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Gene regulation plays a fundamental role in shaping tissue identity, function, and response to perturbation. Regulatory processes are controlled by complex networks of interacting elements, including transcription factors, miRNAs and their target genes. The structure of these networks helps to determine phenotypes and can ultimately influence the development of disease or response to therapy. We developed GRAND (https://grand.networkmedicine.org) as a database for computationally-inferred, context-specific gene regulatory network models that can be compared between biological states, or used to predict which drugs produce changes in regulatory network structure. The database includes 12 468 genome-scale networks covering 36 human tissues, 28 cancers, 1378 unperturbed cell lines, as well as 173 013 TF and gene targeting scores for 2858 small molecule-induced cell line perturbation paired with phenotypic information. GRAND allows the networks to be queried using phenotypic information and visualized using a variety of interactive tools. In addition, it includes a web application that matches disease states to potentially therapeutic small molecule drugs using regulatory network properties.

The MIBiG (Minimum Information about a Biosynthetic Gene Cluster) Repository stores Biosynthetic Gene Clusters (BGC) that are manually curated through community efforts. It is a central reference database for BGCs of known function, and provides the basis for comparative analyses for a number of tools. It has enabled many computational analyses of BGC function and novelty central to both small and large-scale studies of microbes and microbial communities.

The Omics Discovery Index (OmicsDI) provides dataset discovery across a heterogeneous, distributed group of Transcriptomics, Genomics, Proteomics and Metabolomics data resources, including both open and controlled access data resources. The resource provides a short description of every dataset, including accession, description, sample/data protocols biological evidences, and publication. Based on these metadata, OmicsDI provides extensive search capabilities, as well as identification of related datasets by metadata and data content where possible. In particular, OmicsDI identifies groups of related, multi-omics datasets across repositories by shared identifiers.

IEEE DataPort™ is an online data platform created, owned, and supported by IEEE, a technical professional organization. The web-based platform enables researchers to store, share, access and manage research data. IEEE DataPort™ provides a DOI for each dataset, supports Open Access/Open Science initiatives, enables dataset owners to retain copyright and ownership rights, supplies data management workflows, and and provides 2TB/dataset storage capacity for individuals and 10TB/dataset storage capacity for members of institutional subscribers. All datasets are stored in Amazon AWS S3 to simplify dataset access, analysis and management. Standard datasets may be uploaded for free, and will then be available to DataPort™ subscribers. Data owners can also pay a fee to publish an "Open Access" dataset that is available to all IEEE DataPort™ users without requiring a paid subscription or membership. DataPort™ subscribers can also host private or public data competitions that provide non-subscribers with time-limited access to applicable dataset files.

This data contains data on gender involvement in agricultural land exploitation for coffee farming households in the national coffee development area of Bantaeng Regency, South Sulawesi Province

HuSiDa is a public database that serves as a depository for both, sequences of published functional siRNA molecules targeting human genes and important technical details of the corresponding gene silencing experiments. It aims at supporting the setup and actual procedure of specific RNAi experiments in human cells.

The Water Quality Portal (WQP) integrates publicly available water quality data from the USGS National Water Information System (NWIS), the EPA STOrage and RETrieval (STORET) Data Warehouse, and the USDA ARS Sustaining The Earth’s Watersheds - Agricultural Research Database System (STEWARDS) .

The Distributed Archives for Neurophysiology Data Integration (DANDI) is a platform for publishing, sharing, and processing neurophysiology data funded by the BRAIN Initiative. The archive accepts cellular neurophysiology data including electrophysiology, optophysiology, and behavioral time-series, and images from immunostaining experiments.