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At least 235 records · Page 13

MINERvA s Open Data Product: A First for Neutrino Data Preservation

Access to information on neutrino nucleus interactions is critical to the success of all neutrino oscillation experiments. MINERvA's rich dataset covers a range of energies and nuclei unique amongst experiments, and as such is critical to the community in building the important shared knowledge needed to unravel the mysteries of the neutrino. In particular, its dataset provides the greatest statistical coverage in in the range of neutrino energies pertinent for DUNE until DUNE's near detector begins operation. Historically, such significant datasets in neutrino physics have been preserved primarily through their published results. While meaningful and useful, this limits the ability to explore the data to its fullest extent as new perspectives continue to form. MINERvA has undertaken a major effort to break this trend and preserve its data in a format to be as analyzable as possible from outside the collaboration. This has culminated in the officially-released MINERvA Open Data Product for the community to take advantage of and utilize. Maintaining direct access to the dataset in an analyzable form will allow new insights to continue to be extracted indefinitely. This talk will cover the contents of this product, the information included (and excluded), the tools provided to utilize the product effectively, the support MINERvA intends to provide in its use, and some lessons learned through the process.

Last, David [Rochester U.] (ORCID:0000000245147183↗

Enriching OpenStreetMap network data for transportation applications: Insights into the impact of urban congestion on accessibility

OpenStreetMap (OSM) data is a valuable open-source resource for various transportation, traffic, and planning applications. However, OSM network data lack operating traffic speed information, which is critical for transport planning and operations. Addressing this shortcoming, this study leverages commercial vendor data (to serve as ground truth) with exogenous, open-source variables characterizing local transport infrastructure, land use, and demographic information to predict average congested traffic speeds on OSM networks. Three machine-learning models were tested and estimated for OSM links with and without speed limit information in the Denver metropolitan region. Among these, XGBoost performed best, with mean absolute errors of 3.27 and 3.62 mph for links with and without speed limits, respectively. The developed models accurately predicted traffic speeds for different hours and days of the week compared to ground truth data. Using these predicted speeds, drive accessibility scores were computed for the Denver region for different time periods using the Mobility Energy Productivity (MEP) metric to understand the impact of congestion on energy-efficient accessibility. Results show that congestion-adjusted drive accessibility can be significantly lower compared to accessibility calculated using free flow speeds. Specifically, weekday evening hours saw a 42 % drop in accessibility due to reduced speeds, particularly around downtown Denver. Across the Denver metro region, approximately half as many opportunities and jobs are accessible in under 20 min by car during the evening peak period relative to free flow conditions. These findings underscore the importance of using congestion-adjusted operating speeds rather than speed limits in accessibility calculations, as reliance on speed limits can substantially overestimate energy-efficient drive accessibility in large, car-centric cities susceptible to significant congestion. In conclusion, the methodology presented here could further enrich OSM network data, making them useful for an even broader range of transportation applications.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

mzPeak: Designing a Scalable, Interoperable, and Future-Ready Mass Spectrometry Data Format

Advances in mass spectrometry (MS) instrumentation, such as higher resolution, faster scan speeds, and improved sensitivity, have significantly increased the volume and complexity of data. The growing adoption of imaging and ion mobility further amplifies these challenges across MS-based omics fields, including proteomics, metabolomics, and lipidomics. While these technologies unlock new possibilities, they also present significant challenges in data management, storage, and accessibility. Existing open formats, such as the XML-based community standards mzML and imzML, struggle to meet the demands of modern MS workflows due to their large file sizes, slow data access, and limited metadata support. Vendor-specific formats, while optimized for proprietary instruments, lack interoperability, comprehensive metadata support and long-term archival reliability. This white paper lays the groundwork for mzPeak, a next-generation community data format designed to address these challenges and support high-throughput, multi-dimensional MS workflows. By adopting a hybrid model that combines efficient binary storage for numerical data and both human and machine-readable metadata storage, mzPeak will reduce file sizes, accelerate data access, and offer a scalable, adaptable solution for evolving MS technologies. For researchers, mzPeak will enable enhanced interoperability across platforms, seamless support for complex workflows including ion mobility and MS imaging, and faster data access compared to existing community formats such as mzML. Its design will ensure data is managed in compliance with regulatory standards, essential for applications such as precision medicine and chemical safety, where long-term data integrity and accessibility are critical. For vendors, mzPeak provides a streamlined, open alternative to proprietary formats, reducing the burden of regulatory compliance while aligning with the industry's push for transparency and standardization. By offering a high-performance, interoperable solution, mzPeak positions vendors to meet customer demands for sustainable data management tools which will be able to handle emerging and future data types and workflows. mzPeak aspires to become the cornerstone of MS data management, empowering researchers, vendors, and developers to innovate and collaborate more effectively.

data formats↗

sciCAN: single-cell chromatin accessibility and gene expression data integration via cycle-consistent adversarial network

The boom in single-cell technologies has brought a surge of high dimensional data that come from different sources and represent cellular systems from different views. With advances in these single-cell technologies, integrating single-cell data across modalities arises as a new computational challenge. Here, we present an adversarial approach, sciCAN, to integrate single-cell chromatin accessibility and gene expression data in an unsupervised manner. We benchmarked sciCAN with 5 existing methods in 5 scATAC-seq/scRNA-seq datasets, and we demonstrated that our method dealt with data integration with consistent performance across datasets and better balance of mutual transferring between modalities than the other 5 existing methods. We further applied sciCAN to 10X Multiome data and confirmed that the integrated representation preserves biological relationships within the hematopoietic hierarchy. Finally, we investigated CRISPR-perturbed single-cell K562 ATAC-seq and RNA-seq data to identify cells with related responses to different perturbations in these different modalities.

59 BASIC BIOLOGICAL SCIENCES↗

Joint Modeling of Access Mode and Parking Choice of Air Travelers Using Revealed Preference Data

Airport ground access mode choice is distinct from everyday mode choice decisions, necessitating context-specific choice model estimation. Understanding airport ground access mode choice decisions is not only important for developing infrastructure planning strategies, but also for assessing the impacts of emerging modes on airport revenues, particularly from parking. However, parking choice is an often-overlooked dimension in airport ground access choice modeling. This paper addresses this gap through the development of a joint model of airport access mode and parking option choice using a passenger survey conducted at Dallas-Fort Worth (DFW) International Airport in 2015. Compared with a traditional conditional logit model that does not consider parking options available at DFW airport, the joint model of mode and parking decisions was found to generate more realistic values of travel time and was shown to have better predictive performance, both of which are critical for obtaining better airport parking revenue estimates and identifying traveler cohorts who may respond more strongly to potential policies targeting curb congestion and parking demand.

33 ADVANCED PROPULSION SYSTEMS↗

A bi-level data-driven framework for fault-detection and diagnosis of HVAC systems

Long-term operation of heating, ventilation, and air conditioning (HVAC) systems will eventually lead to a range of HVAC system failures, resulting in excessive energy consumption and maintenance costs. Here, to avoid HVAC malfunctioning, fault detection diagnostic (FDD) is utilized as a common practice. Machine learning methods have lately received considerable interest for FDD analysis of HVAC systems due to their high detection accuracy. Meanwhile, HVAC malfunctions are regarded as rare occurrences, hence normal operating data samples are much more accessible than data samples in faulty and malfunctioning conditions. The dominating frequency of normal operation in HVAC datasets has also led to heavily biased classification algorithms within the literature. Moreover, the focus of previous literature has been on increasing the accuracy of the models which leads to a high number of false positives (misleading alarms) in the system. In order to enhance the performance of diagnostic procedures and fill the mentioned gaps, this study proposes a novel data-driven framework. A bi-level machine learning framework is developed for diagnosing faults in air handling units (AHUs) and rooftop units (RTUs) based on principal component analysis (PCA), time series anomaly detection, and random forest (RF). It is shown that PCA can reduce the dataset dimension with one principal component accounting for 95% of data variance. Also, the random forest could classify the faults with 89% precision for single-zone AHU, 85% precision for RTU, and 79% for multi-zone AHU. By proposing this framework, three persistent challenges are addressed: (I) minimizing false positives; (II) accounting for data imbalance; and (III) normal condition monitoring of equipment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Electricity Baseline 2022 Background Data and Log File

The ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0) was used to generate the 2022 electricity baseline: a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data. ElectricityLCI implements a local data store for downloading and accessing public data on an individual's computer. The data store follows the folder definition provided by USEPA's esupy Python package (https://github.com/USEPA/esupy), which utilized the appdirs Python dependency (https://pypi.org/project/appdirs/). This submission includes the background data used to generate the 2022 electricity baseline inventory. Each zip archive stores the source files as found in their data stores. Sub-folders in each of the data stores are archived separately. For example, stewi.zip contains the JSON files, while stewi.facility.zip is the 'facility' sub-folder of stewi data store that stores the parquet files. To reproduce the data store, extract each zip file and drag-and-drop sub-folders in to their appropriate root folders to recreate the data stores, then copy the root folders to your data store folder (as returned by running the following on the command line: `python -c "import appdirs; print(appdirs.user_data_dir())"`). The main five data stores include: 'electricitylci', 'facilitymatcher', 'fedelemflowlist', 'stewi', and 'stewicombo'. The log file generated by the 2022 model run is also included, which contains the statements at the DEBUG level and above.

Electricity; LCA; data inventory↗

Electricity Baseline 2021 Background Data and Log File

The ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0) was used to generate the 2021 electricity baseline: a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data. ElectricityLCI implements a local data store for downloading and accessing public data on an individual's computer. The data store follows the folder definition provided by USEPA's esupy Python package (https://github.com/USEPA/esupy), which utilizes the appdirs Python dependency (https://pypi.org/project/appdirs/). An overview of the ElectricityLCI data stores may be found on the README (https://github.com/USEPA/ElectricityLCI/blob/v2.0/README.md#data-store). This submission includes the background data used to generate the 2021 electricity baseline inventory. Each zip archive stores the source files as found in their data stores. Sub-folders in each of the data stores are archived separately. For example, stewi.zip contains the JSON files, while stewi.facility.zip is the 'facility' sub-folder of stewi data store that stores the parquet files. To reproduce the data store, extract each zip file and drag-and-drop sub-folders in to their appropriate root folders to recreate the data stores, then copy the root folders to your data store folder (as returned by running the following on the command line: python -c "import appdirs; print(appdirs.user_data_dir())"). The main five data stores include: 'electricitylci', 'facilitymatcher', 'fedelemflowlist', 'stewi', and 'stewicombo'. The log file generated by the 2021 model run is also included, which contains the statements at the DEBUG level and above.

Electricity; LCA; LCI; Life Cycle; data inventory↗

Electricity Baseline 2020 Background Data and Log File

The ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0) was used to generate the 2020 electricity baseline: a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data. ElectricityLCI implements a local data store for downloading and accessing public data on an individual's computer. The data store follows the folder definition provided by USEPA's esupy Python package (https://github.com/USEPA/esupy), which utilizes the appdirs Python dependency (https://pypi.org/project/appdirs/). An overview of the ElectricityLCI data stores may be found on the README (https://github.com/USEPA/ElectricityLCI/blob/v2.0/README.md#data-store). This submission includes the background data used to generate the 2020 electricity baseline inventory. Each zip archive stores the source files as found in their data stores. Sub-folders in each of the data stores are archived separately. For example, stewi.zip contains the JSON files, while stewi.facility.zip is the 'facility' sub-folder of stewi data store that stores the parquet files. To reproduce the data store, extract each zip file and drag-and-drop sub-folders in to their appropriate root folders to recreate the data stores, then copy the root folders to your data store folder (as returned by running the following on the command line: python -c "import appdirs; print(appdirs.user_data_dir())"). The main five data stores include: 'electricitylci', 'facilitymatcher', 'fedelemflowlist', 'stewi', and 'stewicombo'. The log file generated by the 2020 model run is also included, which contains the statements at the DEBUG level and above.

Electricity; LCA; LCI; Life Cycle; data inventory↗

FAIR Framework for Physics-Inspired AI in High Energy Physics (Final Technical Report)

The main deliverable of this proposal was to publish data from high energy physics experiments in a FAIR format so that non-specialists could develop machine learning technologies using our data. The Minnesota team of Profs. Cushman, Furmanski and Rusack, from the high energy experiments CDMS, Micro-Boone and CMS, respectively, and Prof J. Sun from Computer Science worked to organize the data, to provide code to access the data, and where relevant provide documentation describing the data. The FAIR4HEP collaboration was formed with groups from UC San Diego, MIT, and the University of Illinois, with the principal investigator was Dr. Huerta. Collectively we collaborated on the publication of datasets from the LHC experiments. Members of the Minnesota group contributed to the common papers published by the collaboration

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DESC DC2 Data Release Note

In preparation for cosmological analyses of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), the LSST Dark Energy Science Collaboration (LSST DESC) has created a 300 deg$^2$ simulated survey as part of an effort called Data Challenge 2 (DC2). The DC2 simulated sky survey, in six optical bands with observations following a reference LSST observing cadence, was processed with the LSST Science Pipelines (19.0.0). In this Note, we describe the public data release of the resulting object catalogs for the coadded images of five years of simulated observations along with associated truth catalogs. We include a brief description of the major features of the available data sets. To enable convenient access to the data products, we have developed a web portal connected to Globus data services. We describe how to access the data and provide example Jupyter Notebooks in Python to aid first interactions with the data. We welcome feedback and questions about the data release via a GitHub repository.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Connecting People to Data: Enabling Data Connected Communities through Enhancements to the Geothermal Data Repository: Preprint

The Department of Energy's (DOE) Geothermal Data Repository (GDR) has implemented a series of new features designed to connect people to data. These features, which are based on feedback from the GDR user community and surveys of the greater geothermal research community, are designed to improve data quality and empower members of all communities to better engage with geothermal data resources by providing universal access to data and by improving the connections between data providers, subject matter experts, and the communities of people using GDR data. This paper will explore some of the recent enhancements made to the GDR to improve data discoverability, reduce submission time, and result in better quality data submissions. These improvements include the ability for users to save a list of their favorite datasets, search for insight into geothermal datasets or data availability, or sign up to receive notifications of future updates to specific datasets. These improvements aim to enhance the overall user experience of the GDR while further connecting communities to the data they need to inform decisions, advance geothermal research, and develop innovative solutions to local energy problems.

DOE↗

The Galaxy platform for accessible, reproducible, and collaborative data analyses: 2024 update

Galaxy (https://galaxyproject.org) is deployed globally, predominantly through free-to-use services, supporting user-driven research that broadens in scope each year. Users are attracted to public Galaxy services by platform stability, tool and reference dataset diversity, training, support and integration, which enables complex, reproducible, shareable data analysis. Applying the principles of user experience design (UXD), has driven improvements in accessibility, tool discoverability through Galaxy Labs/subdomains, and a redesigned Galaxy ToolShed. Galaxy tool capabilities are progressing in two strategic directions: integrating general purpose graphical processing units (GPGPU) access for cutting-edge methods, and licensed tool support. Engagement with global research consortia is being increased by developing more workflows in Galaxy and by resourcing the public Galaxy services to run them. The Galaxy Training Network (GTN) portfolio has grown in both size, and accessibility, through learning paths and direct integration with Galaxy tools that feature in training courses. Code development continues in line with the Galaxy Project roadmap, with improvements to job scheduling and the user interface. Environmental impact assessment is also helping engage users and developers, reminding them of their role in sustainability, by displaying estimated CO 2 emissions generated by each Galaxy job.

97 MATHEMATICS AND COMPUTING↗

Geospatial Data Platform for All

Spatiotemporal data has evolved in scale due to augmented use in cross-domain applications. Simultaneously, there is substantial growth in the availability of Geographic Information Systems (GIS) data provided by the United States Geological Survey (USGS) along with other federal, state, county, or local agencies through open-data portals and public access APIs. However, data availability does not equate with accessibility. Large-scale analyses and applications require robust, performant data management with co-location of data storage and computing. The insufficiency of data management infrastructure compels researchers to adopt ad hoc project- specific GIS data storage solutions (e.g., copying data to High-Performance computer file systems). As an ad hoc storage strategy does not scale, it hampers cross-domain analyses causing difficulty in data reuse and utilizing existing code bases. Furthermore, GIS data is complex and requires expertise to analyze and manipulate due to its intricate data structures and data-specific projection transformations. Despite the challenges, we recognize that derived GIS data products, e.g., satellite or LIDAR-based images, can be used in downstream applications such as AI by domain, but non-GIS experts. To address the data needs and overcome the challenges, we are working towards a GIS Data Platform focused on efficient data storage, data discovery and access, and an API to enable common workflows. We propose a knowledge-graph (KG) approach for data discovery, whereby datasets are semantically linked to higher- level constructs such as projects and research areas. The semantic data links enable researchers to explore datasets in a top-down approach by specifying relevant and meaningful terms (assists in finding hidden data). An advantage is that the nodes and edges in a knowledge graph create built-in semantic documentation. Deeper spatiotemporal connections between data sources can be encoded via Graph Neural Networks (GNN) (Zhang et al., 2021). The KG approach can be extended to integrate the data itself in a Virtual KG (VKG). Our work will derive inspiration from large-scale VKG efforts that have been undertaken or are currently underway as part of the OpenStreetMap project (Ding et al., 2021). For DOE Data Days, we share the proposed geospatial data platform hybrid (cloud/on-prem) architecture, our work-to-date on storing, retrieving, and transforming LiDAR and raster data relevant to two important NREL use-cases, including the Renewable Energy Potential (reV) Model, and present our proposal for a KG based data discovery engine.

data platform↗

CO2-Locate: A Dynamic Database and Tool for Accessing National Oil and Gas Well Data to Inform Carbon Storage Projects

The CO2-Locate Database is a growing compilation of publicly available wellbore resources that have been merged based on common attributes across data sources with an attribute schema developed to be consistent across disparate resources, reduce data gaps, and eliminate record redundancy. The first version of CO2-Locate has been published to Energy Data eXchange (EDX) and includes the integrated public wells dataset as well as additional geospatial summary layers of key wellbore characteristics to protect proprietary resources. Additionally, the CO2-Locate database has been deployed into a web application, enabling easy access, data filtering capabilities, and visualization of U.S. wellbore infrastructure by stakeholders to inform injection site selection and risk assessments.

Dyer, Alec S. [NETL Site Support Contractor, Natio↗

Data for Post-Access Report: Tidal Energy Resource Characterization and Model Validation via the Assessment of Cost-effective Spatially Distributed Current Measurements from microFloat Swarms

This project evaluated how high-resolution, spatially distributed field data can be used to refine and validate site-scale hydrodynamic simulations of tidal channels. Use of such spatially-distributed field observations or site-scale hydrodynamic simulations will be needed for producing accurate predictions of tidal energy production over larger arrays of tidal turbines. The data contained in this archive includes field observations of water velocity from a series of short surveys in Agate Pass taken on August 20th, 2020 using station-keeping ADCP (acoustic doppler current profiler) measurements, drifting downward-looking ADCPs, and microFloats, gathered by researchers at APL-UW under a previous project. It also includes high-resolution hydrodynamic model runs for the site during that time generated under TEAMER support. An explanatory file (.pdf format) describes the survey and field data structure. Processed data is stored in .mat format. Survey summary: start time, number of floats, water level. microFloat data: - Cleaned data for each float in survey, - Time series of position (lat., lon.), depth (relative to surface), and horizontal velocity (u, v) Drifting ADCP data: - Cleaned data for each drifting ADCP in survey - Time series of position (lat., lon.), sample depth (relative to surface), and observed water velocity (u,v,w) Stationary ADCP data: - Cleaned data for each station-keeping data set in survey - Time series of position (lat., lon.), sample depth (relative to surface), and observed water velocity (u,v,w) FVCOM (Finite Volume Coastal Ocean Model) model outputs: Water level and velocity, as well as derived variables (e.g., power density) from the final refined model simulations for the Agate Pass subdomain. Contained in NetCDF and ASCII files for the duration of August 18th and 20th 2020 field surveys. Additional scripts for processing the data and generating the figures in the report are included.

16 TIDAL AND WAVE POWER↗

The nucleardatapy toolkit for simple access to experimental nuclear data, astrophysical observations, and theoretical predictions

Systematic comparisons across theoretical predictions for the properties of dense matter, nuclear physics data, and astrophysical observations (also called meta-analyses) are performed. Existing predictions for symmetric nuclear and neutron matter properties are considered, and they are shown in this paper as an illustration of the present knowledge. Asymmetric matter is constructed assuming the isospin asymmetry quadratic approximation. It is employed to predict the pressure at twice saturation energy-density based only on nuclear-physics constraints, and we find it compatible with the one from the gravitational-wave community. To make our meta-analysis transparent, updated in the future, and to publicly share our results, the Python toolkit nucleardatapy is described and released here. Hence, this paper accompanies nucleardatapy, which simplifies access to nuclear-physics data, including theoretical calculations, experimental measurements, and astrophysical observations. This Python toolkit is designed to easily provide data for: (i) predictions for uniform matter (from microscopic or phenomenological approaches); (ii) correlation among nuclear properties induced by experimental and theoretical constraints; (iii) measurements for finite nuclei (nuclear chart, charge radii, neutron skins or nuclear incompressibilities, etc.) and hypernuclei (single particle energies); and (iv) astrophysical observations. This toolkit provides data in a unified format for easy comparison and provides new meta-analysis tools. It will be continuously developed, and we expect contributions from the community in our endeavor.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗