Diveboard (https://www.diveboard.com/) is an online scuba diving citizen science platform, where divers can digitize or log their dives, participate in citizen science surveys and projects, and interact with others. More then 10,000 divers have already registered with Diveboard and the community is still growing. This dataset contains all observations made by Diveboarders worldwide (mainly fishes) and are linked to the Encyclopedia of Life. The Diveboard community has dedicated the data to the public domain under a Creative Commons Zero waiver, so these can be used as widely as possible. If you have a specific survey need or question, get in touch: Diveboarders are everywhere and willing to help!

The EXOMASTER database contains the EXOSAT observation log. This is a complete list of all EXOSAT observations, observing modes, and principal investigators. The log can be used to find out which targets were observed by EXOSAT, who observed them and the observation configuration. In addition this database can also be used to check the availability of the FOT (Final Observation Tape) files (the original raw data files) and their reformatted FITS files. This database table was originally created in September/October, 1997. The HEASARC revised this database table in August, 2006, in order to fix the equatorial coordinates (which were in the wrong equinox) and to rename or convert some of the time-related fields to better conform with current HEASARC practices. This is a service provided by NASA HEASARC .

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. ○ ○

This set of data holdings provides access to data collected from land surface meteorological observations across the globe. Data are available at the observational level and also at daily and monthly aggregations. Data have been collated and harmonised and quality control checks have been performed, but no attempt has been made to assess for potential biases. Data are provided for a range of commonly observed variables. Surface meteorological observations taken by a broad variety of organisations, including but not limited to National Meteorological Services, are being collated, reconciled, and harmonised. This work provides data that can underpin the development of reanalyses products as well as the production of gridded products and data services. Work is ongoing to prepare and analyse several hundred sources secured to date and new sources continue to accrue. Documentation on sources secured to date can be found in the inventory information provided in the documentation tab. Users with new sources are invited to upload these via the data deposition service for ingestion and consideration. Sources are available in a broad range of formats and frequently individual series have been shared multiple times. Steps have been undertaken to harmonise data formats and reconcile data sources to yield the longest possible individiual station records. Station period-of-record varies on a station-by-station basis with most stations starting after 1950, but many stations extending much further back in time. Daily and monthly aggregations extend further back in time than sub-daily records in general. Periodic updates and reissues shall refresh historical holdings availability and will also build on aspects such as quality checking and flagging of data. Each such release will be accompanied by a version number increment and a reissue of the product user guide. Updates for recent observations are in process of being provided at a latency of 1-2 days for those stations that are operational and sharing data across the WMO Information System, starting with updates to the daily holdings. Data are available for several commonly observed variables as described in the main variables table. Note that which variables are available varies by the chosen timescale of data aggregation and by station. To aid users an inventory of stations by name, location, start and end date, and variables available is provided in the documentation page differentiated by timescale. Attributes are described in the related-variables tables. The datasets can be downloaded as NetCDF files (CDM-Obs-Core, see documentation) or as comma-separated values (CSV) files. Users should ensure an appropriate assessment of long-term data quality prior to use in those applications which require consideration of such aspects.
More details about the land meteorological station holdings are available in the product user guide, and details around data formatting can be found in the common data model documentation, both of which can be found in the documentation section. This work is being completed on behalf of C3S in sustained collaboration with colleagues at NOAA's National Centres for Environmental Information who are the WMO designated World Data Centre for meteorology.

The "Data Inventory of Space-Based Celestial Observations Version 1.0" (DISCO) is a directory to data contained in sixteen catalogs dealing with observations from space. (Sounding rocket, solar, and planetary observations have been excluded.) The information extracted from the catalogs includes names of objects observed, 1950 equatorial coordinates, and the name of the catalog or instrument. A second file contains full references to the source catalogs and other pertinent information. The purpose of creating DISCO is (1) to unify astronomical observations from space, which are at present scattered and hard to locate, and then (2) to provide a machine-readable index to these observations, thus enabling easy access by computer. Such a directory will permit an astronomer to find out what objects have been observed from space, which spacecraft and instruments made the observations, and where to go to find the data themselves.

The analysis of research data plays a key role in data-driven areas of science. Varieties of mixed research data sets exist and scientists aim to derive or validate hypotheses to find undiscovered knowledge. Many analysis techniques identify relations of an entire dataset only. This may level the characteristic behavior of different subgroups in the data. Like automatic subspace clustering, we aim at identifying interesting subgroups and attribute sets. We present a visual-interactive system that supports scientists to explore interesting relations between aggregated bins of multivariate attributes in mixed data sets. The abstraction of data to bins enables the application of statistical dependency tests as the measure of interestingness. An overview matrix view shows all attributes, ranked with respect to the interestingness of bins. Complementary, a node-link view reveals multivariate bin relations by positioning dependent bins close to each other. The system supports information drill-down based on both expert knowledge and algorithmic support. Finally, visual-interactive subset clustering assigns multivariate bin relations to groups. A list-based cluster result representation enables the scientist to communicate multivariate findings at a glance. We demonstrate the applicability of the system with two case studies from the earth observation domain and the prostate cancer research domain. In both cases, the system enabled us to identify the most interesting multivariate bin relations, to validate already published results, and, moreover, to discover unexpected relations.

The dataset presents a snapshot of crowdsourcing data collected on iNaturalist.org and identified by a group of professional mycologists during a revision of crowdsourcing data project (https://sibmyco.org/collections/inaturalist-revision/). The resulted data were exported from iNaturalist.org, analysed and described in a related publication (Filippova et al., 2022).

A regional mycological club was established in 2018, encouraging its members to contribute observations of fungi to iNaturalist.org (https://www.inaturalist.org/projects/fungi-and-myxomycetes-in-northern-west-siberia). A total of about 15K observations of fungi and myxomycetes were uploaded so far, by about 200 observers, from three administrative regions (Yamalo-Nenets Autonomous Okrug, Khanty-Mansi Autonomous Okrug and Tyumen Region).

The goal of the work presented in this study consisted in a collaborative effort of professional mycologists who were invited to help with the identification of these observations and analyze the accumulated data. As the result, all observations were reviewed by at least one expert. About a half of all observations have been identified reliably to the species level and received Research Grade status. Of those, 197 records of 92 species represented finds of taxa new to their respective regions; such finds are listed in the Attachment with brief commentaries; 876 records of 53 species of different protection status provide important data for conservation programs.

The remaining half of the observations consists of records that remained under-identified for various reasons: poor quality photographs, complex taxa (impossible to identify without microscopic or molecular study), or lack of experts in a particular taxonomic group.

Despite that the half of presented records already published through GBIF in "iNaturalist Research-grade Observations" dataset (https://doi.org/10.15468/ab3s5x), we decided to publish a dataset which includes all observations updated by the date of paper publishing and preserved at this state for archiving purposes. The dataset is made with the assumed pros and contras in mind:

Pros: 1) We want to preserve the results of revision work with iNaturalist-based observations in a stable archive. On the contraty, the observations data published on iNaturalist.org are always revised and updated; 2) We aim to publish raw (source) data for the revision paper analyses in a single FAIR archive; 3) We want all records published on iNaturalist.org to be used in further study of fungi in the region and globally, including "Needs ID" status observations; provided that these records were reviewed by professional mycologists and received at least two expert opinions; 4) The resulting dataset was augmented with several fields to add information about regional novelties and identification remarks.

Contras: 1) Duplication of records in two dataset (iNaturalist and ours), which nevertheless could be solved by GBIF duplicates search algorithm.

This data set contains the hourly values of 31 parameters that are a combination of observations from the Atmospheric Surface Flux Group (ASFG) SHEBA site and from other SHEBA sites (Quebec 2, Pittsburgh, and Mainline). Data were collected from 9 October 1997 through 8 October 1998. Data are provided in both Matlab file and ASCII file format, with two data files for each file format.

The dataset consists of hourly U.S. surface airways observations (SAO). These observations extend as far back as 1928, from the time when commercial aviation began in the United States and meteorological observing stations were established at many airports (although occasionally, early-period SAO's were taken at U.S. Weather Bureau city offices). For most stations, this dataset extends through June of 1948. The major data variables are as follows: WBAN Station Identification Number, observational type, ceiling and cloud, visibility, present weather data, temperature, wind and pressure. The observations are generally recorded for the 24-hour period midnight to midnight, although many stations did not record 24-hour observations, especially early in the period when commercial aviation was just getting started. Two output keying formats were created to adjust to an observational form change during the period. One format was generally used for years 1928-33, and the other for sets from around 1934 through June of 1948. Each keying format was designed to reflect the data as entered on the observational form for ease of keying by key entry personnel, who were not trained meteorological technicians. The "raw" observations which comprise the DSI-3851 dataset were quality checked, to include data adjustments, and converted to NCDC's Integrated Surface Hourly (ISH) format.

The complimentary data to this collection can be found in the Surface Weather Observation 1001 Forms (Keyed) collection.

README

WARNING

Below is a template to write a README file for this BIDS dataset. If this message is still present, it means that the person exporting the file has decided not to update the template.If you are the researcher editing this README file, please remove this warning section. The README is usually the starting point for researchers using your dataand serves as a guidepost for users of your data. A clear and informativeREADME makes your data much more usable. In general you can include information in the README that is not captured by some otherfiles in the BIDS dataset (dataset_description.json, events.tsv, ...). It can also be useful to also include information that might already bepresent in another file of the dataset but might be important for users to be aware ofbefore preprocessing or analysing the data. If the README gets too long you have the possibility to create a /doc folderand add it to the .bidsignore file to make sure it is ignored by the BIDS validator. More info here: https://neurostars.org/t/where-in-a-bids-dataset-should-i-put-notes-about-individual-mri-acqusitions/17315/3

Details related to access to the data

• [ ] Data user agreement If the dataset requires a data user agreement, link to the relevant information.
• [ ] Contact person Indicate the name and contact details (email and ORCID) of the person responsible for additional information.
• [ ] Practical information to access the data If there is any special information related to access rights orhow to download the data make sure to include it.For example, if the dataset was curated using datalad,make sure to include the relevant section from the datalad handbook:http://handbook.datalad.org/en/latest/basics/101-180-FAQ.html#how-can-i-help-others-get-started-with-a-shared-dataset ## Overview
• [ ] Project name (if relevant)
• [ ] Year(s) that the project ran If no scans.tsv is included, this could at least cover when the data acquisitionstarter and ended. Local time of day is particularly relevant to subject state.
• [ ] Brief overview of the tasks in the experiment A paragraph giving an overview of the experiment. This should include thegoals or purpose and a discussion about how the experiment tries to achievethese goals.
• [ ] Description of the contents of the dataset An easy thing to add is the output of the bids-validator that describes what type ofdata and the number of subject one can expect to find in the dataset.
• [ ] Independent variables A brief discussion of condition variables (sometimes called contrastsor independent variables) that were varied across the experiment.
• [ ] Dependent variables A brief discussion of the response variables (sometimes called thedependent variables) that were measured and or calculated to assessthe effects of varying the condition variables. This might also includequestionnaires administered to assess behavioral aspects of the experiment.
• [ ] Control variables A brief discussion of the control variables --- that is what aspectswere explicitly controlled in this experiment. The control variables mightinclude subject pool, environmental conditions, set up, or other thingsthat were explicitly controlled.
• [ ] Quality assessment of the data Provide a short summary of the quality of the data ideally with descriptive statistics if relevantand with a link to more comprehensive description (like with MRIQC) if possible. ## Methods ### Subjects A brief sentence about the subject pool in this experiment. Remember that Control or Patient status should be defined in the participants.tsvusing a group column.
• [ ] Information about the recruitment procedure- [ ] Subject inclusion criteria (if relevant)- [ ] Subject exclusion criteria (if relevant) ### Apparatus A summary of the equipment and environment setup for theexperiment. For example, was the experiment performed in a shielded roomwith the subject seated in a fixed position. ### Initial setup A summary of what setup was performed when a subject arrived. ### Task organization How the tasks were organized for a session.This is particularly important because BIDS datasets usually have task dataseparated into different files.)
• [ ] Was task order counter-balanced?- [ ] What other activities were interspersed between tasks?
• [ ] In what order were the tasks and other activities performed? ### Task details As much detail as possible about the task and the events that were recorded. ### Additional data acquired A brief indication of data other than theimaging data that was acquired as part of this experiment. In additionto data from other modalities and behavioral data, this might includequestionnaires and surveys, swabs, and clinical information. Indicatethe availability of this data. This is especially relevant if the data are not included in a phenotype folder.https://bids-specification.readthedocs.io/en/stable/03-modality-agnostic-files.html#phenotypic-and-assessment-data ### Experimental location This should include any additional information regarding thethe geographical location and facility that cannot be includedin the relevant json files. ### Missing data Mention something if some participants are missing some aspects of the data.This can take the form of a processing log and/or abnormalities about the dataset. Some examples:
• A brain lesion or defect only present in one participant- Some experimental conditions missing on a given run for a participant because of some technical issue.- Any noticeable feature of the data for certain participants- Differences (even slight) in protocol for certain participants. ### Notes Any additional information or pointers to information thatmight be helpful to users of the dataset. Include qualitative informationrelated to how the data acquisition went.

The AMSR/ADEOS-II L1A Raw Observing Counts (AMSR-L1A) data set was processed from Level 0 science packet data by the JAXA Earth Observation Center (EOC) in Japan.

Our mission is to help you picture climate change and environmental changes happening on our home planet. Here you can search for and retrieve satellite images of Earth. Download them; export them to GoogleEarth; perform basic analysis. Tracking regional and global changes around the world just got easier.

ben-ge-8k: BigEarthNet Extended with Geographical and Environmental Data

M. Mommert, N. Kesseli, J. Hanna, L. Scheibenreif, D. Borth, B. Demir, "ben-ge: Extending BigEarthNet with Geographical and Environmental Data", IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023.

ben-ge-8k is a small-scale multimodal dataset for Earth observation that is a subset of the ben-ge dataset (https://github.com/HSG-AIML/ben-ge), which in turn serves as an extension to the BigEarthNet dataset. ben-ge complements the Sentinel-1/2 data contained in BigEarthNet by providing additional data modalities:

* elevation data extracted from the Copernicus Digital Elevation Model GLO-30;
* land-use/land-cover data extracted from ESA Worldcover;
* climate zone information extracted from Beck et al. 2018;
* environmental data concurrent with the Sentinel-1/2 observations from the ERA-5 global reanalysis;
* a seasonal encoding.

ben-ge-8k contains 8000 patches out of 590,326 patches in the full ben-ge dataset. These 8000 patches were sampled in such a way that for each of the 8 most common ESA WorldCover land-use/land-cover classes (tree cover, shrubland, grassland, cropland, built-up, bare/sparse vegetation, permanent water bodies, herbaceous wetland), we sampled 1000 patches randomly and used the fractional coverage of this class as a weight in the sampling process. As a result, these classes are slightly more balanced in ben-ge-8k than in the full dataset.

Data Modalities and Products

Meta Data

Relevant meta data for the ben-ge-8k dataset are compiled in the file ben-ge-8k_meta.csv. This file resides on the root level of this archive and contains the following data for each patch:
* patch_id: the Sentinel-2 patch id, which plays a central role for cross-referencing different data modalities for individual patches;
* patch_id_s1: the Sentinel-1 patch id for this specific patch;
* timestamp_s2: the timestamp for the Sentinel-2 observation;
* timestamp_s1: the timestamp for the Sentinel-1 observation;
* season_s2: the seasonal encoding (see below) for the time of the Sentinel-2 observation;
* season_s1: the seasonal encoding (see below) for the time of the Sentinel-1 observation;
* lon: longitude (WGS-84) of the center of the patch [degrees];
* lat: latitude (WGS-84) of the center of the patch [degrees];
* climatezone: integer value indicating the climate zone based on Beck et al. 2018 (see below for details).

Digital Elevation Model (Copernicus DEM GLO-30)

DEM data are contained in the dem/ directory of this archive.

Topographic maps are generated based on the global Copernicus Digital Elevation Model (GLO-30) (https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model). Relevant GLO-30 map tiles from the 2021 data release were downloaded through AWS (https://registry.opendata.aws/copernicus-dem/), reprojected into the coordinate frame of the corresponding Sentinel-1/2 patches and interpolated with bilinear resampling to 10 m resolution on the ground.

Elevation data are provided in a separate geotiff file for each patch. The naming convention for these files uses the Sentinel-2 patch_id to which we append _dem.tif. Each file contains a single band with 16-bit integer values that refer to the elevation of that pixel over sea level.

Land-use/Land-cover Data (ESA WorldCover)

Land-use/land-cover data are contained in the esaworldcover/ directory of this archive.

Land-use/land-cover map tiles matching the Sentinel-1/2 patches were extracted from ESA WorldCover (https://esa-worldcover.org). Relevant tiles were downloaded and reprojected into the coordinate frame of the corresponding Sentinel-1/2 patches. WorldCover data are available both as maps and as class fractions that are aggregated over each patch.

Land-use/land-cover map data are provided in a separate geotiff file for each patch. The naming convention for these files uses the Sentinel-2 patch_id to which we append _esaworldcover.tif. Each file contains a single band with 8-bit integer values that map to land-use/land-cover definitions provided by the ESA WorldCover Product User Manual (https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PUM_V2.0.pdf) (page 15).

The file ben-ge-8k_esaworldcover.csv contains the fractions by which each of the different classes cover the corresponding patch. This product may be useful to generate single-label or multi-label targets for different classification setups.

Environmental Data (ERA-5)

Weather data are contained in the ben-ge-8k_era-5.csv file.

Weather data at the time of observation (temperature at 2 m above the ground, relative humidity, wind vectors at 10 m above the ground) are extracted from the ERA-5 global reanalysis (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels) for the pressure level at the mean elevation of the observed scene and the time of observation (separately queried for Sentinel-1/2 observations).

Environmental data are available in the file ben-ge-8k_era-5.csv. For each patch, identified through the Sentinel-2 patch_id or the corresponding Sentinel-1 patch id patch_id_s1, the file contains the following parameters:
* atmpressure_level: atmospheric pressure level at which parameters have been queried [mbar]
* temperature_s2: temperature 2m above ground at the time of the Sentinel-2 observation [K]
* temperature_s1: temperature 2m above ground at the time of the Sentinel-1 observation [K]
* wind-u_s2: eastward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-2 observation [m/s]
* wind-u_s1: eastward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-1 observation [m/s]
* wind-v_s2: northward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-2 observation [m/s]
* wind-v_s1: northward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-2 observation [m/s]
* relhumidity_s2: relative humidity at the time of the Sentinel-2 observation [%]
* relhumidity_s1: relative humidity at the time of the Sentinel-1 observation [%]

as extracted from the ERA-5 global reanalysis (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels) for the patch location. Please see the corresponding documentation for details.

Seasonal Encoding
To capture the season at the time of observation, we apply a non-linear encoding that scale the date of the observation into the interval [0, 1], referring to [winter, summer] solstice. For any given date, we derive the fractional year and shift it by 9 days such that 21 June has the fractional year 0.5 and 22 December has the fractional year 0 or 1. To account for this ambiguity and the periodicity of the seasons, we modulate the fractional year with a sine function such that 21 June leads to a seasonal encoding of 1 and 22 December leads to a seasonal encoding of 0.

Seasonal encodings are provided by the column season in the ben-ge-8k_meta.csv file. Season values cover the interval [0,1] as a continuous variable where 1 refers to summer solstice and 0 refers to winter solstice.

Climate zone classification (Beck et al. 2018)

Patch-based climate zone classifications, based on the Köppen-Geiger scheme, were extracted from Beck et al. (2018) (https://www.nature.com/articles/sdata2018214), utilizing their present-day 1-km resolution map. Due to geographical focus of BigEarthNet on Europe, only 11 out of 27 different classes are present in this dataset. Please note that patches that are fully covered by surface water have no climate zone class assigned to them (class label equals zero in this case). Labels are encoded as discrete integer values that follow the schema introduced by Beck et al. 2018 in their legend.txt file that is included here: https://doi.org/10.6084/m9.figshare.6396959.

Climate zone classification labels are provided by the column climatezone in the ben-ge-8k_meta.csv file.

File and directory structure

This archive contains the following directory and file structure:

|
|--- README (this file)
|--- ben-ge-8k_meta.csv (ben-ge-8k meta data)
|--- ben-ge-8k_era-5.csv (ben-ge-8k environmental data)
|--- ben-ge-8k_esaworldcover.csv (patch-wise ben-ge-8k land-use/land-cover data)
|--- dem/ (digital elevation model data)
| |--- S2A_MSIL2A_20171208T093351_3_82_dem.tif
| ...
|--- esaworldcover/ (land-use/land-cover data)
| |--- S2B_MSIL2A_20170914T93030_26_83_esaworldcover.tif
| ...
|--- sentinel-1/ (Sentinel-1 SAR data)
| |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43/
| |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_labels_metadata.json (BigEarthNet label file)
| |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VH.tif (BigEarthNet/Sentinel-1 VH polarization data)
| |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VV.tif (BigEarthNet/Sentinel-1 VV polarization data)
| ...
|--- sentinel-2/ (Sentinel-2 multispectral data)
| |--- S2B_MSIL2A_20170818T112109_31_83/
| |--- S2B_MSIL2A_20170818T112109_31_83_B01.tif (BigEarthNet/Sentinel-2 Band 1

KNMI collects observations from the automatic weather stations situated in the Netherlands and BES islands on locations such as aerodromes, North Sea platforms and wind poles. This dataset provides metadata on these weather stations, such as location, name and type. The data in this dataset is formatted as NetCDF. It is also available as a CSV file in this dataset: https://dataplatform.knmi.nl/dataset/waarneemstations-csv-1-0.

This data set consists of the hourly and special METAR surface observations from 39 sites within the UMRBPP region. The data were retrieved from the Pennsylvania State University for the UMRBPP period (5 April to 5 May 1999. The data are in METAR format. Please see the README file for more information.

Japanese Ocean Flux Data Sets with Use of Remote Sensing Observations (J-OFURO) is a research project on air-sea heat, momentum, and freshwater fluxes in the Earth system. The project also provides the satellite-derived data set of air-fluxes over the global oceans with the exception of the sea ice areas.

J-OFURO3 is a third-generation data set in the project. After the first release of J-OFURO3 (V1.0), the dataset has been updated to V1.1. In the V1.1, the data provision period was extended to 1988-2017 and some of the problems were fixed. Furthermore, by using newer satellite observation data, J-OFURO3 V1.1 succeeded to improve data quality and quantity.

J-OFURO3 V1.1 is provided as a numerical data set of air-sea fluxes in netCDF format, along with relevant physical parameters including sea surface temperature, surface wind speed, and surface air specific humidity and so on. The daily and monthly mean data on 0.25 degree grids for 1988-2017 are currently available.

The dataset contains reports of phenological observations, made at more than 700 locations throughout Sweden between 1865 and 1951. The observations were mainly conducted by local weather observers, assigned by the Swedish Weather Service (formerly named SMCA, now SMHI), so weather data can also be found for many of these locations, to support to the phenological observations. For weather data, contact SMHI (see www.smhi.se). The dataset consists of 345 806 posts of phenology observations, in total. The main part of the phenological data consists of observations of budburst, flowering, ripening of fruits and autumn coloured leaves on plants and trees, but also spring and autumn migration of migratory birds, agricultural activities like when spring tillage begins and sowing and harvest begins, and also activities of a few insects. The dataset was digitized and supplied by the Swedish National Phenology Network (SWE-NPN), a network lead by the Swedish University of Agricultural Sciences (SLU), and includes partners from several Swedish universities, governmental agencies and non-governmental organizations. Uppsala University and SMHI has contributed to this dataset, by digitizing the original forms, which they possess. For more information about the Phenology network, see www.swe-npn.se. The phenological observations were made according to the protocol that was set by the SMCA (see uploaded files of the forms used by the observers, in Swedish). The observations was assessed regularly, on average once a week, or when needed, and the date when a new phenological phase occurred was noted in a form. Digital copies of the original forms are available on request. The dataset contains a file with observations, observation_standalone.csv, which can be used alone, without supporting files. This file contains all necessary information for using the data, e.g. coordinates (lat/long) for the location where the observation were made, species name (scientific), phenological phase and the date. The dataset also contains a file with observations, observation.csv, where the location, officiant, species and phenological phase are coded. To use this file, you need the supporting files included in the dataset, for locations, officiant names, species names and phenological phase descriptions. Datasetet innehåller rapporter om fenologiska observationer gjorda på över 700 platser i Sverige mellan åren 1865 och 1951. Observationerna gjordes framför allt av SMCA:s (Sveriges Meteorologiska Centralanstalt, föregångaren till SMHI) lokala väderobservatörer och väderdata finns att tillgå för många ställen, som stöd till dessa observationer (kontakta SMHI för data-förfrågan, se www.smhi.se). Datasetet omfattar totalt 345 806 poster med observationer. Fenologiska data består framför allt av observationer om lövsprickning, blomningstidpunkt, bärmognad och höstfärgning av löven hos växter och träd, men också ankomst- och flyttningsdatum för flyttfåglar, jordbruksaktiviteter som när vårbruket börjar och när sådd och skörd börjar, samt några insekters aktiviteter. Datasetet är digitaliserat och tillhandahålls av Svenska fenologinätverket (SWE-NPN), ett nätverk med SLU som huvudman och flera universitet, myndigheter och intresseorganisationer som partners. Uppsala universitet och SMHI har varit samarbetspartners vid digitaliseringen, då de innehar originalblanketterna och har skannat in dessa till digitala kopior. För mer information om nätverket, se www.swe-npn.se. De fenologiska observationerna gjordes enligt den instruktion som SMCA hade satt upp (se uppladdade originalblanketter i dokumentationen). Observationerna gjordes regelbundet, med ca 1 veckas mellanrum eller vid behov, och datum när en viss fas inträffat noterades på blankett. Digitala kopior av originalblanketterna kan erhållas på förfrågan. Datasetet innehåller en observationsfil, observation_standalone.csv, som kan användas helt separat, utan hjälpfiler. Denna innehåller all nödvändig information för att använda data, t.ex. koordinater (latitud/longitud) till lokalen där observationen är gjord, artnamn (latinskt), fenologisk fas samt tidpunkt. Det finns också en observationsfil, observation.csv, där plats, förrättare, art och fenologisk fas är kodade. Denna behöver övriga hjälpfiler som finns i datasetet, för att kunna kopplas till observationsplatser, förrättarens namn artnamn och fasbeskrivning.

A multi-partner effort to record the locations of aquatic invasive species in waterbodies throughout the state. Points indicate presence and/or approximate physical locations of the species. Data is downloaded from EDDMapS Midwest (https://www.eddmaps.org/midwest/ ). Data from EDDMapS includes all data previously collected by the Minnesota DNR and uploaded to EDDMapS in 2016. From 2016 forward data from MN DNR staff was collected directly into EDDMapS. EDDMapS also contains data from other organizations and individuals in Minnesota.

Hourly observations for the last 24 hours for approximately 140 locations across the UK. It should be noted that not all sites provide all of the parameters that are shown below, where a parameter is not available no data is returned. Please see the tutorial for more information. 1.Wind direction (16 point compass) 2.Wind speed (mph) 3.Wind gust (mph) 4.Screen temperature (degrees celcius) 5.Weather Type ( code) 6.Visibility (m) 7.Pressure (hPa) Hourly observation reports as recorded in real time by the Met Office UK Monitoring System. It should be noted that sites will only report parameters based on the instrumentation installed at each site and we only make available those parameters published on the Met Office website. Observations are subject to final quality control by the Met Office after publication by DataPoint. The QC process can take up to six months to be completed and therefore any changes made will not be retrospectively applied to this dataset.

The differences between the observations and the forecast background used for the analysis (the innovations or O-F for short) and those between the observations and the final analysis (O-A) are by-products of any assimilation system and provide information about the quality of the analysis and the impact of the observations. Innovations have been traditionally used to diagnose observation, background and analysis errors at observation locations (Hollingsworth and Lonnberg 1989; Dee and da Silva 1999). At the most simplistic level, innovation variances can be used as an upper bound on background errors, which are, in turn, an upper bound on the analysis errors. With more processing (and the assumption of optimality), the O-F and O-A statistics can be used to estimate observation, background and analysis errors (Desroziers et al. 2005). They can also be used to estimate the systematic and random errors in the analysis fields. Unfortunately, such data are usually not readily available with reanalysis products. With MERRA, however, a gridded version of the observations and innovations used in the assimilation process is being made available. The dataset allows the user to conveniently perform investigations related to the observing system and to calculate error estimates. Da Silva (2011) provides an overview and analysis of these datasets for MERRA.

    The innovations may be thought of as the correction to the background required by a given instrument, while the analysis increment (A-F) is the consolidated correction once all instruments, observation errors, and background errors have been taken into consideration. The extent to which the O-F statistics for the various instruments are similar to the A-F statistics reflects the degree of homogeneity of the observing system as a whole. Using the joint probability density function (PDF) of innovations and analysis increments, da Silva (2011) introduces the concepts of the effective gain (by analogy with the Kalman gain) and the contextual bias. In brief, the effective gain for an observation is a measure of how much the assimilation system has drawn to that type of observation, while the contextual bias is a measure of the degree of agreement between a given observation type and all other observations assimilated.

    With MERRAs gridded observation and innovation data sets, a wealth of information is available for examination of the quality of the analyses and how the different observations impact the analyses and interact with each other. Such examinations can be conducted regionally or globally and should provide useful information for the next generation of reanalyses.