This dataset provides reproduction code and experimental data for the publication "Simulating Quantum State Transfer between Distributed Devices using Noisy Interconnects". The repository contains an exact snapshot of the code version used to generate all results in the paper, ensuring full reproducibility. The repository is organized into three zip archives: code.zip: Contains the code used to generate and evaluate the data. This archive includes a README with instructions on how to use the code and integrate the two data archives. data_quantum_devices.zip:Contains the raw and partially preprocessed experimental data obtained from quantum devices, as well as calibration data for the devices. data_simulations.zip: Contains experimental data generated from simulations. For reference, this dataset includes a separate PDF file for each figure presented in the publication. These files were generated directly from the enclosed code and data, and they serve as benchmarks for visually verifying reproduced results.

DISCLAIMER: The license for this dataset is 'Restrictive License', but please refer to the original sources of the data for licensing information. We are only redistributing it within their limitation.Information_This is the Air Quality Sensor Data Repository as published in the following workhttps://www.arxiv.org/abs/2508.02724The dataset is a zip file sized roughly 25GB. The unzipped data is roughly 70GB of only CSV and JSON data.To abide by the original owners' licensing, we publish only the raw data and provide all code for preprocessing through the following repository:https://github.com/YahiDar/AQ-SDRPlease check the documentation in the repository to further understand the dataset characteristics.We also provide the modeling and machine learning aspect of the work through:https://github.com/YahiDar/VeliLicenses_Each data source has a different license. Please make sure you are using the data appropriately as requested by the original provided.KNMI Data (folder name: /EU_data/KNMI):The original license is CC BY 4.0as documented on their webpage: https://www.knmidata.nl/open-dataLuchtMeetNet data (folder names: /EU_data/lucht_root and /EU_data/luchtmeetnet_csvs):The original license is CC BY-ND 4.0as documented on their webpage: https://www.luchtmeetnet.nl/informatie/download-data/open-dataRIVM SamenMeten data (folder name: /EU_data/crowd_stations_root):The original license is not specified, but it is open to use and redistribute.as documented on their webpage: https://www.samenmeten.nl/international/OpenDataSensor.Community data (folder name: /EU_data/sencom_hourly):The original license is DbCL v1.0as documented on their webpage: https://sensor.community/nl/Taiwan Ministry of Environment data (folder name: /out_of_distribution_downloaded/downloaded_ref):The original license is The Open Government Data License, version 1.0as documented on their webpage: https://data.gov.tw/licensePM2.5 Open Data Portal - LASS (folder name: /out_of_distribution_downloaded/downloaded_lcs):The original license is CC BY-NC-SA 4.0as documented on their webpage: https://pm25.lass-net.org/Acknowledgement_We sincerely thank the Dutch government for supporting this research with the starter grant (startersbeurzen). We also thank the organizations and researchers who provide the open data to enable this research, including the Dutch National Institute for Public Health and the Environment (RIVM), the Dutch Royal Netherlands Meteorological Institute (KNMI), Dr. Ling-Jyh Chen in Taiwan Academia Sinica for the AirBox project, the Taiwan Ministry of Environment, the Sensor.Community platform, and the European Environmental Agency (EEA). We also thank the GGD Amsterdam and RIVM for providing information about how air quality sensor stations work in the Netherlands. We also thank the CREATE Lab at the Robotics Institute at Carnegie Mellon University for the technical support in building the air quality dashboard.

This dataset contains the climatic and hydrological data, together with the model sub-selections generated for the assessment of climate change impacts on multiple basins.
The NetCDF folder includes both raw and bias-corrected climatic and hydrological datasets derived from regional climate model simulations and hydrological model outputs.
The Subselections folder provides the model combinations selected for each basin and sub-selection method, as described in the accompanying article.
Together, these data support the reproducibility of the analyses and enable further exploration of basin-scale responses to climate change.
All files are provided in open formats and are described in the accompanying README file.

The Advanced Technology Microwave Sounder (ATMS) on board Suomi NPP, NOAA-20, and NOAA-21 is a 22 channel microwave sounder that provides continuous cross-track scanning in an 824 km sun-synchronous orbit. This Sensor Data Record (SDR) from ATMS provides Level 1b calibrated and geolocated radiance data produced from the ATMS Raw Data Record (RDR) and Temperature Data Record (TDR). This operational SDR is used to produce ATMS Environmental Data Records (EDR) for measuring atmospheric temperature and moisture. ATMS SDR data obtained from the NOAA Comprehensive Large Array-Data Stewardship System (CLASS) are distributed as aggregated 8 minute files consisting of 15 granules in the Hierarchical Data Format v.5 (HDF5) with metadata attributes included.

This is the data repository containing the data and the R scripts for determining that "Sea surface freshening supresses the thermal tipping point of marine copepods"

SWAP: A Synthetic Dataset for Multi-Step Reasoning with Process Supervision

This repository contains the data for the paper (ACL 25 main) Deliberate Reasoning in Language Models as Structure-Aware Planning with an Accurate World Model. SWAP (Structure-aware Planning) solves complex reasoning by introducing a Generator-Discriminator architecture, and incorporates structural information to guide the reasoning process and provides a verification mechanism over the steps. We generate… See the full description on the dataset page: https://huggingface.co/datasets/sxiong/SWAP.

This webinar described the Division of State Systems' federal automated function repository called “Child welfare SoftWare and Artifacts Pool (C-SWAP)”, and its new feature, the State Technology Profile. The webinar covered the regulatory background of C-SWAP, benefits of C-SWAP, uploading of software to C-SWAP, and completing the State Technology Profile.

Metadata-only record linking to the original dataset. Open original dataset below.

Datasets generated during Jabari Jones' Master's thesis at Utah State University, focused on channel change of Sixth Water Creek and Diamond Fork River, Utah, USA (Jones, J.C., 2018. Historical channel change caused by a century of flow alteration on Sixth Water Creek and Diamond Fork River, UT. Master's thesis, Utah State University). This resource includes data collected in the field as well as data generated in GIS. Field data include cross-section surveys, RTK GPS surveys, sediment transport measurements, bed grain size analysis, and unmanned aerial vehicle (drone) photography. GIS data include shapefiles generated from aerial imagery and digital elevation models. Data were collected and generated between July 2016 and May 2018 All data, metadata and related materials meet the quality standards relative to the purpose for which they were collected and generated.

Number of biospecimen requests by funding source and website year.

Dataset provided for publication in Frontiers in Forests and Global Change : "Climate change increases the severity and duration of soil water stress in the temperate forest of eastern North America".

The National Heart, Lung, and Blood Institute (NHLBI), within the United States’ National Institutes of Health (NIH), established the Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC) in 2008 to develop the infrastructure needed to link the contents of the NHLBI Biorepository and the NHLBI Data Repository, and to promote the utilization of these scientific resources by the broader research community. Program utilization metrics were developed to measure the impact of BioLINCC on Biorepository access by researchers, including visibility, program efficiency, user characteristics, scientific impact, and research types. Input data elements were defined and are continually populated as requests move through the process of initiation through fulfillment and publication. This paper reviews the elements of the tracking metrics which were developed for BioLINCC and reports the results for the first six on-line years of the program.

This datasets provides regional and spatial-explicit gridded data for the analysis presented in the manuscrip "The role of peatland degradation, protection and restoration for climate change mitigation in the SSP scenarios" under review in "Environmental Research: Climate" with reference "ERCL-100126"

Data and model source code for the publication:

Land use change and carbon emissions of a transformation to timber cities
(Nature Communications, 2022)
DOI: 10.1038/s41467-022-32244-w

Abhijeet Mishra1,2,*, Florian Humpenöder1, Galina Churkina1, Christopher P.O. Reyer1, Felicitas Beier1,2, Benjamin Leon Bodirsky1, Hans Joachim Schellnhuber1, Hermann Lotze-Campen1,2, and Alexander Popp1

1 Potsdam Institute for Climate Impact Research (PIK), Member of Leibniz Association, P.O.Box 60 12 03, 14412,6
Potsdam, Germany
2 Humboldt University of Berlin, Department of Agricultural Economics, Unter den Linden 6, 10099 Berlin,8
Germany

Abhijeet Mishra
*mishra@pik-potsdam.de
May 2022

See README.txt for further details.

This dataset tracks the updates made on the dataset "Upcoming Reporting Cadence Change" as a repository for previous versions of the data and metadata.

Aim: Many rare species are dispersal-limited and minimal land use and climate changes can impact their colonization capacity. Most ecological niche models predict the distribution of species under future climate and land use change scenarios without incorporating specie-specific dispersal abilities. Here we investigated the effect of climate and land use change on low vagile species accounting for their dispersal capacity and defined accessible areas in the future.

Location: Europe.

Taxon: Saproxylic beetles.

Methods: We used the current (2007-2012) occurrences of six endangered saproxylics to develop ecological niche models using current climate and land use conditions. We projected species distributions under four future climate and land use change scenarios to estimate their potential occurrences. Finally, accounting for species-specific dispersal, we limited their distributions to accessible areas in 2040-50.

Results: Without accounting for dispersal abilities we found a strong ...

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 tracks the updates made on the dataset "Skilled Nursing Facility Change of Ownership - Owner Information" as a repository for previous versions of the data and metadata.

Publication

This dataset tracks the updates made on the dataset "Hospital Change of Ownership - Owner Information" as a repository for previous versions of the data and metadata.

Training datasets and benchmark data corresponding to the International Conference on Learning Representations (ICLR 2023) paper "A Control-Centric Benchmark for Video Prediction". Please see the GitHub repository at https://github.com/s-tian/vp2 for usage instructions. This dataset includes all data initially uploaded to the Stanford Digital Repository: https://sdr.stanford.edu/works/6335 and https://sdr.stanford.edu/works/6494.