Efforts and Innovations to Promote Data Sharing and Data Accessibility in the EGS Collab Experiments
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Efforts and Innovations to Promote Data Sharing and Data Accessibility in the EGS Collab Experiments
Large-scale scientific research programs such as the EGS Collab experiments and the FORGE program play extremely important roles in advancing geothermal technologies. Such efforts involve a large number of researchers from multiple institutions, last multiple years, and generate large, complex datasets. The value of the research efforts is only realized when the datasets are used by a large community of researchers in the decades to come. A challenge is that due to the complexity of the data, it could require a user to devote a serious effort into understanding the data before the data can be effectively utilized. In the EGS Collab experiments, the team has devoted remarkable efforts and developed many innovative solutions to make the data more accessible to broader team members and future users. This paper documents the experience gained and lessons learned by the EGS Collab team in disseminating the data in the most informative and inspiring forms to maximize the value of this precious dataset. "Accessibility"in the title does not only mean making the raw data available for download. Particularly, we want to emphasize the importance of organizing, annotating, and presenting the data in ways to make it easy to digest by prospective consumers of the data.
circulation↗
Efforts and Innovations to Promote Data Sharing and Data Accessibility in the EGS Collab Experiments.
Abstract not provided.
Fu, Pengcheng↗
Deliverable D11 – Data Sharing, Storage, Security Protocols, and a Specification of a Potential Data Sharing Portal
Pacific Northwest National Laboratory (PNNL) and Technical University of Denmark (DTU) completed this deliverable as part of Work Package 2: Data Information Catalog for Distributed Wind Research for the International Energy Agency (IEA) Wind Technology Collaboration Programme Task 41: Enabling Wind to Contribute to a Distributed Energy Future. As part of the work plan, Deliverable D11 requires the development of data sharing, storage, and security protocols for metadata to be stored on the platform, if needed. The specification of a potential data sharing portal that expands on the catalog is also required.
17 WIND ENERGY↗
Parallel checksumming of data chunks of a shared data object using a log-structured file system
Checksum values are generated and used to verify the data integrity. A client executing in a parallel computing system stores a data chunk to a shared data object on a storage node in the parallel computing system. The client determines a checksum value for the data chunk; and provides the checksum value with the data chunk to the storage node that stores the shared object. The data chunk can be stored on the storage node with the corresponding checksum value as part of the shared object. The storage node may be part of a Parallel Log-Structured File System (PLFS), and the client may comprise, for example, a Log-Structured File System client on a compute node or burst buffer. The checksum value can be evaluated when the data chunk is read from the storage node to verify the integrity of the data that is read.
97 MATHEMATICS AND COMPUTING↗
Livewire Data Platform - A Solution for Energy Efficient Mobility Systems (EEMS) Data Sharing
The Livewire Data Platform is a data storage and exchange platform. It provides: a platform, allowing data sharing and discovery by linking disparate data sources to a single website via application programming interfaces (APIs); a community, building partnerships and collaboration rather than competition; a system, allowing shared data to increase in size and complexity as EEMS evolves.
47 OTHER INSTRUMENTATION↗
Parallel compression of data chunks of a shared data object using a log-structured file system
Techniques are provided for parallel compression of data chunks being written to a shared object. A client executing on a compute node or a burst buffer node in a parallel computing system stores a data chunk generated by the parallel computing system to a shared data object on a storage node by compressing the data chunk; and providing the data compressed data chunk to the storage node that stores the shared object. The client and storage node may employ Log-Structured File techniques. The compressed data chunk can be de-compressed by the client when the data chunk is read. A storage node stores a data chunk as part of a shared object by receiving a compressed version of the data chunk from a compute node; and storing the compressed version of the data chunk to the shared data object on the storage node.
97 MATHEMATICS AND COMPUTING↗
Data Standards for Omics Data: the Basis of Data Sharing and Reuse.
To facilitate sharing of Omics data, many groups of scientists have been working to establish the relevant data standards. The main components of data sharing standards are experiment description standards, data exchange standards, terminology standards, and experiment execution standards. Here we provide a survey of existing and emerging standards that are intended to assist the free and open exchange of large-format data.
59 BASIC BIOLOGICAL SCIENCES↗
Light-weight cache coherence for data processors with limited data sharing
A data processing system includes a plurality of processors, local memories associated with a corresponding processor, and at least one inter-processor link. In response to a first processor performing a load or store operation on an address of a corresponding local memory that is not currently in the local cache, a local cache allocates a first cache line and encodes a local state with the first cache line. In response to a load operation from an address of a remote memory that is not currently in the local cache, the local cache allocates a second cache line and encodes a remote state with the second cache line. The first processor performs subsequent loads and stores on the first cache line in the local cache in response to the local state, and subsequent loads from the second cache line in the local cache in response to the remote state.
97 MATHEMATICS AND COMPUTING↗
NARUC grid data sharing playbook
In 2022, the National Association of Regulatory Utility Commissioners (NARUC) launched an initiative to support its members in addressing issues related to grid data sharing. The Grid Data Sharing Collaborative was funded by the DOE’s Office of Electricity and Office of Cybersecurity, Energy Security, and Emergency Response (CESER). NARUC invited programmatic, policy, technical, and cybersecurity subject matter experts from public utility commissions, utilities, non-governmental organizations, energy service companies, and DOE to join the two-year Grid Data Sharing Collaborative to help develop a flexible framework for states to use as a starting point when navigating complex decision-making inherent in grid data sharing. The framework took shape through a series of intensive workshops during which Collaborative participants explored illustrative use cases to identify data needs, articulate the benefits and risks of sharing such data, and assess the trade-offs. Along the way, participants offered suggestions for how the framework could be used in practice. The purpose of this playbook is to describe the elements of the Grid Data Sharing Framework and to begin supporting its implementation.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Data Sharing and Scientific Impact in Eddy Covariance Research
Do the benefits of data sharing outweigh its perceived costs? This is a critical question, and one with the potential to change culture and behavior. Dai et al. (2018) examine how data sharing is related to scientific impact in the field of eddy covariance (EC), and find that data sharers are disproportionately high-impact researchers, and vice versa; they also note strong regional differences in EC data sharing norms. The current policies and restrictions of EC journals and repositories are highly uneven. Incentivizing data sharing and enhancing computational reproducibility are critical next steps for EC, ecology, and science more broadly.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
Livewire Data Platform - A Solution for EEMS Data Sharing
To bring about fundamental changes in our transportation system, data sharing must be endemic. The Livewire Data Platform is a user-friendly, searchable platform allowing easy and secure data sharing and discovery for DOE's Energy Efficient Mobility Systems (EEMS) researchers. The platform was funded by the DOE Vehicle Technologies Office (VTO) to enable a research community that builds partnerships and collaboration around data in order to find answers to today's biggest mobility challenges.
33 ADVANCED PROPULSION SYSTEMS↗
The Importance of Sharing Data in Systems Biology
Systems biology research spans a range of biological scales and science domains, and often requires a collaborative effort to collect and share data so that integration is possible. However, sharing data effectively is a challenging task that requires effort and alignment between collaborative partners, as well as coordination between organizations, repositories, and journals. As a community of systems biology researchers, we must get better at efficiently sharing data, and ensuring that shared data comes with the recognition and citations it deserves.
Wood-Charlson, Elisha M. (ORCID:0000000195577715)↗
AMRZone: A Runtime AMR Data Sharing Framework For Scientific Applications
Frameworks that facilitate runtime data sharing across multiple applications are of great importance for scientific data analytics. Although existing frameworks work well over uniform mesh data, they can not effectively handle adaptive mesh refinement (AMR) data. Among the challenges to construct an AMR-capable framework include: (1) designing an architecture that facilitates online AMR data management; (2) achieving a load-balanced AMR data distribution for the data staging space at runtime; and (3) building an effective online index to support the unique spatial data retrieval requirements for AMR data. Towards addressing these challenges to support runtime AMR data sharing across scientific applications, we present the AMRZone framework. Experiments over real-world AMR datasets demonstrate AMRZone's effectiveness at achieving a balanced workload distribution, reading/writing large-scale datasets with thousands of parallel processes, and satisfying queries with spatial constraints. Moreover, AMRZone's performance and scalability are even comparable with existing state-of-the-art work when tested over uniform mesh data with up to 16384 cores; in the best case, our framework achieves a 46% performance improvement.
Zhang, Wenzhao↗
Outcomes of PAX sapiens-Supported Global Wildlife Data Sharing Conferences for Enhanced One Health Security (GWDSC)
Across two consecutive Global Wildlife Data Sharing Conferences supported by PAX sapiens—Year 1 (May 2024) at Pacific Northwest National Laboratory and Year 2 (2025) in Ciudad Real, Spain—the initiative converted wildlife data sharing from aspiration into operational reality, producing measurable impacts in platform development, data mobilization, standards harmonization, and international partnership formation. The conferences addressed a critical gap in global health security: while 75% of emerging infectious diseases affect both humans and animals and over 60% originate in wildlife, wildlife health surveillance has historically lagged behind human and agricultural sectors due to fragmented databases, inconsistent terminology, uneven capacity, and limited cross-border coordination. By convening practitioners, government agencies, international organizations, academic institutions, and NGOs, the GWDSC catalyzed trust-based relationships and practical workflows that enable earlier detection, better risk assessment, and more effective prevention of threats at the wildlife–domestic animal–human–environment interface.
54 ENVIRONMENTAL SCIENCES↗
The Bermuda Triangle: The Pragmatics, Policies, and Principles for Data Sharing in the History of the Human Genome Project
The Bermuda Principles for DNA sequence data sharing are an enduring legacy of the Human Genome Project (HGP). They were adopted by the HGP at a strategy meeting in Bermuda in February of 1996 and implemented in formal policies by early 1998, mandating daily release of HGP-funded DNA sequences into the public domain. The idea of daily sharing, we argue, emanated directly from strategies for large, goal-directed molecular biology projects first tested within the “community” of C. elegans researchers, and were introduced and defended for the HGP by the nematode biologists John Sulston and Robert Waterston. In the C. elegans community, and subsequently in the HGP, daily sharing served the pragmatic goals of quality control and project coordination. Yet in the HGP human genome, we also argue, the Bermuda Principles addressed concerns about gene patents impeding scientific advancement, and were aspirational and flexible in implementation and justification. They endured as an archetype for how rapid data sharing could be realized and rationalized, and permitted adaptation to the needs of various scientific communities. Yet in addition to the support of Sulston and Waterston, their adoption also depended on the clout of administrators at the US National Institutes of Health (NIH) and the UK nonprofit charity the Wellcome Trust, which together funded 90% of the HGP human sequencing effort. The other nations wishing to remain in the HGP consortium had to accommodate to the Bermuda Principles, requiring exceptions from incompatible existing or pending data access policies for publicly funded research in Germany, Japan, and France. We begin this story in 1963, with the biologist Sydney Brenner’s proposal for a nematode research program at the Laboratory of Molecular Biology (LMB) at the University of Cambridge. We continue through 2003, with the completion of the HGP human reference genome, and conclude with observations about policy and the historiography of molecular biology.
59 BASIC BIOLOGICAL SCIENCES↗
MAPSTER: Automated Geospatial Data Sharing – Version 1.4.0
The US Department of Energy’s (DOE) Oak Ridge National Laboratory (ORNL) developed MAPSTER which is a geospatial data management tool that aggregates, organizes, and shares data from dispersed sources such as unmanned aerial systems (UAS). Built specifically for use in environments where communications may be limited, MAPSTER utilizes two key technologies to effectively manage data in the field and enable easy data sharing with authorized partners: Observer and Checkpoint. Observer is a lightweight software package on an edge device, such as a laptop, that automatically detects newly processed UAS data and sends to a central server called Checkpoint. Checkpoint is a centralized server at ORNL that receives and manages data from all Observer instances. Even in a very low bandwidth environment, Observer can still send information about the UAS data product almost instantly as it generates its own metadata package on the size of KB (kilobytes). MAPSTER is not only for UAS data but for any geospatial data collected at the austere edge and dispersed sources.
97 MATHEMATICS AND COMPUTING↗
Data Format and Descriptions for the Alabama Carbon Storage: Data Sharing and Engagement Project
The Alabama Carbon Storage: Data Sharing and Engagement (ACS-DSE) project seeks to develop publicly accessible geologic carbon storage models and data across the southern Gulf Coastal Plain of Alabama. The public online platform developed for this project will include geologic, geophysical, infrastructure, and other relevant datasets and geologic models of the study area. Datasets, model surfaces (e.g. structural contour maps, isolith maps, porosity maps), and infrastructure data (e.g. offshore pipelines, field boundaries) will be downloadable in commonly used file formats. The anticipated primary geologic datasets are well headers, formation tops, average reservoir properties, and core analyses; these will be available as commaseparated values (CSV) text files and MS Excel workbooks. Geophysical logs will be available in Log ASCII Standard (LAS) file format. Modeled surfaces, such as structure contour maps, will be available in ArcGIS formats and text files. Infrastructure data will be available as ArcGIS shapefiles. This document provides information on the data sources and attributes of the datasets.
01 COAL, LIGNITE, AND PEAT↗