This training dataset was calculated using the mechanistic modeling approach. See “Big data training data for artificial intelligence-based Li-ion diagnosis and prognosis“ (Journal of Power Sources, Volume 479, 15 December 2020, 228806) and "Analysis of Synthetic Voltage vs. Capacity Datasets for Big Data Diagnosis and Prognosis" (Energies, under review) for more details

The V vs. Q dataset was compiled with a resolution of 0.01 for the triplets and C/25 charges. This accounts for more than 5,000 different paths. Each path was simulated with at most 0.85% increases for each The training dataset, therefore, contains more than 700,000 unique voltage vs. capacity curves.

4 Variables are included, see read me file for details and example how to use. Cell info: Contains information on the setup of the mechanistic model Qnorm: normalize capacity scale for all voltage curves pathinfo: index for simulated conditions for all voltage curves volt: voltage data. Each column corresponds to the voltage simulated under the conditions of the corresponding line in pathinfo.

This dataset contains monthly-averaged sea-ice velocity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.

This file is an example data set from the Central Valley of California from a drought study corresponding to “recent non-drought conditions” (Scenario 1 in Petrie et al., in review). In 2014, following an 8-year period with 7 below-normal to critically-dry water years, the bioenergetic model TRUEMET was used to assess the impacts of drought on wintering waterfowl habitat and bioenergetics in the Central Valley of California. The goal of the study was to assess whether available foraging habitats could provide enough food to support waterfowl populations (ducks and geese) under a variety of climate and population level scenarios. This information could then be used by managers to adapt their waterfowl habitat management plans to drought conditions. The study area spanned the Central Valley and included the Sacramento Valley in the north, the San Joaquin Valley in the south, and Suisun Marsh and Sacramento-San Joaquin River Delta (Delta) east of San Francisco Bay. The data set consists of two foraging guilds (ducks and geese/swans) and five forage types: harvested corn, rice (flooded), rice (unflooded), wetland invertebrates and wetland moist soil seeds. For more background on the data set, see Petrie et al. in review.

A Dataset, containing runtimes of the adf-bdd solver and comparisons with other ADF-solvers.

statistics_overview.csv contains the raw test-data

xxx-{stb,com,grd}.csv contains further raw test-data

ststs.ipynb is a python notebook to generate the evaluation of the raw data

stats.html is a portable and viewable version of the notebook

Used instances are taken from the benchmark set available at https://www.dbai.tuwien.ac.at/proj/adf/yadf/

Note: more details on test environment can be found in the jupyter notebook and the html-file.

The Gridded Population of the World, Version 4 (GPWv4): Population Count, Revision 11 consists of estimates of human population (number of persons per pixel), consistent with national censuses and population registers, for the years 2000, 2005, 2010, 2015, and 2020. A proportional allocation gridding algorithm, utilizing approximately 13.5 million national and sub-national administrative Units, was used to assign population counts to 30 arc-second grid cells. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research commUnities, the 30 arc-second data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions.

Coups d'Ètat are important events in the life of a country. They constitute an important subset of irregular transfers of political power that can have significant and enduring consequences for national well-being. There are only a limited number of datasets available to study these events (Powell and Thyne 2011, Marshall and Marshall 2019). Seeking to facilitate research on post-WWII coups by compiling a more comprehensive list and categorization of these events, the Cline Center for Advanced Social Research (previously the Cline Center for Democracy) initiated the Coup d’État Project as part of its Societal Infrastructures and Development (SID) project. More specifically, this dataset identifies the outcomes of coup events (i.e., realized, unrealized, or conspiracy) the type of actor(s) who initiated the coup (i.e., military, rebels, etc.), as well as the fate of the deposed leader. Version 2.1.3 adds 19 additional coup events to the data set, corrects the date of a coup in Tunisia, and reclassifies an attempted coup in Brazil in December 2022 to a conspiracy. Version 2.1.2 added 6 additional coup events that occurred in 2022 and updated the coding of an attempted coup event in Kazakhstan in January 2022. Version 2.1.1 corrected a mistake in version 2.1.0, where the designation of “dissident coup” had been dropped in error for coup_id: 00201062021. Version 2.1.1 fixed this omission by marking the case as both a dissident coup and an auto-coup. Version 2.1.0 added 36 cases to the data set and removed two cases from the v2.0.0 data. This update also added actor coding for 46 coup events and added executive outcomes to 18 events from version 2.0.0. A few other changes were made to correct inconsistencies in the coup ID variable and the date of the event. Version 2.0.0 improved several aspects of the previous version (v1.0.0) and incorporated additional source material to include: • Reconciling missing event data • Removing events with irreconcilable event dates • Removing events with insufficient sourcing (each event needs at least two sources) • Removing events that were inaccurately coded as coup events • Removing variables that fell below the threshold of inter-coder reliability required by the project • Removing the spreadsheet ‘CoupInventory.xls’ because of inadequate attribution and citations in the event summaries • Extending the period covered from 1945-2005 to 1945-2019 • Adding events from Powell and Thyne’s Coup Data (Powell and Thyne, 2011)
Items in this Dataset 1. Cline Center Coup d'État Codebook v.2.1.3 Codebook.pdf - This 15-page document describes the Cline Center Coup d’État Project dataset. The first section of this codebook provides a summary of the different versions of the data. The second section provides a succinct definition of a coup d’état used by the Coup d'État Project and an overview of the categories used to differentiate the wide array of events that meet the project's definition. It also defines coup outcomes. The third section describes the methodology used to produce the data. Revised February 2024 2. Coup Data v2.1.3.csv - This CSV (Comma Separated Values) file contains all of the coup event data from the Cline Center Coup d’État Project. It contains 29 variables and 1000 observations. Revised February 2024 3. Source Document v2.1.3.pdf - This 325-page document provides the sources used for each of the coup events identified in this dataset. Please use the value in the coup_id variable to identify the sources used to identify that particular event. Revised February 2024 4. README.md - This file contains useful information for the user about the dataset. It is a text file written in markdown language. Revised February 2024
Citation Guidelines 1. To cite the codebook (or any other documentation associated with the Cline Center Coup d’État Project Dataset) please use the following citation: Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Scott Althaus. 2024. “Cline Center Coup d’État Project Dataset Codebook”. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.1.3. February 27. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V7 2. To cite data from the Cline Center Coup d’État Project Dataset please use the following citation (filling in the correct date of access): Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Emilio Soto. 2024. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.1.3. February 27. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V7

This data set, as part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, combines interferometric phases from multiple satellite interferometric synthetic-aperture radar systems to derive the first comprehensive phase-based map of Antarctic ice velocity. The precision in ice speed and flow direction over 80% of Antarctica is better than prior mappings based on feature and speckle tracking by a factor of 10. Phase-derived velocity mostly covers the years between 2007 and 2018, while tracking-derived velocity (for regions along the coasts) is mostly found in the years from 2013 to 2017. Additional data acquired between 1996 and 2018 were used as needed to maximize coverage.

See Antarctic Ice Sheet Velocity and Mapping Data for related data.

This dataset includes 518 high vertical resolution (2 sec) soundings from the R/V Mirai collected during the DYNAMO field campaign for 2011. Soundings were typically released every three hours during the two cruises (26 September through 26 October and 29 October through 1 December 2011) and four times daily from 5-10 December 2011. For most of these soundings R/V Mirai was on station near 80.5E and 8.00S Latitude. These data were converted to the EOL Sounding Composite (ESC) format. The station used Vaisala RS92-SGPD radiosondes. The ground station software was Digicora III/MW31/ver3.64.1. This data set is considered preliminary. These data were provided by JAMSTEC and had preliminarly quality control by NCAR/EOL. This L3.1 version of the data set had an additional correction applied for deck heating effects by JAMSTEC.

The Soil and Terrain database for the Upper Tana River Catchment (version 1.1) (SOTER_UT_v1.1) at scale 1:250,000 was compiled to support the Green Water Credits (GWC) programme by creating a primary SOTER dataset for a hydrology assessment of the basin. The Kenya Soil Survey of the Kenya Agriculture Research Institute(KARI-KSS) and ISRIC-World Soil Information compiled the SOTER_UT dataset ... according to the standard SOTER methodology. The dataset includes both data of the original KENSOTER database (1:1M) for the Upper Tana Catchment and, new SOTER units and soil profile data taken from other, existing soil surveys mainly at scale 100,000 and from more detailed studies.

The SOTER database was used for the hydrology assessment of Upper Tana basin using the model Soil and Water Assessment Tool (SWAT)to quantify the impact of land management practices change in the basin's waterbalance.

This data set is part of an ongoing project to consolidate interagency fire perimeter data. The record is complete from the present back to 2020. The incorporation of all available historic data is in progress.The InFORM (Interagency Fire Occurrence Reporting Modules) FODR (Fire Occurrence Data Records) are the official record of fire events. Built on top of IRWIN (Integrated Reporting of Wildland Fire Information), the FODR starts with an IRWIN record and then captures the final incident information upon certification of the record by the appropriate local authority. This service contains all wildland fire incidents from the InFORM FODR incident service that meet the following criteria:Categorized as a Wildfire (WF) or Prescribed Fire (RX) recordIs Valid and not "quarantined" due to potential conflicts with other recordsNo "fall-off" rules are applied to this service.Service is a real time display of data.Warning: Please refrain from repeatedly querying the service using a relative date range. This includes using the “(not) in the last” operators in a Web Map filter and any reference to CURRENT_TIMESTAMP. This type of query puts undue load on the service and may render it temporarily unavailable.Attributes:ABCDMiscA FireCode used by USDA FS to track and compile cost information for emergency initial attack fire suppression expenditures. for A, B, C & D size class fires on FS lands.ADSPermissionStateIndicates the permission hierarchy that is currently being applied when a system utilizes the UpdateIncident operation.CalculatedAcresA measure of acres calculated (i.e., infrared) from a geospatial perimeter of a fire. More specifically, the number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands. The minimum size must be 0.1.ContainmentDateTimeThe date and time a wildfire was declared contained. ControlDateTimeThe date and time a wildfire was declared under control.CreatedBySystemArcGIS Server Username of system that created the IRWIN Incident record.CreatedOnDateTimeDate/time that the Incident record was created.IncidentSizeReported for a fire. The minimum size is 0.1.DiscoveryAcresAn estimate of acres burning upon the discovery of the fire. More specifically when the fire is first reported by the first person that calls in the fire. The estimate should include number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.DispatchCenterIDA unique identifier for a dispatch center responsible for supporting the incident.EstimatedCostToDateThe total estimated cost of the incident to date.FinalAcresReported final acreage of incident.FinalFireReportApprovedByTitleThe title of the person that approved the final fire report for the incident.FinalFireReportApprovedByUnitNWCG Unit ID associated with the individual who approved the final report for the incident.FinalFireReportApprovedDateThe date that the final fire report was approved for the incident.FireBehaviorGeneralA general category describing the manner in which the fire is currently reacting to the influences of fuel, weather, and topography. FireCodeA code used within the interagency wildland fire community to track and compile cost information for emergency fire suppression expenditures for the incident. FireDepartmentIDThe U.S. Fire Administration (USFA) has created a national database of Fire Departments. Most Fire Departments do not have an NWCG Unit ID and so it is the intent of the IRWIN team to create a new field that includes this data element to assist the National Association of State Foresters (NASF) with data collection.FireDiscoveryDateTimeThe date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes.FireMgmtComplexityThe highest management level utilized to manage a wildland fire event. FireOutDateTimeThe date and time when a fire is declared out. FSJobCodeA code use to indicate the Forest Service job accounting code for the incident. This is specific to the Forest Service. Usually displayed as 2 char prefix on FireCode.FSOverrideCodeA code used to indicate the Forest Service override code for the incident. This is specific to the Forest Service. Usually displayed as a 4 char suffix on FireCode. For example, if the FS is assisting DOI, an override of 1502 will be used.GACCA code that identifies one of the wildland fire geographic area coordination center at the point of origin for the incident.A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.IncidentNameThe name assigned to an incident.IncidentShortDescriptionGeneral descriptive location of the incident such as the number of miles from an identifiable town. IncidentTypeCategoryThe Event Category is a sub-group of the Event Kind code and description. The Event Category further breaks down the Event Kind into more specific event categories.IncidentTypeKindA general, high-level code and description of the types of incidents and planned events to which the interagency wildland fire community responds.InitialLatitudeThe latitude location of the initial reported point of origin specified in decimal degrees.InitialLongitudeThe longitude location of the initial reported point of origin specified in decimal degrees.InitialResponseDateTimeThe date/time of the initial response to the incident. More specifically when the IC arrives and performs initial size up. IsFireCauseInvestigatedIndicates if an investigation is underway or was completed to determine the cause of a fire.IsFSAssistedIndicates if the Forest Service provided assistance on an incident outside their jurisdiction.IsReimbursableIndicates the cost of an incident may be another agency’s responsibility.IsTrespassIndicates if the incident is a trespass claim or if a bill will be pursued.LocalIncidentIdentifierA number or code that uniquely identifies an incident for a particular local fire management organization within a particular calendar year.ModifiedBySystemArcGIS Server username of system that last modified the IRWIN Incident record.ModifiedOnDateTimeDate/time that the Incident record was last modified.PercentContainedIndicates the percent of incident area that is no longer active. Reference definition in fire line handbook when developing standard.POOCityThe closest city to the incident point of origin.POOCountyThe County Name identifying the county or equivalent entity at point of origin designated at the time of collection.POODispatchCenterIDA unique identifier for the dispatch center that intersects with the incident point of origin. POOFipsThe code which uniquely identifies counties and county equivalents. The first two digits are the FIPS State code and the last three are the county code within the state.POOJurisdictionalAgencyThe agency having land and resource management responsibility for a incident as provided by federal, state or local law. POOJurisdictionalUnitNWCG Unit Identifier to identify the unit with jurisdiction for the land where the point of origin of a fire falls. POOJurisdictionalUnitParentUnitThe unit ID for the parent entity, such as a BLM State Office or USFS Regional Office, that resides over the Jurisdictional Unit.POOLandownerCategoryMore specific classification of land ownership within land owner kinds identifying the deeded owner at the point of origin at the time of the incident.POOLandownerKindBroad classification of land ownership identifying the deeded owner at the point of origin at the time of the incident.POOProtectingAgencyIndicates the agency that has protection responsibility at the point of origin.POOProtectingUnitNWCG Unit responsible for providing direct incident management and services to a an incident pursuant to its jurisdictional responsibility or as specified by law, contract or agreement. Definition Extension: - Protection can be re-assigned by agreement. - The nature and extent of the incident determines protection (for example Wildfire vs. All Hazard.)POOStateThe State alpha code identifying the state or equivalent entity at point of origin.PredominantFuelGroupThe fuel majority fuel model type that best represents fire behavior in the incident area, grouped into one of seven categories.PredominantFuelModelDescribes the type of fuels found within the majority of the incident area. UniqueFireIdentifierUnique identifier assigned to each wildland fire. yyyy = calendar year, SSUUUU = POO protecting unit identifier (5 or 6 characters), xxxxxx = local incident identifier (6 to 10 characters) FORIDUnique identifier assigned to each incident record in the FODR database.

This dataset contains daily-averaged ocean bottom pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4b (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.

Notice to Data Users: The documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.This data set contains several parameters measured in the Walnut Gulch Experimental Watershed for the Soil Moisture Experiment 2004 (SMEX04).

This post contains the stimuli texts, data, and R code for analysis for this article. Here is the abstract for the article: The relationship between construal and redundancy has not been previously explored empirically. Russian aspect allows speakers to construe situations as either Perfective or Imperfective, but it is not clear to what extent aspect is determined by context and therefore redundant. We investigate the relationship between redundancy and open construal by surveying 500 native Russian speakers who rated the acceptability of both Perfective and Imperfective verb forms in complete extensive authentic contexts. We find that aspect is largely redundant in about 81% of uses, and in about 17% of contexts aspect is relatively open to construal. We contend that anchoring in redundant contexts likely facilitates the independence of construal in contexts with less redundancy. However further research is needed to discover what makes contexts redundant since known cues for aspect are absent in the majority of such contexts. Native speakers are fairly consistent in giving the original aspect high ratings, but less consistent in rating the non-original aspect, indicating potential problems in testing the reactions of speakers to non-authentic data.

This dataset contains the International Surface Pressure Databank version 4.7 (ISPDv4), the world's largest collection of pressure observations. It has been gathered through international cooperation with data recovery facilitated by the ACRE Initiative and the other contributing organizations and assembled under the auspices of the GCOS Working Group on Surface Pressure and the WCRP/GCOS Working Group on Observational Data Sets for Reanalysis by NOAA Earth System Research Laboratory (ESRL), NOAA's National Climatic Data Center (NCDC), and the University of Colorado's Cooperative Institute for Research in Environmental Sciences (CIRES). The ISPDv4 consists of three components: station, marine, and tropical cyclone best track pressure observations. The station component is a blend of many national and international collections. In addition to the pressure observations and metadata, ISPDv4 contains feedback from the 20th Century Reanalysis version 3, including quality control information and uncertainty information. Support for the International Surface Pressure Databank is provided by the U.S. Department of Energy, Office of Science Biological and Environmental Research (BER), and by the National Oceanic and Atmospheric Administration Climate Program Office. The International Surface Pressure Databank version 4.7 and 20th Century Reanalysis version 3 used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231.

Comprehensive dataset of 4 Restaurant or caves in Kırıkkale, Turkey as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

The continuous growth of the global human population results in increased use and change of landscapes, with infrastructures like transportation or energy facilities, being a particular risk to large carnivores. Environmental Impact Assessments were established to identify the probable environmental consequences of any new proposed project, find ways to reduce impacts, and provide evidence to inform decision making and mitigation. Portugal has a wolf population of around 300 individuals, designated as an endangered species with full legal protection. They occupy the northern mountainous areas of the country which has also been the focus of new human infrastructures over the last 20 years. Consequently, dozens of wolf monitoring programs have been established to evaluate wolf population status, to identify impacts, and to inform appropriate mitigation or compensation measures. We reviewed Portuguese wolf monitoring programs to answer four key questions: do wolf programs examine adequate biological parameters to meet monitoring objectives? is the study design suitable for measuring impacts? are data collection methods and effort sufficient for the stated inference objectives? and do statistical analyses of the data lead to robust conclusions? Overall, we found a mismatch between the stated aims of wolf monitoring and the results reported, and often neither aligns with the existing national wolf monitoring guidelines. Despite the vast effort expended and the diversity of methods used, data analysis makes almost exclusive use of relative indices or summary statistics, with little consideration of the potential biases that arise through the (imperfect) observational process. This makes comparisons of impacts across space and time difficult and is therefore unlikely to contribute to a general understanding of wolf responses to infrastructure-related disturbance. We recommend the development of standardized monitoring protocols and advocate for the use of statistical methods that account for imperfect detection to guarantee accuracy, reproducibility, and efficacy of the programs. Methods We reviewed all major wolf monitoring programs developed for environmental impact assessments in Portugal since 2002 (Table S1, Supplementary material). Given that the focus here is on the adequacy of targeted wolf monitoring for delivering conclusions about the effects of infrastructure development, we reviewed only monitoring programs that were specifically designed for wolves and not those concerned with general mammalian assessment. The starting point was a compilation from the 2019-2021 National Wolf Census (Pimenta et al., 2023), where every wolf monitoring program that occurred between 2014 and 2019 in Portugal was identified. The list was completed with projects that started before 2014 or after 2019 based on personal knowledge, inquires to principal scientific teams, governmental agencies, and EIA consultants. Depending on duration, wolf monitoring programs can produce several, usually annual, reports that are not peer-reviewed and do not appear on standard search engines (e.g., Web of Science or Google Schoolar) but are publicly available from the Portuguese Environmental Agency (APA – www.apambiente.pt). We conducted an online search on APA´s search engine (https://siaia.apambiente.pt/) and identified a total of 30 projects. For each of these projects, we were interested in the first and the last report to identify any methodological changes. If the last report was not present, we reviewed the most recent one. If no report was present, we requested it from the team responsible. Our investigation centred on characterizing and quantifying four components of wolf monitoring programs that are interlinked and that should be ideally determined by the initial objectives: (1) biological parameters, i.e., what wolf parameters were studied to assess impacts; (2) study design, i.e., what sampling schemes were followed to collect and analyse data; (3) data collection, i.e., which sampling methodology and how much effort was used to collect data; and (4) data analysis, i.e., how data were analysed to estimate relevant parameters and assess impact. Biological parameters were identified and classified under two categories: occurrence and demography, which broadly correspond to the necessary inputs to assess impacts like exclusion effect and changes in reproductive patterns. Occurrence-related parameters refer to variables used to measure the presence or absence of wolves, whereas demographic parameters refer to variables that intend to measure population-level effects such as abundance, density, survival, or reproduction. We also recorded whether any effort was made to quantify prey population distribution or abundance as recommended in the guidelines. For study design, we reviewed the sampling design of the project, with specific focus on the spatial and temporal aspect of the study such as total area surveyed, the definition of a sampling site within this region (i.e., resolution), the duration of the study and the number of sampling seasons. The goal here was to determine whether the sampling scheme used was appropriate for assessing infrastructure impacts on wolf distribution or demography, depending on what the focus was. For data collection, we identified the main data collection methodologies used and the corresponding sampling effort. By far the most frequent method used is sign surveys, and specifically scat surveys, and for these studies we recorded whether genetic identification of species or individuals based on faecal DNA was attempted. We compare how sampling effort varies by the various inference objectives and, as above, assess which, if any, project or data collection approach is most likely to produce evidence of impact. We divided the Analysis component into two groups: single-year and multi-year analyses. For single-year analysis we identified how monitoring projects used data to make inferences about the state biological parameters of interest and discuss the associated strengths and weaknesses. For multi-year analyses, we recorded how differences or trends were quantified and associated with infrastructure impacts, commenting on the statistical robustness of the analyses used across the projects.

Laos Exports of machinery for working rubber or plastics to Japan was US$13.78 Thousand during 2023, according to the United Nations COMTRADE database on international trade. Laos Exports of machinery for working rubber or plastics to Japan - data, historical chart and statistics - was last updated on June of 2025.

This dataset provides daily-averaged Gent-McWilliams ocean bolus velocity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.

The Alaska Geochemical Database Version 4.0 (AGDB4) contains geochemical data compilations in which each geologic material sample has one best value determination for each analyzed species, greatly improving efficiency of use. The relational database includes historical geochemical data archived in the USGS National Geochemical Database (NGDB), the Atomic Energy Commission National Uranium Resource Evaluation (NURE) Hydrogeochemical and Stream Sediment Reconnaissance databases, and the Alaska Division of Geological and Geophysical Surveys (DGGS) Geochemistry database. Data from the U.S. Bureau of Mines and the U.S. Bureau of Land Management are included as well. The data tables describe historical and new quantitative and qualitative geochemical analyses. The analytical results were determined by 120 laboratory and field analytical methods performed on 416,333 rock, sediment, soil, mineral, heavy-mineral concentrate, and oxalic acid leachate samples. The samples were collected as ...

This dataset contains monthly-averaged dynamic sea surface height interpolated to a regular 0.5-degree grid from the ECCO Version 4b revision 4 (V4r4b) ocean and sea-ice state estimate. V4r4b is an errata for ECCO Version 4, Release 4 (V4r4). Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4b is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4b include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4b covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.