The dataset is a result of associating air pollution and climate data subsets to particular health events. The subsets are obtained by aggregating land-based stations observation data relative to each event within county KERRY and the DAY time unit by using the MEAN function. The observation data is related to 2 DAYS prior to each event for a period of 3 DAYS.

This paper explores a unique dataset of all the SET ratings provided by students of one university in Poland at the end of the winter semester of the 2020/2021 academic year. The SET questionnaire used by this university is presented in Appendix 1. The dataset is unique for several reasons. It covers all SET surveys filled by students in all fields and levels of study offered by the university. In the period analysed, the university was entirely in the online regime amid the Covid-19 pandemic. While the expected learning outcomes formally have not been changed, the online mode of study could have affected the grading policy and could have implications for some of the studied SET biases. This Covid-19 effect is captured by econometric models and discussed in the paper. The average SET scores were matched with the characteristics of the teacher for degree, seniority, gender, and SET scores in the past six semesters; the course characteristics for time of day, day of the week, course type, course breadth, class duration, and class size; the attributes of the SET survey responses as the percentage of students providing SET feedback; and the grades of the course for the mean, standard deviation, and percentage failed. Data on course grades are also available for the previous six semesters. This rich dataset allows many of the biases reported in the literature to be tested for and new hypotheses to be formulated, as presented in the introduction section. The unit of observation or the single row in the data set is identified by three parameters: teacher unique id (j), course unique id (k) and the question number in the SET questionnaire (n ϵ {1, 2, 3, 4, 5, 6, 7, 8, 9} ). It means that for each pair (j,k), we have nine rows, one for each SET survey question, or sometimes less when students did not answer one of the SET questions at all. For example, the dependent variable SET_score_avg(j,k,n) for the triplet (j=Calculus, k=John Smith, n=2) is calculated as the average of all Likert-scale answers to question nr 2 in the SET survey distributed to all students that took the Calculus course taught by John Smith. The data set has 8,015 such observations or rows. The full list of variables or columns in the data set included in the analysis is presented in the attached filesection. Their description refers to the triplet (teacher id = j, course id = k, question number = n). When the last value of the triplet (n) is dropped, it means that the variable takes the same values for all n ϵ {1, 2, 3, 4, 5, 6, 7, 8, 9}.Two attachments:- word file with variables description- Rdata file with the data set (for R language).Appendix 1. Appendix 1. The SET questionnaire was used for this paper. Evaluation survey of the teaching staff of [university name] Please, complete the following evaluation form, which aims to assess the lecturer’s performance. Only one answer should be indicated for each question. The answers are coded in the following way: 5- I strongly agree; 4- I agree; 3- Neutral; 2- I don’t agree; 1- I strongly don’t agree. Questions 1 2 3 4 5 I learnt a lot during the course. ○ ○ ○ ○ ○ I think that the knowledge acquired during the course is very useful. ○ ○ ○ ○ ○ The professor used activities to make the class more engaging. ○ ○ ○ ○ ○ If it was possible, I would enroll for the course conducted by this lecturer again. ○ ○ ○ ○ ○ The classes started on time. ○ ○ ○ ○ ○ The lecturer always used time efficiently. ○ ○ ○ ○ ○ The lecturer delivered the class content in an understandable and efficient way. ○ ○ ○ ○ ○ The lecturer was available when we had doubts. ○ ○ ○ ○ ○ The lecturer treated all students equally regardless of their race, background and ethnicity. ○ ○

The MILINDA dataset encompasses all military and non-military peace operations between 1947 and 2016 explicitly designated as peace operations. Peace operations are the expeditionary use of uniformed personnel with or without a UN mandate, but with an explicit mandate to assist in the prevention of armed conflict by supporting a peace process. It includes not only UN missions but also a substantial number of missions conducted by ad hoc coalitions of states or individual state not included in the UN or SIPRI’s database. The MILINDA dataset contains 293 observations altogether. The unit of analysis is the mission. If a mission changes its category—for example, from an Observer to a Peacekeeping operation—we coded this as a new observation. MILINDA codes four types of interventions, denoting the distribution of responsibilities among the various actors, which we defined in line with the international legal literature: Peace Enforcement, Peacekeeping, Observer, and Other.

This part of DS 781 presents video observations from cruise Z107SC for the Santa Barbara Channel region and beyond in southern California. The vector data file is included in "z107sc_video_observations.zip," which is accessible from http://pubs.usgs.gov/ds/781/video_observations/data_catalog_video_observations.html. Some of the video observations from cruise Z107SC are published in Scientific Investigations Map 3225, "California State Waters Map Series--Hueneme Canyon and Vicinity, California" (see sheet 6). In addition, some of the video observations will be published in three future California State Waters Map Series SIMs of the region (namely, the Mugu Canyon and Vicinity, Offshore of Coal Oil Point, and Offshore of Gaviota map areas). Between 2006 and 2007, the seafloor in the Mugu Canyon and Vicinity, Hueneme Canyon and Vicinity, Offshore of Coal Oil Point, and Offshore of Gaviota map areas in southern California was mapped by California State University, Monterey Bay, Seafloor Mapping Lab (CSUMB) and by the U.S. Geological Survey (USGS), using both multibeam echosounders and bathymetric sidescan sonar units (for example, see sheets 1, 2, and 3, SIM 3225, for details). These mapping missions combined to collect bathymetry and acoustic-backscatter data from about the 10-m isobath to out beyond the 3-nautical-mile limit of California's State Waters. To validate the interpretations of sonar data in order to turn it into geologically and biologically useful information, the USGS ground-truth surveyed the data by towing camera sleds over specific locations throughout the region. During the 2008 ground-truth cruise, the camera sled housed two video cameras (one forward looking and the other vertical looking), a high-definition video camera, and an 8-megapixel digital still camera. The video was fed in real time to the research vessel, where USGS and NOAA scientists recorded both the geologic and biologic character of the seafloor into programmable keypads once every minute. In addition to recording the seafloor characteristics, a digital still photograph was captured once every 30 seconds. This ArcGIS shape file includes the position of the camera, the time each observation was started, and the visual observations of geologic and biologic habitat.

This data set contains meteorological element observation data of heihe remote sensing station in the middle reaches of heihe hydrometeorological observation network from January 1, 2016 to December 31, 2016.The station is located in the east of dangzhai town, zhangye city, gansu province.The longitude and latitude of the observation point are 100.4756e, 38.8270n and 1560m above sea level.The air temperature and humidity sensor is located at 1.5m, facing due north.The barometer is in the waterproof box;The tilting bucket rain gauge is installed at 0.7 m;The wind speed and direction sensor is located at 10m, facing due north;The installation height of the four-component radiometer is 1.5m, facing due south;The installation height of the two infrared thermometers is 1.5m, facing due south and the probe facing vertically downward.The soil temperature probe is buried at 0cm on the surface and 2cm, 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm underground.The soil water probe was buried at 2cm, 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm.Average soil temperature probes were buried in 2cm and 4cm;The soil heat flow plate (3 pieces) is buried 6cm underground.Two photosynthetically active radiometers were set up 1.5m above the canopy (one probe vertically upwards and one probe vertically downwards), facing due south. Observation projects are: air temperature and humidity (Ta_1. 5 m, RH_1. 5 m) (unit: c, percentage), pressure (Press) (unit: hundred mpa), precipitation (Rain) (unit: mm), wind speed (WS_10m) (unit: m/s), wind (WD_10m) (unit: degrees), the radiation of four component (DR, UR, DLR_Cor, ULR_Cor, Rn) (unit: watts per square meter), the surface radiation temperature (IRT_1, IRT_2) (unit:C), soil heat flux (Gs_1, Gs_2, Gs_3) (in watts/m2), soil temperature (Ts_0cm, Ts_2cm, Ts_4cm, Ts_10cm, Ts_20cm, Ts_40cm, Ts_80cm, Ts_120cm, Ts_160cm) (in:C), soil moisture (Ms_0cm, Ms_2cm, Ms_4cm, Ms_10cm, Ms_20cm, Ms_40cm, Ms_80cm, Ms_120cm, Ms_160cm) (unit: %), upward and downward photosynthetically active radiation (PAR_U_up, PAR_U_down) (unit: micromole/sq.s), mean soil temperature (TCAV) (unit: Celsius). Processing and quality control of observed data :(1) ensure 144 pieces of data every day (every 10min), and mark by -6999 in case of data missing;2016.1.01-1.29 due to collector problems, many observation elements have more error values;(2) excluding the time with duplicate records;(3) data that obviously exceeds the physical significance or the range of the instrument is deleted;(4) the part marked with red letter in the data is the data in question;(5) date and time have the same format, and date and time are in the same column.For example, the time is: 2016-6-10-10:30;(6) the naming rule is: AWS+ site name. For information of hydrometeorological network or station, please refer to Liu et al. (2018), and for observation data processing, please refer to Liu et al. (2011).

Multi-scale networked observation is the basic means to obtain regional ecological information, and it is the source of data for comprehensively and deeply understanding the dynamics of ecosystems and assessing the interrelationships between ecosystems, global changes and human activities. The Chinese Ecosystem Research Network (CERN), based on the field observation stations of important ecosystem types in China, adopts unified protocols to carry out long-term positional observation and research on typical ecosystems such as forests, grasslands, deserts and marshes in China, so as to provide scientific data support for the construction of the national ecological environment. This dataset covers the background, plant community species richness, biomass and habitat information of 84 biological long-term observation plots from 10 forest stations, 6 desert stations, 2 grassland stations and 1 marsh stations of CERN from 1998 to 2010. The dataset has gone through a strict three-tier audit and quality control process, and constructed a biological and habitat dataset with the annual observation data of the biological long-term observation samples as the basic unit. The dataset can provide data support for the distribution of biological resources, the relationship between biological and environmental changes and human activities.

Wildlife that share habitats with humans with limited options for spatial avoidance must either tolerate frequent human encounters or concentrate their activity on those periods with the least risk of encountering people. Based on 5,259 camera trap images of adult wolves from eight territories, we analyzed the extent to which diel activity patterns in a highly cultivated landscape with extensive public access (Denmark) could be explained by diel variation in darkness, human activity, and prey (deer) activity. A resource selection function that contrasted every camera observation (use) with 24 alternative hourly observations from the same day (availability), revealed that diel activity correlated with all three factors simultaneously with human activity having the strongest effect (negative), followed by darkness (positive) and deer activity (positive). A model incorporating these three effects had lower parsimony and classified use and availability observations just as well as a ‘circadian’ model that smoothed the use-availability ratio as a function of time of the day. Most of the selection for darkness was explained by variation in human activity, supporting the notion that nocturnality (proportion of observations registered at night vs. day at the equinox) is a proxy for temporal human avoidance. Contrary to our expectations, wolves were no more nocturnal in territories with unrestricted public access than in territories where public access was restricted to roads, possibly because wolves in all territories had few possibilities to walk more than a few hundred meters without crossing roads. Overall, Danish wolf packs were 6.5 (95% CI: 4.6-9.6) times more active at night than at daylight, which makes them amongst the most nocturnally active wolves reported so far. These results confirm the prediction that wolves in habitats with limited options for spatial human avoidance, invest more in temporal avoidance. Methods Population monitoring and data collection Since 2017, the Natural History Museum Aarhus and Aarhus University have monitored all wolves in Denmark for the Danish Environmental Protection Agency. The occurrence and turnover of individuals are registered from genetic markers obtained from scat, hair, saliva, or urine samples collected by systematic patrolling of forest roads and by snow tracking (active monitoring) as well as saliva samples from livestock kills obtained by the Danish Nature Agency. A territory was defined as the area patrolled by a single wolf, pair, or pack for a minimum of six months. The core areas and approximate territory extensions were estimated from the distribution of wolf signs (scats, tracks, kills, photos, etc.) within the landscape. With permission from the landowners, we placed wildlife cameras in places known (from the appearance of footprints, scats, or other signs) or suspected (leading lines in the landscape which from experience are known to be used by wolves when commuting, e.g. forest roads) to be used by the wolves within the territories. At locations with public access, visitors were informed about the presence of the cameras through signs containing project information and our contact details. We used cameras with fast trigger times able to record fast-moving species, that recorded videos and/or multiple pictures. Cameras were usually visited every two to six weeks, checking battery levels and changing memory cards. Where possible, the wolves on the images were identified to age defined as pup (born same calendar year) or adult (not a pup, hence all grown-up wolves observed January-June were coded as adults) and coded in the database. If multiple wolves on the same photo or video sequence were identified as different ages or individuals, they were registered as different records in the database. Prior to the analyses, such doublets or triplets were removed, so only one unique camera observation entered the analysis as an observation unit. As the cameras were placed to maximize the number of wolf observations, sampling effort was concentrated in the central parts of the territories where wolf sign concentrations were highest. Cameras aimed at recording wolves were usually placed along trails and forest roads used by wolves when traversing their territories and at places with a high density of scats and footprints, that indicated frequent use by wolves at a given time. In a subset of the territories we also had cameras placed in the terrain, optimized to register all large and medium-sized mammal species. For wolf population monitoring purposes, observations from both types of surveys were entered into the Danish National Database of Wolf Observations. The effort expended in terms of camera days was not registered in this database. Observations of general wildlife were logged in a separate database, which also included information on the effort expended in terms of the number of camera days (26,210 in total). Due to resource constraints, this database only contained a subset of the total number of camera observations available in the raw data. The wolf data used for this analysis were therefore drawn from the first database. The number of different camera locations, resulting in wolf observations varied from 26 to 198 per territory (median: 51) and the total area covered (100% minimum convex polygon) by cameras delivering wolf data for the analysis, ranging from 6.5 to 79.3 (median: 21.3) km2 per territory. Selection of observations for analyses We selected wolf camera trap data separated by a minimum of 5 minutes from eight independent territories from six areas (one territorial area was occupied by three different constellations of individuals during different time periods. This selection resulted in 5,259 camera observations of adults, 1,814 observations only showing pups (representing five litters from four territories), and 158 observations where the age could not be determined (excluded from the analyses). Of the 5,259 observations of adult wolves, 3,280 (62%) originated from cameras for monitoring wolves, 1,257 (24%) from cameras for monitoring general wildlife, and 722 (14%) from cameras where the initial purpose had not been recorded. As any dependence between observations within territories was accounted for in the statistical analyses by stating territory as random effect (see below), we decided to use the full data set rather than a reduced data set based on observations separated by 30 minutes, as often recommended to avoid serial dependence of observations. Under all circumstances, increasing the minimum sampling interval from 5 to 30 minutes only reduced the data set by 250 observations, and did not change the outcome of any of the statistical analyses. Digitized data on wildlife and human activity was available from three of the six territory areas (five of eight territories. As ungulates, especially cervids (Cervidae, ‘deer’ hereafter) constitute the main and most selected prey type of wolves in Central Europe, we used 11,315 camera observations of deer (species composition: red deer Cervus elaphus: 60%, roe deer Capreolus capreolus: 31%, fallow deer Dama dama: 4%, unidentified deer species: 5%) to represent diel activity of prey. The 15,017 observations of humans were divided between 48% pedestrians, 16% bicyclists, 31% motorized vehicles, and 5% horse riders. Seasonal definition To account for seasonal variation, we divided the year into quartiles: November-January (mean day length at 56°N: 8.47 hours; range: 7.92-9.68 hours), February-April (11.97; 9.22-14.77), May-July (16.01; 14.83-16.55) and August-October (12.57; 9.73-15.32). These not only contrasted the two three-month periods with the shortest and longest daylengths, but also provided a good division of the ecological and reproductive annual cycle for wolf packs that have young offspring in May-July, mobile offspring (frequenting rendezvous sites) in August-October, increasingly independent offspring through November-January, and a pre-parturition period from February-April, when last year’s offspring have attained full independence. Statistical analysis of diel activity patterns of wolves, deer, and humans For each three-month period, we quantified the general variation in the diel activity of juvenile and adult wolves (W hereafter), deer (D), and humans (H), by modelling the relative frequency of observations per one-hour-interval from midnight to midnight (0: 00:00-00:59, 1: 01:00-01:59, etc.). We used the R package ‘mgcv’ v. 1.8 to fit generalized additive models (GAM) with beta distribution and logit-link, an adaptive cyclical cubic smoothing spline for time, and territory ID as random effect. Models were visually validated by plotting standardized model residuals against fitted values. As data for human and deer activity was not available for all areas, extrapolation was necessary. As the season-specific diel activity curves for humans were highly correlated between the different study areas (Supporting information), we produced one season-specific diel activity function based on all data pooled. Among the deer, diel activity correlated less between fenced and unfenced areas. As we know from GPS-data that red deer in fenced areas move shorter hourly distances around dusk and dawn than red deer in unfenced nature areas (P. Sunde and R.M. Mortensen, unpublished), we created one activity distribution for deer based on data from all three areas (“Deer-Total”, abbreviated to “DT”), and one differentiated between fenced and unfenced areas (“Deer-Local”, abbreviated to “DL”). To quantify the extent to which diel activity levels of adult wolves, deer, and humans were associated with light conditions and correlated internally throughout the year, we created a correlation matrix comprised of 24 * 365 = 8,760 hourly time observations, covering the entire year (1 January-31 December). For each hourly time observation, we assigned light conditions

The Joint Effort for Data assimilation Integration (JEDI) software package is a unified, innovative data assimilation system for Earth System Prediction. Developed and distributed by UCP's Joint Center for Satellite Data Assimilation (JCSDA), JEDI is versatile and sophisticated enough for a variety of applications, from operational weather forecasting and atmospheric research on High Performance Computing (HPC) platforms to learning the fundamentals of data assimilation by running idealized toy models on your laptop. This data set provides example observation files, model backgrounds, and other input files needed to run a variety of JEDI applications, including the comprehensive suite of unit tests and example activities that are described in online tutorials. This data set accompanies JEDI-SkyLab, version 2.0.0, released in October 2022.

Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (StatsSA). It collects data on the labour market activities of individuals aged 15 years or older who live in South Africa. Since 2008, StatsSA have produced an annual dataset based on the QLFS data, "Labour Market Dynamics in South Africa". The dataset is constructed using data from all all four QLFS datasets in the year. The dataset also includes a number of variables (including income) that are not available in any of the QLFS datasets from 2010.

Geographic coverage

The survey had national coverage.

Analysis unit

Individuals

Universe

The QLFS sample covers the non-institutional population except for those in workers' hostels. However, persons living in private dwelling units within institutions are enumerated. For example, within a school compound, one would enumerate the schoolmaster's house and teachers' accommodation because these are private dwellings. Students living in a dormitory on the school compound would, however, be excluded.

Kind of data

Sample survey data

Sampling procedure

Each year the LMDSA is created by combining the QLFS waves for that year and then including some additional variables. The QLFS master frame for this LMDSA was based on the 2011 population census by Stas SA. The sampling is stratified by province, district, and geographic type (urban, traditional, farm). There are 3324 PSUs drawn each year, using probability proportional to size (PPS) sampling. In the second stage Dwelling Units (DUs) are systematically selected from PSUs. The 3324 PSU are split into four groups for the year, and at each quarter the DUs from the given group are replaced by substitute DUs from the same PSU or the next PSU on the list (in the same group). It should be noted that the sampling unit is the dwelling, and the unit of observation is the household. Therefore, if a household moves out of a dwelling after being in the sample for, two quarters and a new household moves in, the new household will be enumerated for two more quarters until the DU is rotated out. If no household moves into the sampled dwelling, the dwelling will be classified as vacant (or unoccupied).

Mode of data collection

Computer Assisted Telephone Interview

Data appraisal

The statistical release notes that missing values were "generally imputed" for item non-response but provides no detail on how Statistics SA did so.

1. Temporal Coverage of Data: The data collection periods are 2010, 2030, and 2050.2. Spatial Coverage and Projection:Spatial Coverage: GlobalLongitude: -180° - 180°Latitude: -90° - 90°Projection: GCS_WGS_19843. Disciplinary Scope: The data pertains to the fields of Earth Sciences and Geography.4. Data Volume: The total data volume is approximately 31.5 MB.5. Data Type: Raster (GeoTIFF)6. Thumbnail (illustrating dataset content or observation process/scene): · 7. Field (Feature) Name Explanation:a. Name Explanation: IND: Industrial Value Addedb. Unit of Measurement: Unit: US Dollars (USD)8. Data Source Description:a. Remote Sensing Data:2010 Global Vegetation Index data (Enhanced Vegetation Index, EVI, from MODIS monthly average data) and 2010 Nighttime Light Remote Sensing data (DMSP/OLS)b. Meteorological Data:From the CMCC-CM model in the Fifth International Coupled Model Intercomparison Project (CMIP5) published by the United Nations Intergovernmental Panel on Climate Change (IPCC)c. Statistical Data:From the World Development Indicators dataset of the World Bank and various national statistical agenciesd. Gross Domestic Product Data:Sourced from the project "Study on the Harmful Processes of Population and Economic Systems under Global Change" under the National Key R&D Program "Mechanisms and Assessment of Risks in Population and Economic Systems under Global Change," led by Researcher Sun Fubao at the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciencese. Other Data:Rivers, roads, settlements, and DEM, sourced from the National Oceanic and Atmospheric Administration (NOAA), Global Risk Data Platform, and Natural Earth9. Data Processing Methods(1) Spatialization of Baseline Industrial Value Added: Using 2010 global EVI vegetation index data and nighttime light remote sensing data, we addressed the oversaturation issue in nighttime light data by constructing an adjusted nighttime light index to obtain the optimal global light data. The EANTIL model was developed using NTL, NTLn, and EVI data, with the following formula:Here, EANTLI represents the adjusted nighttime light index, NTL represents the original nighttime light intensity value, and NTLn represents the normalized nighttime light intensity value. Based on the optimal light index EANTLI and the industrial value-added data from the World Bank, we constructed a regression allocation model to derive industrial value added (I), generating the global 2010 industrial value-added data with the formula:Here, I represents the industrial value added for each grid cell, and Ii represents the industrial value added for each country, EANTLi derived from ArcGIS statistical analysis and the regression allocation model.(2) Spatial Boundaries for Future Industrial Value Added: Using the Logistic-CA-Markov simulation principle and global land use data from 2010 and 2015 (from the European Space Agency), we simulated national land use changes for 2030 and 2050 and extracted urban land data as the spatial boundaries for future industrial value added. To comprehensively characterize the influence of different factors on land use and considering the research scale, we selected elevation, slope, population, GDP, distance to rivers, and distance to roads as land use driving factors. Accuracy validation using global 2015 land use data showed an average accuracy of 91.89%.(3) Estimation of Future Industrial Value Added: Based on machine learning and using the random forest model, we constructed spatialization models for industrial value added under different climate change scenarios: Here, tem represents temperature, prep represents precipitation, GDP represents national economic output, L represents urban land, D represents slope, and P represents population. The random forest model was constructed using factors such as 2010 industrial value added, urban land distribution, elevation, slope, distances to rivers, roads, railways (considering transportation), and settlements (considering noise and environmental pollution from industrial buildings), along with temperature and precipitation as climate scenario data. Except for varying temperature and precipitation values across scenarios, other variables remained constant. The model comprised 100 decision trees, with each iteration randomly selecting 90% of the samples for model construction and using the remaining 10% as test data, achieving a training sample accuracy of 0.94 and a test sample accuracy of 0.81.By analyzing the proportion of industrial value added to GDP (average from 2000 to 2020, data from the World Bank) and projected GDP under future Shared Socioeconomic Pathways (SSPs), we derived future industrial value added for each country under different SSP scenarios. Using these projections, we constructed regression models to allocate future industrial value added proportionally, resulting in spatial distribution data for 2030 and 2050 under different SSP scenarios.10. Applications and Achievements of the Dataseta. Primary Application Areas: This dataset is mainly applied in environmental protection, ecological construction, pollution prevention and control, and the prevention and forecasting of natural disasters.b. Achievements in Application (Awards, Published Reports and Articles):Achievements: Developed a method for downscaling national-scale industrial value-added data by integrating DMSP/OLS nighttime light data, vegetation distribution, and other data. Published the global industrial value-added dataset.

This dataset provides high-resolution gridded temperature and precipitation observations from a selection of sources. Additionally the dataset contains daily global average near-surface temperature anomalies. All fields are defined on either daily or monthly frequency. The datasets are regularly updated to incorporate recent observations. The included data sources are commonly known as GISTEMP, Berkeley Earth, CPC and CPC-CONUS, CHIRPS, IMERG, CMORPH, GPCC and CRU, where the abbreviations are explained below. These data have been constructed from high-quality analyses of meteorological station series and rain gauges around the world, and as such provide a reliable source for the analysis of weather extremes and climate trends. The regular update cycle makes these data suitable for a rapid study of recently occurred phenomena or events. The NASA Goddard Institute for Space Studies temperature analysis dataset (GISTEMP-v4) combines station data of the Global Historical Climatology Network (GHCN) with the Extended Reconstructed Sea Surface Temperature (ERSST) to construct a global temperature change estimate. The Berkeley Earth Foundation dataset (BERKEARTH) merges temperature records from 16 archives into a single coherent dataset. The NOAA Climate Prediction Center datasets (CPC and CPC-CONUS) define a suite of unified precipitation products with consistent quantity and improved quality by combining all information sources available at CPC and by taking advantage of the optimal interpolation (OI) objective analysis technique. The Climate Hazards Group InfraRed Precipitation with Station dataset (CHIRPS-v2) incorporates 0.05° resolution satellite imagery and in-situ station data to create gridded rainfall time series over the African continent, suitable for trend analysis and seasonal drought monitoring. The Integrated Multi-satellitE Retrievals dataset (IMERG) by NASA uses an algorithm to intercalibrate, merge, and interpolate “all'' satellite microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and potentially other precipitation estimators over the entire globe at fine time and space scales for the Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM) satellite-based precipitation products. The Climate Prediction Center morphing technique dataset (CMORPH) by NOAA has been created using precipitation estimates that have been derived from low orbiter satellite microwave observations exclusively. Then, geostationary IR data are used as a means to transport the microwave-derived precipitation features during periods when microwave data are not available at a location. The Global Precipitation Climatology Centre dataset (GPCC) is a centennial product of monthly global land-surface precipitation based on the ~80,000 stations world-wide that feature record durations of 10 years or longer. The data coverage per month varies from ~6,000 (before 1900) to more than 50,000 stations. The Climatic Research Unit dataset (CRU v4) features an improved interpolation process, which delivers full traceability back to station measurements. The station measurements of temperature and precipitation are public, as well as the gridded dataset and national averages for each country. Cross-validation was performed at a station level, and the results have been published as a guide to the accuracy of the interpolation. This catalogue entry complements the E-OBS record in many aspects, as it intends to provide high-resolution gridded meteorological observations at a global rather than continental scale. These data may be suitable as a baseline for model comparisons or extreme event analysis in the CMIP5 and CMIP6 dataset.

Groundwater samples have been collected in the hydrogeological unit, for various types of analysis. The dataset is not used to represent a particular phenomenon or observation but rather as a utility dataset to add context and reference to groundwater analysis. It represents a general description of the sample site and sample. Sampling methods vary according to the types of analysis.

Data are either (1) depths and spacings between stylolites and faults within Unit IV, (2) images from IODP drill core image logs of the locations of samples observed, (3) photomicrographs and flatbed thin section scans of thin sections cut from samples, (4) SEM BSE or EDS data collected at Cardiff University. These data form the basis of: Leah et al. "Brittle-ductile strain localisation and weakening in pelagic sediments seaward of the Hikurangi margin, New Zealand", Tectonics, Submitted. Images and data from samples collected at IODP Expedition 375 Site U1520 (38°58.1532'S, 179°7.9112'E, 3522.1 mbsl). This is just seaward (east) of the trench of the Hikurangi Margin, New Zealand.

Region of Interest (ROI) is comprised of the Belgium, the Netherlands and Luxembourg

We use the communes administrative division which is standardized across Europe by EUROSTAT at: https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units This is roughly equivalent to the notion municipalities in most countries.

From the link above, communes definition are taken from COMM_RG_01M_2016_4326.shp and country borders are taken from NUTS_RG_01M_2021_3035.shp.

images: Sentinel2 RGB from 2020-01-01 to 2020-31-12 filtered out pixels with clouds during the observation period according to QA60 band following the example given in GEE dataset info page, and took the median of the resulting pixels

  see https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED


  see also https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py

labels: ESA WorldCover 10m V100 labels mapped to the interval [1,11] according to the following map { 0:0, 10: 1, 20:2, 30:3, 40:4, 50:5, 60:6, 70:7, 80:8, 90:9, 95:10, 100:11 } pixel value zero is reserved for invalid data. see https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100

  see also https://github.com/rramosp/geetiles/blob/main/geetiles/defs/esaworldcover.py

_aschips.geojson the image chips geometries along with label proportions for easy visualization with QGIS, GeoPandas, etc.

_communes.geojson the communes geometries with their label prortions for easy visualization with QGIS, GeoPandas, etc.

splits.csv contains two splits of image chips in train, test, val - with geographical bands at 45° angles in nw-se direction - the same as above reorganized to that all chips within the same commune fall within the same split.

data/ a pickle file for each image chip containing a dict with - the 100x100 RGB sentinel 2 chip image - the 100x100 chip level lavels - the label proportions of the chip - the aggregated label proportions of the commune the chip belongs to

Data found in this dataset was collected from the Climate Data Online (CDO) of the National Centers For Environmental Information (NCEI). It contains daily country average precipitation and air temperature data (in metric units). The original dataset collected from the CDO's site consisted of around 4.9 million individual observations from 1306 distinct weather stations throughout the three countries. Missing data points were imputed with the daily mean and averaged across all weather stations within the country.

• Precipitation - How much rain, snow, hail, etc has fallen. Measured in centimeters (cm).
• Snow depth - How much snow has collected on the ground. Measured in millimeters (mm).
• Temperature average - Country average of daily mean temperatures. Measured in degrees Celsius (°C).
• Temperature maximum - Country average of daily maximum temperatures. Measured in degrees Celsius (°C).
• Temperature minimum - Country average of daily minimum temperatures. Measured in degrees Celsius (°C).

Additional notes on the original dataset for consideration: - Not every weather station reported every day (records/samples or rows of data) - Not every weather station reported on every observation (precipitation, snow depth, temperature average, temperature) - Percentage of missing data should be considered

Percentage of missing values in the original datasets: | Country | Precipitation | Snow depth | TAVG | TMAX | TMIN | | --- | --- | --- | --- | --- | --- | | Finland | 24% | 96% | 89% | 38% | 38% | | Norway | 12% | 30% | 94% | 63% | 64% | | Sweden | 5% | 43% | 99% | 65% | 65% |

This data set contains meteorological element observation data from January 1, 2014 to December 31, 2014 from the burg station upstream of heihe hydrometeorological observation network.The station is located in caochang, qilian county, qinghai province.The latitude and longitude of the observation point is 100.9151e, 37.9492n and 3294m above sea level.The air temperature and relative humidity sensors are located at 5m, facing due north.The barometer is installed in the anti-skid box on the ground;The tilting bucket rain gauge is installed at 10m;The wind speed and direction sensor is set at 10m, facing due north;The four-component radiometer is installed at 6m, facing due south;Two infrared thermometers are installed at 6m, facing due south, and the probe facing vertically downward;The soil temperature probe is buried at 0cm on the surface and 4cm underground, 10cm, 20cm, 40cm, 80cm, 120cm, 160cm, 2m to the south of the meteorological tower.The soil water probe is buried at 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm underground, 2m to the south of the meteorological tower.The soil heat flow plates (3 pieces) are buried in the ground 6cm underground, 2m to the south of the meteorological tower. Observation projects are: air temperature and humidity (Ta_5m, RH_5m) (unit: c, percentage), pressure (Press) (unit: hundred mpa), precipitation (Rain) (unit: mm), wind speed (WS_10m) (unit: m/s), wind (WD_10m) (unit: degrees), the radiation of four component (DR, UR, DLR_Cor, ULR_Cor, Rn) (unit: watts per square meter), the surface radiation temperature (IRT_1, IRT_2) (unit:Temperature (Ts_0cm, Ts_4cm, Ts_10cm, Ts_20cm, Ts_40cm, Ts_80cm, Ts_120cm, Ts_160cm) (unit: Celsius), soil moisture (Ms_4cm, Ms_10cm, Ms_20cm, Ms_40cm, Ms_80cm, Ms_120cm, Ts_160cm) (unit: volumetric water content, percentage). Processing and quality control of observed data :(1) ensure 144 pieces of data every day (every 10min), and mark by -6999 in case of data missing;The temperature of 4cm soil was between May 31, 2014 and June 17, 2014. Due to sensor problems, data was missing.(2) excluding the time with duplicate records;(3) data that obviously exceeds the physical significance or the range of the instrument is deleted;(4) the part marked with red letter in the data is the data in question;(5) date and time have the same format, and date and time are in the same column.For example, the time is: September 10, 2014, 10:30;(6) the naming rule is: AWS+ site name. For information of hydrometeorological network or station, please refer to Liu et al.(2018), and for observation data processing, please refer to Liu et al.(2011).

This data set contains the data of meteorological element gradient observation system of the middle reaches of heihe hydrometeorological observation network from January 1, 2015 to December 31, 2015.The station is located in the farmland of daman irrigation district of zhangye city, gansu province.The longitude and latitude of the observation point are 100.3722e, 38.8555n and 1556m above sea level.The wind speed/direction, air temperature and relative humidity sensors are located at 3m, 5m, 10m, 15m, 20m, 30m and 40m respectively, with a total of 7 layers, facing due north.The barometer is installed at 2m;The tilting bucket rain gauge was installed at about 8m on the west side of the tower, with a height of 2.5m;The four-component radiometer is installed at 12m, facing due south;Two infrared thermometers are installed at 12m, facing due south and the probe facing vertically downward.Soil heat flow plate (self-calibration formal) (3 pieces) were buried in the ground 6cm in turn, 2m away from the tower body due south, two of which (Gs_2 and Gs_3) were buried between the trees, and one (Gs_1) was buried under the plants.The mean soil temperature sensor TCAV is buried 2cm and 4cm underground, facing due south and 2m away from the tower body.The soil temperature probe is buried at 0cm of the surface and 2cm, 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm underground, 2m to the south of the meteorological tower.The soil water sensor is buried 2cm, 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm underground, 2m to the south of the meteorological tower.The photosynthetic effective radiometer is installed at 12m with the probe facing vertically upward.Four other photosynthetically active radiometers were installed above and inside the canopy, 12m above the canopy (one probe vertically up and one probe vertically down), and 0.3m above the canopy (one probe vertically up and one probe vertically down), facing due south. The observation items are: wind speed (WS_3m, WS_5m, WS_10m, WS_15m, WS_20m, WS_30m, WS_40m) (unit: m/s), wind direction (WD_3m, WD_5m, WD_10m, WD_15m, WD_20m, WD_30m, WD_40m) (unit:Air temperature and humidity (Ta_3m, Ta_5m, Ta_10m, Ta_15m, Ta_20m, Ta_30m, Ta_40m and RH_3m, RH_5m, RH_10m, RH_15m, RH_20m, RH_30m, RH_40m) (unit: Celsius, percentage), air pressure (Press) (unit: hpa), precipitation (Rain) (unit: mm), four-component radiation (DR, UR, DLR_Cor, ULR_Cor, Rn) (unit:Watts/m2), surface radiant temperature (IRT_1, IRT_2) (unit: Celsius), average soil temperature (TCAV) (unit: Celsius), soil heat flux (Gs_1, Gs_2, Gs_3) (unit: watts/m2), soil moisture (Ms_2cm, Ms_4cm, Ms_10cm, Ms_20cm, Ms_40cm, Ms_80cm, Ms_120cm, Ms_160cm) (unit:Soil temperature (Ts_0cm, Ts_2cm, Ts_4cm, Ts_10cm, Ts_20cm, Ts_40cm, Ts_80cm, Ts_120cm, Ts_160cm)Mmol/m s) and the upward and downward photosynthetic effective radiation (PAR_D_up, PAR_D_down) under the canopy (in mmol/m s). Processing and quality control of observed data :(1) ensure 144 pieces of data every day (every 10min), and mark by -6999 in case of data missing;The wind speed and direction of 3m and 5m were missing due to sensor problems between November 16 and November 25, 2015;(2) excluding the time with duplicate records;(3) data that obviously exceeds the physical significance or the range of the instrument is deleted;(4) the part marked with red letter in the data is the data in question;(5) date and time have the same format, and date and time are in the same column.For example, the time is: June 10, 2015, 10:30;(6) the naming rule is: AWS+ site name. For information of hydrometeorological network or station, please refer to Liu et al. (2018), and for observation data processing, please refer to Liu et al. (2011).

This data set contains meteorological element observation data from January 1, 2014 to December 31, 2014 from the grand salon station upstream of heihe hydrometeorological observation network.The station is located in shalantan, west of qilian county, qinghai province.The latitude and longitude of the observation point is 98.9406e, 38.8399n and 3739m above sea level.The air temperature and relative humidity sensors are located at 5m, facing due north.The barometer is installed in the anti-skid box on the ground;The tilting bucket rain gauge is installed at 10m;The wind speed and direction sensor is set at 10m, facing due north;The four-component radiometer is installed at 6m, facing due south;Two infrared thermometers are installed at 6m, facing due south, and the probe facing vertically downward;The soil temperature probe is buried at 0cm on the surface and 4cm underground, 10cm, 20cm, 40cm, 80cm, 120cm, 160cm, 2m to the south of the meteorological tower.The soil water probe is buried at 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm underground, 2m to the south of the meteorological tower.The soil hot plates (3 pieces) are buried in the ground 6cm underground and 2m to the south of the weather tower. Observation projects are: air temperature and humidity (Ta_5m, RH_5m) (unit: c, percentage), pressure (Press) (unit: hundred mpa), precipitation (Rain) (unit: mm), wind speed (WS_10m) (unit: m/s), wind (WD_10m) (unit: degrees), the radiation of four component (DR, UR, DLR_Cor, ULR_Cor, Rn) (unit: watts per square meter), the surface radiation temperature (IRT_1, IRT_2) (unit:Temperature (Ts_0cm, Ts_4cm, Ts_10cm, Ts_20cm, Ts_40cm, Ts_80cm, Ts_120cm, Ts_160cm) (unit: Celsius), soil moisture (Ms_4cm, Ms_10cm, Ms_20cm, Ms_40cm, Ms_80cm, Ms_120cm, Ts_160cm) (unit: volumetric water content, percentage). Processing and quality control of observed data :(1) ensure 144 pieces of data every day (every 10min), and mark by -6999 in case of data missing;Due to insufficient power supply, data was missing between January 1, 2014 and January 19, 2014.(2) excluding the time with duplicate records;(3) data that obviously exceeds the physical significance or the range of the instrument is deleted;(4) the part marked with red letter in the data is the data in question;(5) date and time have the same format, and date and time are in the same column.For example, the time is: September 10, 2014, 10:30;(6) the naming rule is: AWS+ site name. For information of hydrometeorological network or station, please refer to Liu et al.(2018), and for observation data processing, please refer to Liu et al.(2011).

This data set includes observation data of meteorological elements in the Shenshawo Desert Station in the middle of the Heihe Hydrometeorological Observation Network from January 1, 2015 to April 12, 2015. The site is located in Shenshawo, Zhangye City, Gansu Province, and the underlying surface is desert. The latitude and longitude of the observation point is 100.4933E, 38.7892N, and the altitude is 1594m. The air temperature and relative humidity sensors are installed at 5m and 10m, facing the north; the barometer is installed at 2m; the tipping bucket rain gauge is installed at 10m; the wind speed sensor is set at 5m, 10m, and the wind direction sensor is set at 10m, facing the north; the four-component radiometer is installed at 6m, facing south; two infrared thermometers are installed at 6m, facing south, the probe orientation is vertically downward; the soil temperature probe is buried in the ground surface 0cm and underground 2cm, 4cm, 10cm, 20cm 40cm, 60cm and 100cm, in the south of the 2m from the meteorological tower; soil moisture sensors are buried in the underground 2cm, 4cm, 10cm, 20cm, 40cm, 60cm and 100cm, in the south of the 2m from the meteorological tower, and among them a repetitive soil moisture sensor (Ms_40cm_2) was embedded in the underground 40cm on May 6, 2014.soil heat flux plates (3 pieces) are buried in the ground 6 cm in order. Observation items include: air temperature and humidity (Ta_5m, RH_5m, Ta_10m, RH_10m) (unit: centigrade, percentage), air pressure (Press) (unit: hectopascal), precipitation (Rain) (unit: mm), wind speed (WS_5m, WS_10m) (unit: m / s), wind direction (WD_10m) (unit: degree), four-component radiation (DR, UR, DLR_Cor, ULR_Cor, Rn) (unit: watts / square meter), surface radiation temperature (IRT_1, IRT_2 ) (unit: centigrade), soil heat flux (Gs_1, Gs_2, Gs_3) (unit: watts/square meter), soil moisture (Ms_2cm, Ms_4cm, Ms_10cm, Ms_20cm, Ms_40cm, Ms_60cm, Ms_100cm) (unit: volumetric water content, percentage) and soil temperature (Ts_0cm, Ts_2cm, Ts_4cm, Ts_10cm, Ts_20cm, Ts_40cm, Ts_60cm, Ts_100cm) (unit: centigrade). Processing and quality control of the observation data: (1) ensure 144 data per day (every 10 minutes), when there is missing data, it is marked by -6999; From March 19, 2015 to March 26, due to the collector problem, the data is missing; (2) eliminate the moment with duplicate records; (3) delete the data that is obviously beyond the physical meaning or the range of the instrument; (5) the format of date and time is uniform, and the date and time are in the same column. For example, the time is: 2015-6-10 10:30; (6) the naming rules are: AWS+ site name. The station was dismantled after April 12. For hydrometeorological network or site information, please refer to Li et al. (2013). For observation data processing, please refer to Liu et al. (2011).

The data set contains meteorological element observation data from January 1, 2017 to December 31, 2017 from jingyangling station, upstream of heihe hydrological meteorological observation network.The station is located in jingyangling pass, qilian county, qinghai province.The longitude and latitude of the observation point are 101.1160e, 37.8384N and 3750m above sea level.The air temperature and relative humidity sensors are located at 5m, facing due north.The barometer is installed in the anti-skid box on the ground;The tilting bucket rain gauge is installed at 10m;The wind speed and direction sensor is set at 10m, facing due north;The four-component radiometer is installed at 6m, facing due south;Two infrared thermometers are installed at 6m, facing due south, and the probe facing vertically downward;The soil temperature probe is buried at 0cm on the surface and 4cm underground, 10cm, 20cm, 40cm, 80cm, 120cm, 160cm, 2m to the south of the meteorological tower.The soil water probe is buried at 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm underground, 2m to the south of the meteorological tower.The soil heat flow plates (3 pieces) are buried in the ground 6cm underground, 2m to the south of the meteorological tower. Observation items are: air temperature and humidity (Ta_5m, RH_5m) (unit: c, percentage), pressure (Press) (unit: hundred mpa), precipitation (Rain) (unit: mm), wind speed (WS_10m) (unit: m/s), wind (WD_10m) (unit: degrees), the radiation of four component (DR, UR, DLR_Cor, ULR_Cor, Rn) (unit: watts per square meter), the surface radiation temperature (IRT_1, IRT_2) (unit:Soil heat flux (Gs_1, Gs_2, Gs_3) (in watts/m2), soil temperature (Ts_0cm, Ts_4cm, Ts_10cm, Ts_20cm, Ts_80cm, Ts_120cm, Ts_160cm) (in Celsius), soil moisture (Ms_4cm, Ms_10cm, Ms_20cm, Ms_40cm, Ms_80cm, Ms_120cm, Ms_160cm) (unit: percentage). Processing and quality control of observed data :(1) ensure 144 pieces of data every day (every 10min), and mark by -6999 in case of data missing;Some invalid values of 4cm soil moisture appeared in November and December.5.13-5.27 and 6.7-7.5, data is missing due to problems with the collector;7.17-8.17 problems with the wind speed sensor and missing data;Problems with the infrared temperature sensor and missing data;(2) excluding the time with duplicate records;(3) data that obviously exceeds the physical significance or the range of the instrument is deleted;(4) the part marked with red letter in the data is the data in question;(5) date and time have the same format, and date and time are in the same column.For example, the time is: 2017-9-1010:30;(6) the naming rule is: AWS+ site name. For information of hydrometeorological network or station, please refer to Li et al. (2013), and for observation data processing, please refer to Liu et al. (2011).