic characteristics of metastable cold-water calcium carbonate hexahydrate (ikaite; CaCO<sub>3</sub>·6H<sub>2</sub>O) and its pseudomorphs (glendonites) is represented. Its current version includes 690 records ranged from Cambrian to recent, which based on 323 references, the authors’ data and additional personal communications from the colleagues. Database is provided as Excel2003 (.xls)- file, which consists from 5 sheets. The major sheet ‘Glendonites and latitudes’ includes a list of localities along with latitude / longitude, geologic age (in Ma and substage), palaeolatitude, references, environments and host rock for all records. Three sheets, entitled “Cenozoic glendonite and ikaite”, “Mesozoic glendonite” and “Paleozoic glendonite” includes data concerning carbon and oxygen stable isotope values of, respectively, Cenozoic, Mesozoic and Paleozoic samples. The last sheet “References” contains list of references in APA style, accompanied by DOI numbers and hyperlinks to electronic versions (if available).</p>
Global database of offshore freshened groundwater records
<p>A global database of 305 records of offshore freshened groundwater has been compiled using published information. The criteria for including records in this database include salinities of ≤33 PSU, a location of ≥1 km away from the coast, and a distance of >10 km from other records. </p> <p>This is an outcome of the MARCAN project. This project has received funding from the European Research Council (ERC), under the European Union's Horizon 2020 research and innovation programme (grant agreement No 677898 (MARCAN)).</p>
MOSEV: A global burn severity database from MODIS (2000-2020)
<p>To advance in the fire discipline as well as in the study of CO<sub>2</sub> emissions it is of great interest to develop a global database with estimators of the degree of biomass consumed by fire, which is defined as burn severity. We present the first global burn severity database (MOSEV database), which is based on Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance and burned area (BA) products scenes since November 2000 to near real time. To build the database we combined Terra MOD09A1 and Aqua MYD09A1 surface reflectance products to obtain dense time series of the Normalized Burn Ratio (NBR) spectral index, and we used the MCD64A1 product to identify BA and the date of burning. Then, we calculated for each burned pixel the difference of the NBR (dNBR), and its relativized version (RdNBR), as well as the post-burn NBR which are the most commonly used burn severity spectral indices. The database also includes the pre-burn NBR used for calculations, the date of the pre- and post-burn NBR and the date of burning.</p>
Data from: GlobTherm, a global database on thermal tolerances for aquatic and terrestrial organisms
How climate affects species distributions is a longstanding question receiving renewed interest owing to the need to predict the impacts of global warming on biodiversity. Is climate change forcing species to live near their critical thermal limits? Are these limits likely to change through natural selection? These and other important questions can be addressed with models relating geographical distributions of species with climate data, but inferences made with these models are highly contingent on non-climatic factors such as biotic interactions. Improved understanding of climate change effects on species will require extensive analysis of thermal physiological traits, but such data are scarce and scattered. To overcome current limitations, we created the GlobTherm database. The database contains experimentally derived species' thermal tolerance data currently comprising over 2,000 species of terrestrial, freshwater, intertidal and marine multicellular algae, pl ants, fungi, and animals. The GlobTherm database will be maintained and curated by iDiv with the aim of expanding it, and enable further investigations on the effects of climate on the distribution of life on Earth.
Data from: Global database of paleocurrent trends through the Phanerozoic and Precambrian
Paleocurrents are sedimentological features contained in all sedimentary deposits, enabling the direction of movement of the sediment and the containing fluid at the time of deposition to be determined. This database contains paleocurrent directions and other relevant associated data from published sources and theses and dissertations for the entire Phanerozoic and Precambrian for all continents. Such information may be of general interest to sedimentologists and will be of specific interest in sedimentary basin analysis, and to petroleum geologists and mineralogists seeking source areas. Paleocurrents may also be useful in plate reconstructions and in testing the timing of global tectonic events.
Data from: MarTurtSI, a global database of stable isotope analyses of marine turtles
Marine turtles are both flagship species of conservation concern and indicators of ocean health. As highly migratory species, and despite substantial research effort focusing on nesting females and satellite tagging studies, we still know little about the trophic ecology and habitat use of immature stages and males. Consequently, marine turtle biologists began using stable isotope analyses in the last decade to elucidate various aspects of trophic ecology, including habitat use and trophic position. This has resulted in a burgeoning but largely disconnected literature of mostly single-species case studies. Here we comprehensively synthesize this body of work into a unified data repository, the MarTurtSI database. MarTurtSI contains stable isotope data from six of seven marine turtle species ranging from juveniles to adults, in different developmental, feeding, and breeding habitats across multiple ocean basins. MarTurtSI will be curated and updated with the aim of enabling continued comprehensive and global investigations into the trophic ecology of marine turtles especially in the face of climate change and other conservation challenges.
Data from: MycoDB, a global database of plant response to mycorrhizal fungi
Plants form belowground associations with mycorrhizal fungi in one of the most common symbioses on Earth. However, few large-scale generalizations exist for the structure and function of mycorrhizal symbioses, as the nature of this relationship varies from mutualistic to parasitic and is largely context-dependent. We announce the public release of MycoDB, a database of 4,010 studies (from 438 unique publications) to aid in multi-factor meta-analyses elucidating the ecological and evolutionary context in which mycorrhizal fungi alter plant productivity. Over 10 years with nearly 80 collaborators, we compiled data on the response of plant biomass to mycorrhizal fungal inoculation, including meta-analysis metrics and 24 additional explanatory variables that describe the biotic and abiotic context of each study. We also include phylogenetic trees for all plants and fungi in the database. To our knowledge, MycoDB is the largest ecological meta-analysis database. We aim to share these data to highlight significant gaps in mycorrhizal research and encourage synthesis to explore the ecological and evolutionary generalities that govern mycorrhizal functioning in ecosystems.
Data from: Survey completeness of a global citizen-science database of bird occurrence
<p>Measuring the completeness of survey inventories created by citizen-science initiatives can identify the strengths and shortfalls in our knowledge of where species occur geographically. Here, we use occurrence information from eBird to measure the survey completeness of the world's birds in this database at three temporal resolutions and four spatial resolutions across the annual cycle during the period 2002 to 2018. Approximately 84% of the earth's terrestrial surface contained bird occurrence information with the greatest concentrations occurring in North America, Europe, India, Australia, and New Zealand. The largest regions with low levels of survey completeness were located in central South America, northern and central Africa, and northern Asia. Across spatial and temporal resolutions, survey completeness in regions with occurrence information was 55–74% on average, with the highest values occurring at coarser temporal and coarser spatial resolutions and during spring migration within temperate and boreal regions. Across spatial and temporal resolutions, survey completeness exceeded 90% within <i>ca</i>. 4–14% of the earth's terrestrial surface. Survey completeness increased globally from 2002 to 2018 across all months of the year at a rate of <i>ca</i>. 3% per year. The slowest gains occurred in Africa and in montane regions, and the most rapid gains occurred in India and in tropical forests after 2012. Thus, occurrence information from a global citizen-science program for a charismatic and well-studied taxon was geographically broad but contained heterogeneous patterns of survey completeness that were strongly influenced by temporal and especially spatial resolution. Our results identify regions where the application of additional effort would address current knowledge shortfalls, and regions where the maintenance of existing effort would benefit long-term monitoring efforts. Our findings highlight the potential of citizen science initiatives to further our knowledge of where species occur across space and time, information whose applications under global change will likely increase.</p>
Data from: Global database of matched Plasmodium falciparum and P. vivax incidence and prevalence records from 1985–2013
Measures of clinical incidence are necessary to help estimate the burden of a disease. Incidence is a metric not commonly measured in malariology because the longitudinal surveys required are costly and labour intensive. This database is an effort to collate published incidence records obtained using active case detection for Plasmodium falciparum and Plasmodium vivax malaria. The literature search methods, data abstraction procedures and data processing procedures are described here. A total of 1,680 spatio-temporally unique incidence records were collected for the database: 1,187 for P. falciparum and 493 for P. vivax. These data were gathered to model the relationship between clinical incidence and prevalence of infection and can be used for a variety of modelling exercises including the assessment of change in disease burden in relation to age and control interventions. The subset of data that have been used for such modelling exercises are described and identified.
SoilCompDB: Global soil compressive properties database. Version 1.0
<p><strong>Data collection and processing</strong></p><p>Our data collection comprised published journal articles sourced from Web of Science and Scopus databases, using search terms such as 'soil precompression stress,' 'soil compression index,' 'soil compaction index,' 'soil recompression index,' 'soil swelling index,' 'soil precompaction stress,' and 'preconsolidation pressure' for articles published up to February 2022. A total of 1235 publications were found. Duplicate records were eliminated using the Endnote Web citation management application. The remaining references were exported to Rayyan software for title and abstract screening based on predefined criteria for full-text selection. After a careful review, we identified 128 papers where the data on soil compressive properties (precompression stress, compression index, and swelling index) were reported in numerical format or legible graphical format and considered suitable for inclusion in the database. We employed the WebPlotDigitizer software to extract data from figures within the original publications. For each chosen study, we systematically recorded data concerning soil compressive properties and collected information on soil properties, soil conditions, site characteristics, and experimental settings. We compiled 4,743 individual data entries.</p><p><strong>Time and place</strong></p><p>The database includes data from 128 independent studies published between 1992 and 2021. Each study reported between 1 and 360 measurements, with a study median of 14 measurements and a mean of 38 measurements, totalling 4743 database entries. Our database includes data from 20 countries, with a significant concentration of the data originating from Brazil, followed by Germany, Switzerland, Sweden, and Denmark. The majority of the data came from arable soils, representing approximately 72% of data entries. </p><p><strong>Instruments</strong></p><p>The soil compressive properties included in the database were based on soil compressive tests performed in the laboratory by uniaxial method. The procedure used for stress application on soil samples was mainly the stepwise stress application method, while the constant strain rate method was applied in few studies (less than 2% of the data). The component of the compressive curve related to the soil packing state was represented by soil bulk density, void ratio, and strain. The stress component of the curve was represented in a logarithmic form in the entirety of the database. The database also comprised eight different methods for calculating precompresion stress: Casagrande (1936), Dias Junior and Pierce (1995), Lamandé et al. (2017), Sullivan and Robertson (1996), Casini (2012), Culley and Larson (1987), Pacheco Silva (1990), Gregory et al. (2006).</p><p><strong>Resources</strong></p><p>Web of Science, Scopus – literature search</p><p>Endnote Web – removal of duplicates</p><p>Rayyan software – initial paper selection based on title and abstract</p><p>WebPlotDigitizer – data extraction from figures</p><p>Microsoft Access – database platform</p><p><strong>Description of the collected data (column, unit, and description)</strong></p><p>Sample ID- A unique identification number assigned to each individual sample within the database </p><p>Study ID- Identification number assigned to each research study in the database</p><p>Reference - Research paper reference</p><p>Year - Year of research paper publication </p><p>Language - Language of the research paper </p><p>Soil classification (SiBCS) - Soil Classification according to the Brazilian System (SiBCS), as described in portuguese-language papers</p><p>Soil classification (original in paper) - Soil classification described in research paper </p><p>Soil classification (convertion to Soil Taxonomy orders) - Soil classification aligned with the Soil Taxonomy system developed by the United States Department of Agriculture (USDA) </p><p>Location - Study location country </p><p>Texture classification (USDA) - Soil textural classification according USDA</p><p>Texture classification USDA (letter code) - Letter code for soil textural classification according USDA: S=sand; LS=loamy sand; SL=sandy loam; SiL=silt loam; Si=silt; L=loam; SCL= Sandy clay loam; SiCL=Silty clay loam; CL=clay loam; SC=Sandy clay; SiC=Silty clay; C=clay</p><p>Clay (USDA) - % - Soil clay content (weight based) - (<0.002 mm) </p><p>Silt (USDA) - % - Soil silt content (weight based) - (0.002 < x < 0.05 mm, interpolated for European samples where needed using the k-nearest neighbor technique by Nemes et al. 2006) </p><p>Sand (USDA) - % - Soil sand content (weight based) - (0.05 < x < 2 mm, interpolated for European samples where needed using the k-nearest neighbor technique by Nemes et al. 2006)</p><p>USDA PSD interpolated - =0 if the data was NOT interpolated; =1 if the data was interpolated</p><p>Published texture class - Texture classification provided in the source publication when the values for clay, silt and sand were not available</p><p>Clay - g kg-1 - Soil clay content - original in the paper</p><p>Clay class upper boundary - µm - The clay class upper boundary informed in source publication</p><p>Silt - g kg-1 - Silt clay content - original in the paper</p><p>Silt class upper boundary - µm - The silt class upper boundary informed in source publication</p><p>Sand - Soil sand content - original in the paper</p><p>Sand class upper boundary - µm - The sand class upper boundary informed in source publication</p><p>Particle size data flag - =0 if no issues; =1 if there are issues (summing)</p><p>Sum particle size- g kg-1 - Sum of clay, silt, and sand content</p><p>Soil depth FROM – cm - When soil depth is presented as a range (e.g., 0-10cm), it indicates the minimum depth at which soil samples were collected </p><p>Soil depth TO – cm - When soil depth is presented as a range (e.g., 0-10cm), it indicates the maximum depth at which soil samples were collected </p><p>Depth – cm -Specific depth value as presented in paper, or when soil depth is showed as a range (e.g., 0-10cm), it indicates the average depth at which soil samples were collected (e.g 5cm) </p><p>SOC - g kg-1 - Soil organic carbon content informed in research paper or soil organic carbon content calculate from soil organic matter content by multiplying by 0,58 </p><p>SOC converted from SOM - 1= yes for soil organic carbon derived from soil organic matter content calculations</p><p>Particle density - Mg m-3 - Soil particle density </p><p>Initial matric potential – hPa - Soil water matric potential before loading</p><p>log Initial matric potential - Soil water matric potential expressed by log </p><p>Wetness (based on initial matric potential) - 1=if initial matric potential (MP)<100 hPa; 2= if 100<=initial MP<1000 hPa; 3= initial MP>=1000 hPa</p><p>Initial gravimetric water content - g g-1 - Gravimetric soil water content before loading provided by source publication, or calculated by volumetric water content divided by soil bulk density</p><p>Initial volumetric water content - m3 m-3 - Volumetric soil water content before loading, when the soil bulk density was not reported</p><p>Initial water content data source - Graph or table from where the data was collected, or explanation on calculation used</p><p>Matric potential type - Compressive tests performed on soil samples under different conditions: 1= equilibrated at matric potential; 2= field matric potential; 3= air-dried samples </p><p>Initial bulk density - Mg m-3 - Soil bulk density before loading </p><p>Initial BD data source - Graph or table from where the data was collected, or explanation on calculation used </p><p>Initial volumetric water content calculated - m3 m-3 - Soil volumetric water content calculated by multiplying soil gravimetric water content by soil bulk density</p><p>Precompression stress – kPa - Precompression stress </p><p>Precompression stress (SD) – kPa - Standard deviation for precompression stress values reported in paper </p><p>Precompression stress data source - Graph or table from where the data was collected, or explanation on calculation used</p><p>Compression index - Compression index </p><p>Compression index (SD) - Standard deviation of compression index values reported in paper </p><p>Compression index data source - Graph or table from where the data was collected, or explanation on calculation used</p><p>Swelling index - Swelling index </p><p>Swelling index (SD) - Standard deviation of swelling index values reported in paper </p><p>Swelling index data source - Graph or table from where the data was collected, or explanation on calculation used</p><p>N - Number of replicates used for calculating precompression stress, compression index, and swelling index when mean values are reported</p><p>Land use (paper) - Land use described in the research paper</p><p>Land use (categories) - Land use categorized</p><p>Land use standardized - Land use classified as: arable, forest, grassland, and native vegetation. The latter includes forest, grassland, and savanna</p><p>Land use (number code) - Number code for land use: 1=Arable, 2= forest, 3= grassland, and 4= native vegetation</p><p>Tillage system - Tillage system</p><p>Tillage system (arable soils) - Tillage system for arable soils classified as "conventional" and "conservation"</p><p>Coordinates - Geographical coordinates of study location</p><p>Climate - Climatic region classification: temperate, tropical, subtropical</p><p>Climatecod - Code number assigned to each climatic region: 1=temperate, 2=tropical, 3=subtropical</p><p>Sampling position (paper) - Field position where soil samples were collected with details described in the paper</p><p>Sampling position - Field position where soil samples were collected standardized</p><p>Treatment - Experimental treatment type where the soil samples were collected</p><p>Stress rate - kPa - Stress applied in compressive tests </p><p>Minimum stress – kPa - Minimum stress applied in compressive tests</p><p>Maximum stress – kPa - Maximum stress applied in compressive tests</p><p>Number of stress rate steps - Number of steps in stepwise stress application procedure</p><p>Stess application type - 1=Stepwise stress 2=one sample per stress 3=Strain controlled</p><p>Stess application type – min - Time for stress application in each step in stepwise stress application procedure</p><p>Degree of deformation at the end of loading - % - Degree of deformation at the end of compressive test</p><p>Sample diameter – cm - Diameter of the soil samples</p><p>Sample height – cm - Height of the soil samples</p><p>Ratio sample diameter and height - Ratio between diameter and height of the soil samples</p><p>Sample volume - cm3 -Sample volume when the sample diameter and height are nor presented</p><p>Precompression stress calculation method - Calculation method of precompression stress</p><p>Precompression stress calculation method (number code) - Number code for calculation method PC:1=Casagrande (1936); 2=Dias Junior and Pierce (1995); 3= Lamandé et al. (2017); 4=O`Sullivan and Robertson (1996); 5=Casini (2012); 6=Culley and Larson (1987);7=ABNT (1990); 8=Gregory et al. (2006)</p><p>Description of precompression stress calculation - Brief explanation of precompression stress calculation</p><p>Soil compressive curve components - Component of the soil compression curve related to the soil packing state: soil bulk density, void ratio, and strain. </p><p>Soil compressive curve components (number code) - Number code for component of the soil compressive curve related to the soil packing state: 1= soil bulk density; 2= strain; 3= void ratio</p><p>Curve components source - Source of the component of the soil compressive curve related to the soil packing state: 1= showed in the paper, 2= according to original method for precompression stress calculation, 3= described in method, but not clear in the paper</p><p>Compressive curve available - Original soil compressive curve available in the paper: 1= No 2=Yes</p><p>Comments - Brief comments on the paper</p><p><strong>Issues and remarks</strong></p><p>We sought out important information not included in the paper by directly communicating with the authors whenever possible. In cases where multiple papers covered the same experiment, we prioritized the one offering more comprehensive details. If two papers complemented each other, we included both. When analyzing studies comparing various methods for calculating soil precompression stress, we exclusively gathered data calculated using the widely accepted Casagrande (1936) method. To ensure comparability across studies, we standardized the collected data by converting it to the same unit. The standardization process involved: i) assuming that 58% of soil organic matter (SOM) was soil organic carbon (SOC) when only SOM was reported, ii) calculating soil bulk density using a soil particle density of 2.65 Mg m-3 when only total porosity data were provided, and iii) harmonizing all texture data to the USDA classification system, which defines the silt/sand boundary as 50 μm, utilizing the k-nearest neighbor approach (referred to as "similarity method" by Nemes et al. (1999). </p><p><strong>Reference</strong></p><p>Associação Brasileira de Normas Técnicas - ABNT. NBR 12007: Ensaio de adensamento unidimensional. Rio de Janeiro: 1990.</p><p>Casagrande, A., 1936. Determination of the preconsolidation load and its practical significance. In: Proceedings of the International Conference on Soil Mechanics and Foundation Engineering, vol. III, Harvard University, Cambridge, MA, pp. 60–64.Casini, F. 2012. Deformation induced by wetting: A simple model. Can. Geotech. J. 49:954–960 10.1139/T2012-054. doi:10.1139/t2012-054</p><p>Culley, J.L.B., Larson, W.E., 1987. Susceptibility to compression of a clay loam Haplaquoll. Soil Sci. Soc. Am. J. 51, 562–567.</p><p>Dias Junior, M.S., Pierce, F.J., 1995. A simple procedure for estimating preconsolidation pressure from soil compression curves. Soil Technology 8, 139–151. doi:10.1016/0933-3630(95)00015-8</p><p>Gregory, A.S., Whalley, W.R., Watts, C.W., Bird, N.R.A., Hallett, P.D., Whitmore, A.P., 2006. Calculation of the compression index and pre-compression stress from soil compression test data. Soil Till Res. 89:45-57. doi:10.1016/j.still.2005.06.012</p><p>Lamandé, M., Schjønning, P., Labouriau, R., 2017. A novel method for estimating soil precompression stress from uniaxial confined compression tests. Soil Sci. Soc. Am. J. 81 https://doi.org/10.2136/sssaj2016.09.0274.</p><p>Nemes, A., Wösten, J.H.M., Lilly, A., Oude Voshaar, J.H., 1999. Evaluation of different procedures to interpolate the cumulative particle-size distribution to achieve compatibility within a soil database. Geoderma 90: 187-202. 129 </p><p>O'Sullivan, M.F., Robertson, E.A.G., 1996. Critical state parameters from intact samples of two agricultural topsoils. Soil Tillage Res 39(3 – 4):161 – 173.</p>
Data from: Improper data practices erode the quality of global ecological databases and impede the progress of ecological research
<p>The scientific community has entered an era of big data. However, with big data comes big responsibilities, and best practices for how data are contributed to databases have not kept pace with the collection, aggregation, and analysis of big data. Here, we rigorously assess the quantity of data for specific leaf area (SLA) available within the largest and most frequently used global plant trait database, the TRY Plant Trait Database, exploring how much of the data were <em>applicable </em>(i.e., original, representative, logical, and comparable) and <em>traceable </em>(i.e., published, cited, and consistent). Over three-quarters of the SLA data in TRY either lacked applicability or traceability, leaving only 22.9% of the original data usable compared to the 64.9% typically deemed usable by standard data cleaning protocols. The remaining usable data differed markedly from the original for many species, which led to altered interpretation of ecological analyses. Though the data we consider here make up only 4.5% of SLA data within TRY, similar issues of <em>applicability </em>and <em>traceability </em>likely apply to SLA data for other species as well as other commonly measured, uploaded, and downloaded plant traits. We end with suggested steps forward for global ecological databases, including suggestions for both uploaders to and curators of databases with the hope that, through addressing the issues raised here, we can increase data quality and integrity within the ecological community.</p>
First insights into the scale of invasions in African marine protected areas: leveraging global databases and crowdsourced data
<p>This dataset stems from the paper "First insights into the scale of invasions in African marine protected areas: leveraging global databases and crowdsourced data". Additional data is provided as supplementary material and is associated with the manuscript. </p>
Global River Obstruction Database v1.1
<p>GROD v1.1 (filename: GROD_v1.1.csv), or Global River Obstruction Database version 1.1, contains 30549 manually identified human-made structures that obstructing river longitudinal flow. Obstructions have been identified on Google Earth Engine satellite map for all rivers mapped in the Global River Widths from Landsat (<a href="https://zenodo.org/record/1297434#.YU0rAGZKhGo">GRWL</a>) database. Each obstruction has assigned one of the six types—Dam, Lock, Low head dam, Channel dam, Partial dam 1, Partial dam 2. Details of the mapping process and data quality can be found in the following publication:</p> <p>Yang, X., Pavelsky, T.M., Ross, M.R.V., Januchowski-Hartley, S.R., Dolan,W., Altenau, E.H., Belanger, M., Byron, D.K., Durand, M.T., Dusen, I.V., Galit, H., Jorissen, M., Langhorst, T., Lawton, E., Lynch, R., Mcquillan, K.A., Pawar, S., Whittemore, A., in revision. Mapping ow-obstructing structures on global rivers. Water Resources Research.</p> <p>The single csv file contain the version 1 of GROD that accompanying the above-mentioned publication. It contains 7 columns:</p> <p><strong>grod_id: </strong>unique identifier (character)</p> <p><strong>type:</strong> obstruction type (character)</p> <p><strong>lon:</strong> longitude in decimal degrees (float)</p> <p><strong>lat:</strong> latitude in decimal degrees (float)</p> <p><strong>sword_reach_id<sup>1</sup>: </strong>nearest sword reach id (character)</p> <p><strong>distance_to_sword: </strong>distance to the nearest sword reach (float)</p> <p> </p> <p><strong>Note:</strong></p> <p><strong>1. </strong>sword, or SWOT River Database (https://zenodo.org/record/3898570#.YU0urWZKhGo), is an improved version of GRWL with better topology. Majority of the GROD obstructions (N=30502) has been matched with the closest SWORD reach. The remaining 16 obstructions were further than 10km away from any SWORD reach and were not matched to SWORD. </p> <p><strong>2.</strong> GROD, along with many other large scale river obstructions databases, will be hosted on Global Dam Watch website (http://globaldamwatch.org/).</p>
The ProkaBioDen database, a global database of benthic prokaryotic biomasses and densities in the marine realm
<p class="MsoNormal">Benthic prokaryotes include Bacteria and Archaea and dominate densities of marine benthos. They play major roles in element cycles and heterotrophic, chemoautotrophic, and phototrophic carbon production. To understand how anthropogenic disturbances and climate change might affect these processes, better estimates of prokaryotic biomasses and densities are required. Hence, I developed the ProkaBioDen database, the largest open-access database of benthic prokaryotic biomasses and densities in marine surface sediments. In total, the database comprises 1,089 georeferenced benthic prokaryotic biomass and 1,875 density records extracted from 85 and 112 studies, respectively. I identified all references applying the procedures for systematic reviews and meta analyses and report prokaryotic biomasses as g C cm<sup>-3</sup> sediment, g C g<sup>-1</sup> sediment, and g C m<sup>-2</sup>. Density records are presented as cell cm<sup>-3</sup> sediment, cell g<sup>-1</sup> sediment/ sulfide/ vent precipitate, and cell m<sup>-2</sup>. This database should serve as reference to close sampling gaps in the future.</p>
Above- and below-ground net primary productivity: a field-based global database of grasslands
<p>Net primary productivity (NPP) over global grasslands is crucial for understanding the terrestrial carbon cycling and for the assessments of wild herbivores food security. During the past few decades, numerous field investigations have been conducted to estimate grassland NPP since the measuring criterion released by the International Biological Program. However, a comprehensive NPP database, particularly for belowground NPP (BNPP), in global grasslands is rare to date. Here, field NPP measurements from 438 publications (1957–2018) in global grasslands were collected, critically filtered, and incorporated in a comprehensive global database with observations for aboveground NPP (ANPP), BNPP, total NPP (TNPP), and BNPP fraction (fBNPP). Associated information on geographical locations, climatic records, grassland types, land use patterns, manipulations subjected to manipulative experiments, sampling year of study sites as well as NPP measurement methods are also documented. This database included 2985 entries from 1785 study sites. Among them, 806 entries contained paired data of ANPP and BNPP, resulting in the 806 fBNPP data. The study sites encompassed global grasslands with latitudinal range of 54.5° S~78.9° N, longitudinal range of 157.4° W~175.8° E, and altitudes from 0 to 5168 m above sea level, covering broad climatic gradients (-17.6 to 28.8 °C in mean annual temperature and 63 to 2052 mm in mean annual precipitation). This global database is the world's largest paired data of ANPP and BNPP field measurements in grasslands. It can be used to study the spatio-temporal patterns of NPP and its allocation, evaluate the responses of above- and below-ground carbon components to future global changes, and validate the NPP estimation by empirical or process-based models in global grasslands. The database can be freely used for non-commercial applications. We kindly request users cite this data paper when using the database, respecting all the hard work during data compilation.</p>
A Global Database of Pitted Cones on Mars for Research on Martian Volcanism
<p>The generated datasets as reported in an article by Chen et al. (2024) "A Global Database of Pitted cones on Mars for Research on Martian Volcanism".</p>
Global riverine DOC flux by TRIPLEX-HYDRA model and a comparison with a published DOC database
<p>A database of global riverine DOC flux simulated by TRIPLEX-HYDRA model. And the temporal variation of riverine DOC flux in terms of different continents, oceans and latitudinal bands. </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.