Version 2.0 corrects a coding error in the Process Model of Emotion Regulation Questionnaire (PMERQ) variables.This repository contains:Data set containing self-report and structured clinical interview-based data.Data set with repeatedly assessed self-report and cardiac ecological momentary assessment measures over one week.Data set with repeatedly assessed self-report and physiological variables collected during the Trier Social Stress Test.For the study protocol see:Heekerens, J. B., Gross, J. J., Kreibig, S. D., Wingenfeld, K., & Roepke, S. (2023). The temporal dynamics of dissociation: protocol for an ecological momentary assessment and laboratory study in a transdiagnostic sample. BMC Psychology, 11(1), 178. https://doi.org/10.1186/s40359-023-01209-zFor the laboratory protocol see:Heekerens, J. B. (2023). Inducing psychosocial stress in the laboratory: A study protocol for the Trier Social Stress Test (TSST) v3. https://doi.org/10.17504/protocols.io.e6nvwj85zlmk/v3

This folder contains the masks generated after segmentation of single nuclei or fibers in CODEX images.The folder also contains the AnnData file with analyses of nuclei-type and cell-type identity and neighborhoods, as well as all the markers imaged in the CODEX panel.

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This folder contains the masks generated after segmentation of single nuclei or fibers in CODEX images.The folder also contains the AnnData file with analyses of nuclei-type and cell-type identity and neighborhoods, as well as all the markers imaged in the CODEX panel.

Additional metadata and analysis products for HDMA (Liu*, Jessa*, Kim*, Ng* et al, bioRxiv 2025). Includes per cell metadata, caCRE BED annotation, ChromBPNet peak and nonpeak training regions, motif lexicon as TF-MoDISco and MEME formats, motif instances per cell type, and ABC loops. A detailed description of the main data types deposited on Zenodo can be found here.

Dataset to accompany the paper "Evaluating the Effectivness of Multi-Sector Demand Response". This dataset contains the raw network files, processed data, and result figures for the California and Capacity Constrained California systems.

A single Excel file 'SupplyChainGHGEmissionFactorsv1.4.0.xlsx' contains sheets:'CO2e' for the Factors in kg CO2e (carbon dioxide equivalent) per USD 2024 output of the commodity, where the AR6 GWP-100 values have been applied to each gas and summed'byGHG' for the Factors in kg gas per USD 2024 output of the commodity (before GWP is applied). The attached PDF 'About the Supply Chain Greenhouse Gas Emission Factors v1.4.0.pdf' provided a full description of the background, methods, a quantitative summary of the dataset, use of the dataset, a comparison to the previous version, and an analysis of the correlation between price change and Factor change. 'SI_RelativeChangefromv1.3.0tov1.4.0inSEFs.xlsx' is supporting information showing relative change in the SEFs with Margins from the previous version to the current version, also with sheets for CO2e changes and by GHG.'USEEIOv2.6.0-phoebe-23.rds' is the USEEIO model in native R format used to generate the Factors.

The 2019-2023 National-Level Greenhouse Gas Emission Totals by Industry dataset contains ~23 thousand records of GHGs directly emitted in the U.S. by economic sectors and households across two Excel files, one for each level of industry detail. The aggregate level provides data for all U.S. industries and households in 118 categories. The detail provides data for all U.S. industries and households in 527 categories. Sectors are defined using the codes from the 2017 version of the North American Industry Classification System (NAICS). The 2025 version of the U.S. GHG Inventory (GHGI) is the primary source for the emissions quantities in the dataset (U.S. EPA 2025). The 17 unique GHG chemicals or groups are reported using the nomenclature from the Federal LCA Commons Elementary Flow List v1.3.2 (Edelen et al. 2019), where all individual GHGs present in the GHGI are reported. The dataset uses the Flow-By-Sector collapsed format (Birney et al. 2022). Emissions of each GHG are reported in kilograms (kg) per year, except for groups of hydrofluorocarbons (HFCs) and perfluorohydrocarbons (PFCs) where the original data were only available in CO2-equivalents (CO2e). Even more detailed versions of the Excel datasets are available in Apache parquet files, where each record provides a total for a given GHG, sector, year, location, and data source and provides all the fields in the Flow-By-Sector collapsed format. The Excel versions of the data provide records aggregated at the level of GHG, sector, and year. The Excel versions also provide supporting definitions of sector codes. The geographic scope of the emission totals include the U.S. States and District of Columbia. The code used to generate the datasets is available in v2.1.0 of the FLOWSA tool.These data are an update to 2012-2022 National-Level Greenhouse Gas Emission Totals by Industry (Young and Ingwersen 2024). Data for 2023 are added and data for 2019-2022 are updated. Data for years prior to 2019 are not included in this release. Please see the related publication for more background information and methodological detail (Young, Birney, and Ingwersen 2024), along with the release notes below.Also included in this release are the 378 GHGI Flow-By-Activity (FBA) datasets used to generate the GHG sector attribution models. These FBA are the GHGI data imported and formatted in v2.1.0 of FLOWSA. Additional FBAs used for allocation in the sector attribution models can be found on USEPA's Data Commmons.

Data archive for Tier 1 of the ForceSMIP project, which is described in "Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP)" by Wills et al. Please cite that paper for any usage of this data (preprint citation information below; cite final published version once available). Wills, R.C.J., C. Deser, K.A. McKinnon, A. Phillips, S. Po-Chedley, S. Sippel, A.L. Merrifield, C. Bône, C. Bonfils, G. Camps-Valls, S. Cropper, C. Connolly, S. Duan, H. Durand, A. Feigin, M.A. Fernandez, G. Gastineau, A. Gavrilov, E. Gordon, M. Günther, M. Höver, S. Kravtsov, Y.-N. Kuo, J. Lien, G.D. Madakumbra, N. Mankovich, M. Newman, J. Rader, J.-R. Shi, S.-I. Shin, G. Varando: Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP), ESS Open Archive, https://doi.org/10.22541/essoar.175003371.14843115/v1.Types of data included here are:Evaluation-Tier1: Raw data for 10 evaluation members, including reanalysis/observations (member "1I")ensmeans-Tier1: The "true forced response", from the corresponding large ensemble mean, for the 9 evaluation members that are from models (all except "1I")ForceSMIP-estimates-Tier1: ForcesSMIP method estimates of the forced response in each evaluation memberEach of these types of data is provided at monthly temporal resolution over 1950-2022, for each of 8 variables: tos (sea-surface temperature), tas (surface air temperature), pr (precipitation), psl (sea-level pressure), monmaxtasmax (monthly maximum daily maximum temperature), monmintasmin (monthly minimum daily minimum temperature), monmaxpr (monthly maximum daily precipitation), and zmta (zonal-mean atmospheric temperature). Annual maximums of monmaxtasmax and monmaxpr give the annual maximums TXx and Rx1day following standard notational conventions in the study of extreme events (Zhang et al. 2011, https://doi.org/10.1002/wcc.147). Similarly, the annual minimum of monmintasmin gives TNn.For further details about the dataset and how it was generated, see Wills et al., "Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP)".Correspondence: Robert Jnglin Wills ([email protected])