Eligible studies from the CureSCi Metadata Catalog and their available predictor variables.

Harmful algal blooms (HABs) are overgrowths of algae or cyanobacteria in water and can be harmful to humans and animals directly via toxin exposure or indirectly via changes in water quality and related impacts to ecosystems services, drinking water characteristics, and recreation. While HABs occur frequently throughout the United States, the driving conditions behind them are not well understood, especially in flowing waters. In order to facilitate future national model development and characterization of HABs, this data release publishes a synthesized and cleaned collection of HABs-related water quality and quantity data for river and stream sites across the United States. It includes nutrients, major ions, sediment, physical properties, streamflow, chlorophyll and other types of water data. This data release contains files of harmonized data from the USGS National Water Information System (NWIS). Continuous sensor data for 132 parameters (35 of which returned data) between Janu ...

This dataset is was created by re-compiling available open, gender/sex-disaggregated Feed the Future datasets for Bangladesh and applying standard processing methods to enhance their accessibility and interoperability. This process entailed the standardization of variable names and labels, the creation of derived socio-economic indicators such as dietary diversity scores, household dependency ratios, and household age and gender composition. This dataset allows researchers to easily use data for Bangladesh, as well as make cross country comparisons with other standardized datasets. Moreover, the provision of household GIS coordinates (offset for confidentiality purposes) allows users to match data at different levels. This work combines multi-topic household and community socio-economic and agricultural surveys with biophysical datasets from multiple sources, including remote sensing, for a thorough comparison of different phenomena. These biophysical sources include the International Soil Reference and Information Centre (ISRIC) World Soil Information, NASA MODIS vegetation indices and land surface temperature data, and the HarvestChoice spatially-disaggregated subnational crop production statistics database (Spatial Production Allocation Model; SPAM).

Harmful algal blooms (HABs) are overgrowths of algae or cyanobacteria in water and can be harmful to humans and animals directly via toxin exposure or indirectly via changes in water quality and related impacts to ecosystems services, drinking water characteristics, and recreation. While HABs occur frequently throughout the United States, the driving conditions behind them are not well understood, especially in flowing waters. In order to facilitate future model development and characterization of HABs in the Illinois River Basin, this data release publishes a synthesized and cleaned collection of HABs-related water quality and quantity data for river and stream sites in the basin. It includes nutrients, major ions, sediment, physical properties, streamflow, chlorophyll and other types of water data. This data release contains files of harmonized data from the USGS National Water Information System (NWIS), the U.S. Army Corps of Engineers (USACE), the Illinois Environmental Protec ...

Predictor variables used in analysis and the methods used to harmonize to the categorical variables.

Location of transboundary groundwater model included in RESOURCE-WP4 project and the reports: 4.2 and 4.3

Multi-Omics Clinical Data Harmonization Market Outlook

According to our latest research, the global Multi-Omics Clinical Data Harmonization market size reached USD 1.65 billion in 2024, reflecting robust adoption across healthcare and life sciences. With a strong compound annual growth rate (CAGR) of 14.2% projected from 2025 to 2033, the market is anticipated to reach USD 4.65 billion by 2033. This growth is primarily driven by the escalating integration of multi-omics approaches in clinical research, the increasing demand for personalized medicine, and the urgent need to standardize complex biological data for actionable insights. As per our latest research, the market's expansion is underpinned by technological advancements and the broadening scope of omics-based applications in diagnostics and therapeutics.

The rapid growth of the Multi-Omics Clinical Data Harmonization market can be attributed to several key factors. One of the most significant drivers is the exponential increase in biological data generated from next-generation sequencing and other high-throughput omics platforms. As researchers and clinicians seek to unravel the complexities of human health and disease, the need to integrate and harmonize disparate data types—such as genomics, proteomics, metabolomics, and transcriptomics—has become paramount. This harmonization enables a more comprehensive understanding of disease mechanisms, facilitating the identification of novel biomarkers and therapeutic targets. Moreover, regulatory bodies and funding agencies are increasingly emphasizing data standardization and interoperability, further fueling demand for robust harmonization solutions.

Another major growth factor is the accelerating adoption of precision medicine initiatives worldwide. The shift from one-size-fits-all therapies to tailored treatment regimens necessitates the integration of multi-omics data with clinical and phenotypic information. Harmonized data platforms empower clinicians and researchers to draw meaningful correlations between omics signatures and patient outcomes, thereby enhancing diagnostic accuracy and enabling the development of personalized therapeutic strategies. Pharmaceutical and biotechnology companies, in particular, are leveraging multi-omics harmonization to streamline drug discovery pipelines, improve patient stratification, and optimize clinical trial designs, contributing to significant market growth.

Technological innovation plays a central role in propelling the Multi-Omics Clinical Data Harmonization market forward. Advances in artificial intelligence, machine learning, and cloud computing have revolutionized the way multi-omics data is processed, integrated, and analyzed. Sophisticated software platforms now offer automated data curation, normalization, and annotation, reducing manual errors and accelerating research timelines. Additionally, collaborative efforts between academic institutions, healthcare providers, and industry stakeholders have led to the establishment of large-scale multi-omics databases and consortia, further driving market expansion. The growing focus on data privacy, security, and regulatory compliance also shapes market dynamics, prompting continuous innovation in harmonization technologies.

Regionally, North America remains the dominant force in the Multi-Omics Clinical Data Harmonization market, accounting for the largest share in 2024. The region's leadership is attributed to its advanced healthcare infrastructure, significant investments in omics research, and a strong presence of key market players. Europe follows closely, leveraging robust public-private partnerships and supportive regulatory frameworks. Meanwhile, the Asia Pacific region is witnessing the fastest growth, fueled by increasing government initiatives, expanding healthcare access, and rising awareness of precision medicine. Latin America and the Middle East & Africa, though currently smaller markets, are expected to demonstrate steady growth as they enhance their research capabilities and digital health ecosystems.

Solution Analysis

The Solution segment of the Multi-Omics Clinical Data Harmonization market is bifurcated into software and services, each playing a pivotal role in enabling seamless integration and analysis of diverse omics datasets. Software solutions encompass a wide range of platforms and tools designed to automate data normalization, annotation, and integ

A harmonized collection of the core data pertaining to COVID-19 reported cases by geography, in a format prepared for analysis

This dataset provides 0.25-degree gridded, global, annual estimates of fractional land use and land cover patterns for the period 2015-2100, designed to support the ISIMIP2b effort to assess the impacts of 1.5 Deg Celcius global warming. Land use types, land use transitions, and cropland estimates of area fraction are provided and include detailed separation of primary and secondary natural vegetation into forest and non-forest sub-types, pasture into managed pasture and rangeland, and cropland into multiple crop functional types; all transitions between land use states per grid cell per year, including crop rotations, shifting cultivation, and wood harvest; and agriculture management including irrigation, synthetic nitrogen fertilizer, and biofuel management. The LUH2-ISIMIP2b datasets were derived using Land Use Harmonization 2 (LUH2) methodology and are based on land-use scenarios provided by the REMIND-MAgPIE Integrated Assessment Model using an SSP2 storyline along with RCP2.6 and RCP6.0 emissions scenarios. In contrast to the standard SSP scenarios, these land use changes additionally account for climate and atmospheric CO2 fertilization effects on the underlying patterns of potential crop yields, water availability, and terrestrial carbon content. This is achieved by using the LPJmL (Lund-Potsdam-Jena managed land) model forced with atmospheric CO2 concentrations and patterns of climate change generated from 4 different climate models (GFDL, HADGEM, IPSL, and MIROC) consistent with the 2 different RCP scenarios, resulting in a set of 8 different LUH2-ISIMIP2b datasets.

This data set describes select global soil parameters from the Harmonized World Soil Database (HWSD) v1.2, including additional calculated parameters such as area weighted soil organic carbon (kg C per m2), as high resolution NetCDF files. These data were regridded and upscaled from the Harmonized World Soil Database v1.2

The HWSD provides information for addressing emerging problems of land competition for food production, bio-energy demand and threats to biodiversity and can be used as input to model global carbon cycles.

The data are presented as a series of 27 NetCDF v3/v4 (*.nc4) files at 0.05-degree spatial resolution, and one NetCDF file regridded to the Community Land Model (CLM) grid cell resolution (0.9 degree x 1.25 degree) for the nominal year of 2000.

Abstract

This multi-country harmonized dataset concerning forcibly displaced populations (FDPs) and their host communities was produced by the World Bank’s Poverty and Equity Global Practice. It incorporates representative surveys conducted in 10 countries across five regions that hosted FDPs in the period 2015 to 2020. The goal of this harmonization exercise is to provide researchers and policymakers with a valuable input for comparative analyses of forced displacement across key developing country settings.

Geographic coverage

The datasets included in the harmonization effort cover key recent displacement contexts: the Venezuelan influx in Latin America’s Andean states; the Syrian crisis in the Mashreq; the Rohingya displacement in Bangladesh; and forcible displacement in Sub-Saharan Africa (Sahel and East Africa). The harmonization exercise encompasses 10 different surveys. These include nationally representative surveys with a separate representative stratum for displaced populations; sub-national representative surveys covering displaced populations and their host communities; and surveys designed specifically to provide insights on displacement contexts. Most of the surveys were collected between 2015 and 2020.

Analysis unit

Household

Universe

Forcibly displaced populations and their hosts communities.

Kind of data

Sample survey data [ssd]

Mode of data collection

Computer Assisted Personal Interview [capi]

The dataset contains spatial features extracted and harmonized from GTK’s Mineral deposit database to MIN4EU data model. The mineral deposit database contains all mineral deposits, occurrences, and prospects in Finland. Data is based on all public information on the deposits available including published literature, archive reports, press releases and companies’ web pages.

This repository contains the database presented in the publication "A harmonized database of European forest simulations under climate change". It contains a collection of harmonized forest simulation model outputs from 17 different models covering 1.1 million individual simulation runs, over 136 million simulation years across over 13,000 unique locations in Europe.

Detailed description can be found in the publication (DOI will follow). The file "forest_simulation_db_v1.7z" contains all raw simulation outputs and a metadata table of all simulations including information about locations and harmonized soil conditions for those locations. Simulation outputs with harmonized climate data are stored in one SQLite database per climate scenario.

The code that was used to create the database, as well as to access and explore the data can be found here: https://github.com/magrueni/forest_simulation_database.git

Note: Please be cautious with the use of the simulations with unique identifiers 1037-1047. There were some abrupt species compositions changes reported that suggest that in a small number of the original simulations there was an underlying issue in the compilation of the raw simulation data.

--- Please use the updated version 1.1 ---

Unfortunately we found an bug in the daily climate extraction process of the previous version, leading to inconsistencies in the harmonized climate data. We corrected the harmonized daily climate data for all scenarios. Additionally, the LAI values in the raw data of the simulations with the unique identifier 1016 were calculated wrongly and therefore corrected in this version. Please use the updated version for all analyses. We apologize for any inconveniences.

Version 1.1 can be found here: 10.5281/zenodo.12750180

--- Please use the updated version 1.1 ---

These data represent fractional land use and land cover patterns annually for the years 1500 - 2100 for the globe at 0.5-degree (~50-km) spatial resolution. Land use categories of cropland, pasture, primary land, secondary (recovering) land, and urban land, and underlying annual land-use transitions, are included. Annual data on age and biomass density of secondary land, as well as annual wood harvest, are included for each grid cell. Historical land cover data for the years 1500 - 2005 are based on HYDE 3.1 and future land cover projections for the period 2006 - 2100 came from four Integrated Assessment Model (IAM) scenarios which reach different levels of radiative forcing by year 2100: MESSAGE (8.5 W/m2), AIM (6 W/m2), GCAM (4.5 W/m2), and IMAGE (2.6 W/m2). A key feature of these data is that historical reconstructions of land use were harmonized (computationally adjusted to minimize differences at the transition period) with modeled future scenarios, allowing for a seamless examination of historical and future land use. The output data present a single consistent, spatially gridded set of land-use change scenarios for studies of human impacts on the past, present, and future Earth system. For additional information about the algorithms, inputs, and options used in creating the land use transitions data, please refer to Hurtt et al. (2006) and Hurtt et al. (2011).Data are presented as a series of twenty (20) different data products representing different past and future model scenarios. There are a total of 560 NetCDF v4 files (*.nc4), one for each combination of data product and land use variable.

Description and harmonization strategy for the predictor variables.

EO Data Harmonization Pipelines Market Outlook

According to our latest research, the global EO Data Harmonization Pipelines market size reached USD 2.17 billion in 2024, with a robust compound annual growth rate (CAGR) of 13.2% projected through the forecast period. By 2033, the market is expected to attain a value of USD 6.19 billion. This growth is primarily driven by the surging demand for integrated, high-quality Earth Observation (EO) data across various sectors, including environmental monitoring, agriculture, and urban planning, as organizations increasingly seek actionable insights from multi-source geospatial datasets.

The exponential increase in the volume and diversity of EO data sources has emerged as a primary growth factor for the EO Data Harmonization Pipelines market. Organizations now rely on satellite imagery, aerial photographs, UAV data, and ground-based sensors to monitor and analyze dynamic terrestrial and atmospheric phenomena. However, the heterogeneity and varying formats of these datasets have posed significant challenges for seamless integration and analysis. The development and adoption of sophisticated EO data harmonization pipelines have become essential, enabling the conversion, standardization, and fusion of disparate data streams into coherent, analysis-ready datasets. This capability not only enhances the accuracy and reliability of downstream analytics but also accelerates decision-making processes in critical domains such as disaster management, climate change assessment, and precision agriculture.

Another pivotal driver is the rapid technological advancement in cloud computing, artificial intelligence, and machine learning, which has revolutionized the EO data harmonization landscape. Cloud-based platforms now offer scalable, on-demand processing power, allowing for real-time harmonization of massive EO datasets. AI-powered algorithms automate data cleansing, normalization, and feature extraction, significantly reducing manual intervention and operational costs. These innovations have democratized access to EO data harmonization solutions, making them accessible to a broader spectrum of end-users, from government agencies and research institutes to commercial enterprises. The integration of these advanced technologies not only improves the efficiency of EO data pipelines but also opens new avenues for developing predictive models and geospatial intelligence solutions.

The increasing focus on sustainability and environmental stewardship has further amplified the demand for EO data harmonization pipelines. Governments and international organizations are investing heavily in monitoring land use, water resources, and atmospheric conditions to meet regulatory requirements and inform policy decisions. Harmonized EO data enables comprehensive, cross-border analyses that are vital for addressing global challenges such as deforestation, urban sprawl, and natural disasters. As regulatory frameworks around data quality and interoperability become more stringent, organizations are compelled to invest in robust harmonization solutions to ensure compliance and maintain data integrity. This regulatory push, combined with growing public and private sector awareness of the value of harmonized EO data, is expected to sustain market growth over the coming decade.

Regionally, North America and Europe continue to dominate the EO Data Harmonization Pipelines market, accounting for a combined market share of over 60% in 2024. The United States, in particular, benefits from a mature geospatial technology ecosystem and significant investments in satellite infrastructure. Meanwhile, the Asia Pacific region is witnessing the fastest growth, driven by expanding EO satellite programs in China, India, and Japan, coupled with increasing adoption of cloud-based geospatial solutions. Latin America and the Middle East & Africa are gradually emerging as promising markets, propelled by investments in environmental monitoring and disaster management initiatives. As these regions enhance their EO capabilities, the global market is poised for sustained expansion.

Component Analysis

The EO Data Harmonization Pipelines market by component is segmented into software, hardware, and services. Software solutions remain the largest segment, accounting for over 45% of the market share in 2024. These platforms are integral for the automated ingestion, normalization, and fusio

This dataset, referred to as LUH2-GCB2019, includes 0.25-degree gridded, global maps of fractional land-use states, transitions, and management practices for the period 0850-2019. The LUH2-GCB2019 dataset is an update to the previous Land-Use Harmonization Version 2 (LUH2-GCB) datasets prepared as required input to land models in the annual Global Carbon Budget (GCB) assessments, including land-use change data relating to agricultural expansion, deforestation, wood harvesting, shifting cultivation, afforestation, and crop rotations. Compared with previous LUH2-GCB datasets, the LUH2-GCB2019 takes advantage of new data inputs that corrected cropland and grazing areas in the globally important region of Brazil, as far back as 1950. LUH2-GCB datasets are used by bookkeeping models and Dynamic Global Vegetation Models (DGVMs) for the GCB.

From Lisk et al. (in review): "In the arid and semi-arid western U.S., access to water is regulated through a legal system of water rights. Individuals, companies, organizations, municipalities, and tribal entities have documents that declare their water rights. State water regulatory agencies collate and maintain these records, which can be used in legal disputes over access to water. While these records are publicly available data in all western U.S. states, the data have not yet been readily available in digital form from all states. Furthermore, there are many differences in data format, terminology, and definitions between state water regulatory agencies. Here, we have collected water rights data from 11 western U.S. state agencies, harmonized terminology and use definitions, formatted them consistently, and tied them to a western U.S.-wide shapefile of water administrative boundaries. We demonstrate how these data enable consistent regional-scale western U.S. hydrologic and economic modeling."

Abstract of associated article: This study is an impact analysis of European Union (EU) energy efficiency policy that employs both top-down energy consumption data and bottom-up energy efficiency statistics or indicators. As such, it may be considered a contribution to the effort called for in the EU's 2006 Energy Services Directive (ESD) to develop a harmonized calculation model. Although this study does not estimate the realized savings from individual policy measures, it does provide estimates of realized energy savings for energy efficiency policy measures in aggregate. Using fixed effects panel models, the annual cumulative savings in 2011 of combined household and manufacturing sector electricity and natural gas usage attributed to EU energy efficiency policies since 2000 is estimated to be 1136PJ; the savings attributed to energy efficiency policies since 2006 is estimated to be 807PJ, or the equivalent of 5.6% of 2011 EU energy consumption. As well as its contribution to energy efficiency policy analysis, this study adds to the development of methods that can improve the quality of information provided by standardized energy efficiency and sustainable resource indexes.

Within the ESA funded WorldCereal project we have built an open harmonized reference data repository at global extent for model training or product validation in support of land cover and crop type mapping. Data from 2017 onwards were collected from many different sources and then harmonized, annotated and evaluated. These steps are explained in the harmonization protocol (10.5281/zenodo.7584463). This protocol also clarifies the naming convention of the shape files and the WorldCereal attributes (LC, CT, IRR, valtime and sampleID) that were added to the original data sets.

This publication includes those harmonized data sets of which the original data set was published under the CC-BY license or a license similar to CC-BY. See document "_In-situ-data-World-Cereal - license - CC-BY.pdf" for an overview of the original data sets.