Despite a growing recognition of the importance of data to the economy and to science, investment in repositories to manage and disseminate that data in easily accessible and understandable ways is scarce. Keeping repository services active and up-to-date for a long time period is difficult due to this funding situation. As a result, repositories must continually provide proof of their value, their Return on Investment (ROI) to their sponsors; yet doing so has always been difficult, problematic and not always successful. In this work, an analysis of approaches for assessing the ROI of several scientific data repositories has identified various techniques that repositories use to report on the impact and value of their data products and services. A survey of selected repositories rated the set of metrics identified and rated each by its importance as well as the ease with which the metric could be measured. The discussion is broken down into considerations for calculating costs, perceived value of repositories and suggested metrics that would allow a repository to calculate an ROI. The authors, representatives of environmental data repositories, concluded that easily obtainable data use metrics, such as data downloads, etc., have limited value while more informative analyses would require additional resources.

Data repository to preserve and provide access to agricultural and environmental data produced during research projects undertaken at the University of Guelph including datasets on topics such as crop yield, soil moisture, weather and agroforestry. A special emphasis is placed on research funded by Ontario Ministry of Agriculture and Food (OMAF) and MRA.

Metafile contains github links for R code and input datasets (including those generated in our analysis and those publicly available) for probabilistic and deterministic crop footprint generation and field-level simulation analysis. This dataset is associated with the following publication: McCaffrey, K., E. Paulukonis, S. Raimondo, S. Sinnathamby, S. Purucker, and L. Oliver. A multi-scale approach for identification of potential pesticide use sites impacting vernal pool critical habitat in California. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 857(1): 159274, (2023).

Preface:

This is a part of my contribution to the Kiva, to help them continue and expand their initiative to alleviate global poverty.

Context:

In order for Kiva to best set its investment priorities, help inform lenders, and understand their target communities, knowing the level of poverty for each borrower is crucial. However, attaining individual-level information is time-consuming, labor-intensive and expensive.

Therefore, I propose a method that combines machine learning and satellite imagery to predict poverty. This approach is easier, less expensive and scalable since satellite data is often inexpensive and open source. Before developing this model we have to first collect the data that would be relevant for prediction regionalized poverty, which is why I have created this dataset.

Content:

This data set is a collection of the following:

1. Environmental Data: Vegetation indices, soil characteristics, evaporation
2. Climate Data: Temperature, precipitation, elevation
3. Socioeconomic and Demographic Data: Population Density, Access to major cities, Nightlight, Land usage
4. Conflict Data: Conflicts, death tolls, civilian casualties
5. Natural Disaster Data (coming shortly)

I have provided the data in the following formats:

• Individual Level Data: For each region in the loans (kiva_loans) and MPI study set, I have extracted all of the information listed above. The data is provided in the csv files; MPIData_augmented.csv, kivaData_augmented.csv
• Stacked satellite images for each country with a loan. For each country, a satellite image is provided as a .grd file in the Satellite Imagery folder

Usage:

If you are unfamiliar with working with satellite images I suggest you utilize the csv files. I will put out a tutorial on working with the satellite images in the near future. If you have any questions, please post them an I will try to answer them as soon as I can. If you have any questions related to the data, please refer to the data dictionary.

Documentation:

Documentation for this dataset is an ongoing process given the complex and extensive process to preparing this dataset, amd the wide range of data sources. If you have a particular question please post it and I'll answer it as soon as possible.

Upcoming Work:

As I mentioned above, I am going to use this data to build a machine learning model that will be able to predict the poverty for any region in any impoverished country! Stay tuned!

This dataset contains supplementary information for a manuscript describing the ESS-DIVE (Environmental Systems Science Data Infrastructure for a Virtual Ecosystem) data repository's community data and metadata reporting formats. The purpose of creating the ESS-DIVE reporting formats was to provide guidelines for formatting some of the diverse data types that can be found in the ESS-DIVE repository. The 6 teams of community partners who developed the reporting formats included scientists and engineers from across the Department of Energy National Lab network. Additionally, during the development process, 247 individuals representing 128 institutions provided input on the formats. The primary files in this dataset are 10 data and metadata crosswalk for ESS-DIVE’s reporting formats (all files ending in _crosswalk.csv). The crosswalks compare elements used in each of the reporting formats to other related standards and data resources (e.g., repositories, datasets, data systems). This dataset also contains additional files recommended by ESS-DIVE’s file-level metadata reporting format. Each data file has an associated dictionary (files ending in _dd.csv) which provide a brief description of each standard or data resource consulted in the data reporting format development process. The flmd.csv file describes each file contained within the dataset.

Data repository for the data used in the thesis of A. Perttu from the experimental PDCs at the PELE facility.

All data used for these analyses are publicly available, either through CPDat, ToxRefDB, or the CompTox Chemicals Dashboard. Script associated with these analyses are publicly available through the Ragerlab Github repository. Data that were combined and analyzed in generating results for this specific study are provided as supplemental material (Supplementary Tables 1–10, provided online through the Ragerlab-Dataverse repository). This dataset is associated with the following publication: Koval, L., K. Dionisio, K. Friedman, K. Isaacs, and J. Rager. Environmental mixtures and breast cancer: identifying co-exposure patterns between understudied vs breast cancer-associated chemicals using chemical inventory informatics. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 32: 794-807, (2022).

The Environmental Data Initiative (EDI) is a trustworthy, stable data repository and data management support organization for the environmental scientist. EDI provides tools and support that allow the environmental researcher to easily integrate data publishing into the research workflow. Almost ten years since going into production, these data and code were used to provide a general description of EDI’s collection of data and its data management philosophy and placement in the repository landscape. They show how comprehensive metadata and the repository infrastructure lead to highly findable, accessible, interoperable, and reusable (FAIR) data by evaluating compliance with specific community proposed FAIR criteria. Finally, they provide measures and patterns of data (re)use, assuring that EDI is fulfilling its stated premise.

Load cell data of Ocean Sentinel (OS) mooring, OS locations data and environmental conditions data (wave, wind, current) measured in the 2013 field test.

This portal contains environmental radiological monitoring data collected in response to the nuclear emergency following the March 11th, 2011 Tohoku earthquake and tsunami. Available data sets include field measurements, field samples, and analysis results. It is designed to contain data sets from other large-scale response efforts should they occur.

The concept of ecosystem services (ES) is attracting increased attention as a way to communicate the value of nature for human well-being by using a language that reflects dominant political and economic views. Progress in ES research has been rapid (Guerry et al. 2015), and there is an increasing information demand for diverse groups of decision makers (Schaefer et al. 2015; Bouwma et al. 2017). Incorporating ES information into decision making, however, is a long-term project and requires successfully addressing a number of challenges. One challenge is to efficiently exploit available information sources for decision advice. In Schmidt and Seppelt (2018) we reviewed how information contained in ES databases can support policy instruments to better take nature’s benefits into account. Here the data compiled within Schmidt and Seppelt (2018) was made available. In total 29 databases with global coverage were reviewed that contain information of 36,014 studies, projects and methods within more than 600,000 entries. Additionally, I identified 93 indicators of information demand for six major policy instruments which deal with or are directly related to the use of natural resources or land. Database entries were than matched with indicators of information demand. The resulting dataset encompasses the total number of data entries of ES databases that could be thematically linked to information requirements from indicators of information demand. Also, data was made available that provides broader insights into the content, design and impact of reviewed ES databases. Facilitating data discovery and linking ES databases with policy instruments are essential steps for the incorporation of ES information into decision making.

TERN (funded by NCRIS and EIF) has been developing coherent community-wide management structures for several of the required key data streams, so the relevant data are no longer unmanaged. eMAST builds on this infrastructure, by generating products that integrate the different streams of data e.g. water use and other ecosystem functions. The eMAST ANUClimate climate surfaces will be the first, continental 0.01 degree (nominal 1km) resolution climate surfaces generated using the Hutchinson et al. (ANU) methodology. Combined with the ancillary bioclimatic, ecosystem variables and indices derived from these data, this will be the first complete collection of its kind made publically available as a single resource. This collection of datasets, is a resource for the ecosystem science community and enhances the capacity for research. For example the development of an advanced benchmarking system for terrestrial ecosystem models (i.e. PALS). In addition, the data will be made accessible through the SPEDDEXES web-interface at the NCI, making the data sets conveniently available to a wide audience/community. The datasets generated within the scope of eMAST focus on Australia ecosystems, but are expected to encourage global as well as national interests, because of the universal data formats use. The project is thus expected to facilitate ecosystem modellers to perform comparative analyses of model performance; build new connections between Australian and overseas researchers, and between different research communities in Australia; and accelerate the development, testing and optimization of terrestrial ecosystem models. Working towards the next generation of robust, process based ecosystem models; we are synthesizing observations of plant biophysical and physiological traits, developing gridded surfaces of these traits, and working with TERN MultiScale Plot Network to improve national coverage of trait measurements. Working in collaboration with international collaborators from NEON and NCAR; eMAST are demonstrating and developing Australia capacity for making models utilise these information rich collections.

More information about this collection can be found at http://www.emast.org.au

This dataset contains an inventory for the paper entitled "ecocomDP: A flexible data design pattern for ecological community survey data" (O'Brien et al), submitted to Ecological Informatics. The paper describes an approach for harmonizing and reformatting community survey data such as organism abundance or cover measurements. Data currently using this data model and workflow approach are from the repository of the Environmental Data Initiative (EDI), the Long Term Ecological Research (LTER) Network, and the National Ecological Observatory Network (NEON). Data were assembled for this analysis in late 2020. The inventory is composed of two tables, describing data from EDI (including LTER) and data from NEON. The EDI inventory includes information for 70 datasets: identifiers for both the original and converted datasets, and basic coverage information such as temporal coverage (range of years and a measurement of sampling evenness), spatial coverage (maximum bounding coordinates and area of the "bounding box"), and taxonomic coverage (taxonomic classes). The NEON inventory contains information from 11 continent-wide NEON data products, divided into individual field sites to be more spatially compatible with EDI and LTER data. Taxonomic coverage is by group (e.g., algae, birds) rather than explicit taxonomic classes. Spatial coverage is the area of a field sampling site polygon. Temporal coverage includes the same minimum and maximum sampling years and temporal evenness measures as for the EDI data plus a count of months during that period when sampling occurred. At the time of data download, NEON data was considered provisional, however identifiers are persistent and now deliver final, "released" data. Also included in the data package is a script to reformat inventory data and create Figure 3 of the paper.

The Enhanced Delta Smelt Monitoring Program (EDSM) was initiated by the U.S. Fish and Wildlife Service in 2016. EDSM focuses on providing real-time data to help managers respond to population patterns of Delta Smelt within the upper San Francisco Estuary, primarily by providing estimates of Delta Smelt distribution and abundance. The dataset can also be useful in evaluating habitat use and behavior patterns of this species and other fish species of interest. Sampling is done year round via Kodiak trawls and larval (“20-mm”) gear to sample Delta Smelt across most life stages. Sites are chosen via stratified random sampling. Over the course of a week, field crews sample between 18 and 41 random sites. A minimum of two tows are conducted at each site. All fish collected are identified (in the field when possible, in the lab for early life stages), measured, enumerated, and recorded. In addition to fish information, environmental data are collected for each sampling event. For more information: https://www.fws.gov/lodi/juvenile_fish_monitoring_program/jfmp_index.htm

Data repository for Seasonality modulates coral trophic plasticity in an extreme, multi-stressor environment in Limnology and Oceanography.

Dataset links to the Digital Collections of Colorado, DSpace Repository. From the homepage, you can search the 1240 datasets hosted there, or browse using a list of filters on the right. DSpace is a digital service that collects, preserves, and distributes digital material. Resources in this dataset:Resource Title: GeoData catalog record. File Name: Web Page, url: https://geodata.nal.usda.gov/geonetwork/srv/eng/catalog.search#/metadata/ShortgrassSteppe_eaa_2015_March_19_1220

This repository contains files of spatial environmental data layers which were used as predictors of nesting habitat suitability for six biodiversity indicator bird species in Finland: (i) three hawk species, the European honey buzzard (Pernis apivorus), the northern goshawk (Accipiter gentilis) and the common buzzard (Buteo buteo), and (ii) three woodpecker species, the white-backed woodpecker (Dendrocopos leucotos), the lesser spotted woodpecker (Dryobates minor) and the Eurasian three-toed woodpecker (Picoides tridactylus). These six bird species have been shown to provide useful indicators of different conservation and biodiversity values of boreal forests, such as occurrences of red listed polypores, and indicative of forest characteristics related to old-growth forests such as representative occurrences of dead wood. The modelling spesifically targetted the nest sites of the bird species where the role of critical suitable environmental conditions is elevated. The data of nesting sites of the bird species are not open access data due to its sensitivity, but can be requested for research purposes by sending a query to the head of the Zoology unit at the Finnish Museum of Natural History. However, the Maxent results of the nesting habitat suitability for bird species across the whole of Finland are available in Zenodo (https://doi.org/10.5281/zenodo.4779108). Please also note that the environmental data is split - due to its large size - into two repository locations, this one containing the 1 km buffer variables and the two climate variables, and the other one containing the local site variables and 500 m buffer variables. Taken together, 40 different environmental data layers were developed and their information sampled for a 96 x 96 m resolution lattice system covering the whole Finland. These 40 data layers were organised for the modeling into the following groups of predictor variables: (1) Data on forest structure and other forest stand characteristics (96 x 96 m cell) (8 variables), (2) Data on land cover at the forest stand (96 x 96 m cell) (8 variables), (3) Data on forest characteristics in the 500 m landscape buffer area (3 variables), (4) Data on forest characteristics in the 1 km landscape buffer area (3 variables), (5) Data on land cover in the 500 m landscape buffer area, (6) Data on land cover in the 500 m landscape buffer area, and (7) Climate data (2 variables). These data were used to examine what are the key determinants of the nesting site suitability of the six indicator bird species and to developed predictive maps across the whole Finland for the locations of most optimal nesting forest areas. The nesting habitat suitability modelling was done using the MaxEnt model. The forest structure and habitat quality predictor variables were developed based on national forest data gathered from three sources: (i) Finnish Forest Center (FFC), (ii) Metsähallitus Parks & Wildlife (MPW), and (iii) the multi-source national forest inventory carried out by the Natural Resources Institute Finland (LUKE). The land cover variables were measured using the CORINE Land Cover 2018 system, from the database produced, maintained and distributed by Syke. The two climate variables were initially for the SUMI project by the Finnish Meteorological Institute and further applied in this modelling study. The environmental variables, the six indicator bird species and the processses, steps and choices included in the MaxEnt modelling are described in full detail in the following publication: Virkkala, Raimo, Leikola, Niko, Kujala, Heini, Kivinen, Sonja, Hurskainen, Pekka, Kuusela, Saija, Valkama, Jari and Heikkinen, Risto K.: Developing fine-grained nationwide predictions of valuable forests using biodiversity indicator bird species, Ecological Applications, in press. The details of the environmental data are also described in the read_me.doc file uploaded into this repository.

This data repository contains: A) MATLAB source code B) Sample locations and chemical measurements used for Be-10 and C-14 analysis C) Dose recovery test results for luminescence protocol D) Sample locations and dosimetry information for luminescence samples

Cawthron Institute (Cawthron) has recently compiled a national dataset containing ecological estuary monitoring data (2001 to 2016) largely acquired from councils and unitary authorities (councils) around New Zealand. The dataset comprises fine-scale intertidal benthic ecological data collected using the Estuary Monitoring Protocol (EMP: Robertson et al. 2002), or similar, survey methodologies. This is in the form of macrofaunal abundance data and corresponding physico-chemical sediment data, as well as associated metadata. The dataset was compiled to facilitate national-scale research within the MBIE-funded Oranga Taiao, Oranga Tangata (OTOT) programme, led by Murray Patterson from Massey University. For further details on the dataset please refer to the National Estuary Dataset User Manual, which is included as a reference in the FigShare data repository. Use of the dataset is entirely at the risk of the recipient and we accept no responsibility for any inaccuracies that may be present.Subsets of this dataset have been used to 1) summarise benthic ecological health indicators from New Zealand estuaries, 2) test the suitability of nine biotic indices for assessing the health of New Zealand estuaries, and 3) develop a standardised indicator of the health of New Zealand estuaries in response to sedimentation and metal loading. These paper are also included as references in the FigShare Data Repository.

Data show the effects of nano CeO2 particles on the nutrient contents and stable isotopes of wheat plants over 3 generations of exposure. This dataset is associated with the following publication: Rico, C.M., O.M. Abolade, D. Wagner, B. Lottes, J. Rodriguez, R. Biagioni, and C. Andersen. Wheat exposure to cerium oxide nanoparticles over three generations reveals transmissible changes in nutrition, biochemical pools, and response to soil N. JOURNAL OF HAZARDOUS MATERIALS. Elsevier Science Ltd, New York, NY, USA, 384: 121364, (2020).