cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0169947.10fu
title: SH0169947.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0169948.10fu
title: SH0169948.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0169949.10fu
title: SH0169949.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0169950.10fu
title: SH0169950.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.5285/61b7df6e-4e27-460a-84a5-c100f0dc919f
title: Flower-insect timed count data from the UK Pollinator Monitoring Scheme, 2017-2020

description: This dataset contains information on the location of the count, some environmental variables, and the number of insects of each type counted for the Flower-Insect Timed (FIT) Count survey as part of the UK Pollinator Monitoring Scheme (PoMS). It covers the years 2017 to 2020 (note that 2017 was a pilot year and has less data than subsequent years).

The “public” FIT Count asks volunteer citizen scientists to count the number of insects, identified into broad taxon groups, seen landing on the flowers of a particular target plant within a 50 cm × 50 cm quadrat during a period of ten minutes.

The “1 km square” FIT Count uses the same methodology, but is carried out by PoMS volunteers and staff as part of the PoMS 1 km square survey, which takes place within a randomly allocated set of 1 km squares in England, Scotland and Wales, and also gathers data on pan-trapped insects.

The UK Pollinator Monitoring Scheme is a partnership between the UK Centre for Ecology & Hydrology, Bumblebee Conservation Trust, Butterfly Conservation, British Trust for Ornithology, Hymettus, Natural History Museum, University of Reading and University of Leeds, working with the Bees, Wasps and Ants Recording Society, wider stakeholders and volunteer networks. PoMS is jointly funded by Defra, the Welsh and Scottish Governments, JNCC and project partners.
description: Public FIT Counts are carried out at locations and dates chosen by the participants, at any time when weather conditions are suitable between 1 April and 30 September each year. A set of 14 target flower types is provided, but participants are free to choose a different flower if a target species is not available. Guidance is provided to participants on how to select a suitable location and position a quadrat, and on how to identify the insect group categories when counting visiting insects.

One km square FIT Counts follow the same methodology for the ten-minute count, but take place in up to 75 1 km squares, during four visits each summer, aiming for one visit in each month from May to August. There will be gaps in the data where a square cannot be visited during a particular year or month.

Data is entered using online forms that promote consistency in data formats. QA checks are carried out on any photos provided of the target flowers, and these are corrected where the original identification is not correct. Participants can optionally add photos to show examples of the insect groups that they have seen and counted, although these photos should not be taken during the count itself (so as to avoid disturbing visiting insects) so cannot be corrected in the same way.
DOI: 10.5285/ee9ab43d-a4fe-4e73-afd5-cd4fc4c82556
title: Gridded estimates of daily and monthly areal rainfall for the United Kingdom (1890-2017) [CEH-GEAR]

description: 1 km gridded estimates of daily and monthly rainfall for Great-Britain and Northern Ireland (together with approximately 3000 km2 of catchment in the Republic of Ireland) from 1890 to 2017. The rainfall estimates are derived from the Met Office national database of observed precipitation. To derive the estimates, monthly and daily (when complete month available) precipitation totals from the UK rain gauge network are used. The natural neighbour interpolation methodology, including a normalisation step based on average annual rainfall, was used to generate the daily and monthly estimates. The estimated rainfall on a given day refers to the rainfall amount precipitated in 24 hours between 9am on that day until 9am on the following day. The CEH-GEAR dataset has been developed according to the guidance provided in BS 7843-4:2012.
description: The rainfall estimates are derived from the Met Office national database of observed precipitations. To derive the monthly estimates, monthly and daily (when complete month availabe) precipitation totals from the UK raingauge network are used. An additional quality control step (data already quality controlled by the Met Office) was introduced to check events greater than the 200 year return period. The natural neighbour interpolation methodology, including a normalisation step based on average annual rainfall (SAAR61-90), was used to generate the daily and monthly rainfall grids. The daily grids are adjusted so that the monthly totals calculated from those are in agreement with the monthly grids. As an indicator of the quality of the estimates, the dataset also contains for each grid generated a grid containing the distance to the closest raingauge used to interpolate the grid point. A grid containing the number of missing estimates for each year is also provided. The CEH-GEAR dataset has been developed according to the guidance provided in BS 7843-4:2012.

This version of the CEH-GEAR dataset differs from the previous version (1890-2015) only by temporal coverage.  That is, previous years' data have not been modified.
DOI: 10.15156/bio/sh0169951.10fu
title: SH0169951.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.5285/7a5cfd3e-0247-4228-873d-5be563c4ee3b
title: Post Chernobyl surveys of radiocaesium in soil, vegetation, wildlife and fungi in Great Britain

description: Please note - this dataset is not current. For the most recent version, please search for the dataset titled 'Post Chernobyl surveys of radiocaesium in soil, vegetation, wildlife and fungi in Great Britain'. Data comprise radiocaesium concentrations in soil, vegetation, wildlife and fungi analysed from samples collected from throughout Great Britain after the 1986 Chernobyl accident by the Centre for Ecology & Hydrology (CEH), formerly the Institute of Terrestrial Ecology (ITE). National level vegetation surveys were conducted in May 1986, October 1986 and Spring 1987. More intensive surveys of vegetation (grass and heather) and wildlife (grouse, fox, etc.) in restricted areas were carried out in Cumbria, Wales and North Yorkshire in 1989, 1990, 1991 and 1993. Surveys of fungi were carried out between 1994 and 1997. The data are suitable for interpolation to create spatially variable surfaces suitable for input into models.
description: This dataset has been superseded by an updated version due to formatting requirements of the Data Journal Earth System Science Data (ESSD). Vegetation was clipped from 1 metre squared quadrats to a height of 1 centimetre above ground level. Samples were dried at 80 degrees centigrade, weighed, ground and counted in plastic containers (130 millilitre) on a NaI(T1) detector to determine the radiocaesium activity. The most active samples were recounted on a high resolution germanium detector. Soil type was noted at each site (Soil survey 1983). Soil samples were allocated to a series of textural categories (Clay (particles of less than 2 m in diameter); Silt (particles of 2-40 micron diameter); Sand (particles of 50 micron -2millimetre diameter); Organic (humified plant material); Loam (significant quantities of material from 3 or more categories). Soil pH was determined using an antimony electrode pH meter. Animal samples consisted of necks and rumen contents from 70 red deer (Cervus elaphus), whole bodies of three species of lagomorphs (25 rabbits (Oryctolagus cuniculus) and 2 black hares (Lepus capensis), two species of grouse (13 red grouse (Lagopus lagopus), 4 black game (Tetrao tetrix), and 22 foxes (Vulpes vulpes) totalling 141 flesh and 94 gut samples. Samples of flesh were removed from the neck of the deer, from the legs and back of the hares, rabbits and foxes, and from the breast muscle of the grouse. These were weighed fresh and placed in standard counting containers which were kept in a deep freeze until required for analysis. Each sample was thawed for 24 hours prior to counting. Samples were analysed by high resolution gamma spectrometry using either hyperpure germanium or germanium lithium detectors with relative efficiencies of 20 to 25 percent. Counting times varied between 25k to 80k seconds. Detectors were calibrated. The column headings for Easting, Northing and Sampling_date' in dataset 4 are labelled incorrectly. The correct order should be column C Sampling_date, column D Easting, column E Northing.
DOI: 10.5285/8ac670c7-17d8-433f-847c-170eeeb3ee47
title: Soil moisture, temperature and bulk density and earthworm abundance and biomass along transects from hedges into arable fields, pasture fields, ley strips established in arable fields and arable strips established in pasture fields at a farm in West Yorkshire.

description: Data are presented on earthworm abundance with distance from hedgerows was recorded for arable fields and pasture leys at Spens farm, the University of Leeds experiment farms, Yorkshire. Sampling was carried out annual from April 2015 to April 2017 with additional sampling in December 2015, and July and October 2016. Pits were excavated and soil hand sorted for earthworms. Mustard solution was then poured into the pit and any emerging earthworms collected. All earthworms were preserved in ethanol for identification using the Sims and Gerard Field studies key. At each pit the following measurements were also taken: soil moisture, soil temperature, soil bulk density. The samples were taken to determine the influence of leys on soil quality by Miranda Prendergast-Miller and colleagues as part of the SoilBioHedge project (Grant Reference NE/M017095/1) funded by the NERC Soil Security Programme. (Grant Reference NE/M017044/1).
description: Soil pits (18 x 18 x 15 cm) were excavated along transects and the soil hand sorted for earthworms. Mustard solution was then poured into the pit and any emerging earthworms collected.  All earthworms were preserved in ethanol for identification using the Sims and Gerard Field studies key. At each pit the following measurements were also taken: three soil moisture readings in millivolts (mV) were taken at both 5 and 10 cm depth using a ML3 ThetaProbe Soil Moisture Sensor, (Delta-T Devices); single soil temperature measurements at 5 and 10 cm depth using a Checktemp®1 probe (Hanna Instruments); soil bulk density samples were taken at 5 and 15 cm soil depth using steel bulk density rings (118 cm3), soil density was calculated on an oven dry basis. The data were entered into Excel spreadsheets and exported as comma separated value files (csv) for ingestion into the Environmental Information Data Centre.
DOI: 10.5285/a6012796-291c-4fd6-a7ef-6f6ed0a6cfa5
title: Daily and sub-daily hydrometeorological and soil data (2013-2017) [COSMOS-UK] 

description: This dataset contains daily and sub-daily hydrometeorological and soil observations from COSMOS-UK (cosmic-ray soil moisture)  monitoring network from October 2013 to the end of 2017. These data are from 46 sites across the UK recording a range of hydrometeorological and soil variables.

Each site in the network hosts a cosmic-ray sensing probe; a novel sensor technology which can be used, in combination with hydrometeorological data, to calculate the volumetric water content of soil over a field scale. The hydrometeorological and soil data are recorded at a 30 minute resolution and they include neutron counts from the Cosmic-ray sensing probe, humidity and atmospheric pressure data that are used to derive volumetric water content at two temporal resolutions (hourly and daily). Also included are soil heat flux, air temperature, wind speed and radiation data which are used to derive potential evapotranspiration at a daily resolution.

This work was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability.
description: Hydrometeorological and soil observations are recorded at sites around the UK using an array of sensors. Every hour, the raw data collected is sent to CEH in Wallingford, where it is subject to quality control. Soil volumetric water content data are derived from 30 minute hydrometeorological data and data from the cosmic-ray sensing probe by Python scripts run every hour. The data are calculated using the method outlined in Evans et al. (2016, https://doi.org/10.1002/hyp.10929). Potential evapotranspiration data are derived by Python scripts at the end of the day from the hydrometeorological data according to the Penman-Monteith method (FAO 56, http://www.fao.org/3/X0490E/x0490e00.htm). 

All data, that is raw, quality controlled and derived, is saved in an Oracle database.

See supporting documentation for further information.
DOI: 10.15156/bio/sh0169952.10fu
title: SH0169952.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.5285/37702a54-b7a4-40ff-b62e-d14b161b69ca
title: Daily and sub-daily hydrometeorological and soil data (2013-2018) [COSMOS-UK]

description: This dataset contains daily and sub-daily hydrometeorological and soil observations from COSMOS-UK (cosmic-ray soil moisture) monitoring network from October 2013 to the end of 2018. These data are from 50 sites across the UK recording a range of hydrometeorological and soil variables.

Each site in the network records the following hydrometeorological and soil data at 30 minute resolution: Radiation (short wave, long wave and net), precipitation, atmospheric pressure, air temperature, wind speed and direction, humidity, soil heat flux, and soil temperature and volumetric water content (VWC), measured by point senors at various depths.

Each site hosts a cosmic-ray sensing probe; a novel sensor technology which counts fast neutrons in the surrounding atmosphere. In combination with the recorded hydrometeorological data, neutron counts are used to derive VWC over a field scale (COSMOS VWC), at two temporal resolutions (hourly and daily).
The presence of snow leads to erroneously high measurements of COSMOS VWC due to all the extra water in the surrounding area. Included in the daily data are indications of snow days, on which, the COSMOS VWC are adjusted and the snow water equivalent (SWE) is given.

The potential evapotranspiration (PE), derived from recorded hydrometeorological and soil are also included at daily resolution.

Two levels of quality control are carried out, firstly data is run through a series of automated checks, such as range tests and spike tests, and then all data is manually inspected each week where any other faults are picked up, including sensor faults or connection issues. Quality control flags are provided for all recorded (30 minute) data, indicating the reason for any missing data.
description: Hydrometeorological and soil observations are recorded at sites around the UK using an array of sensors. Every hour, the raw data collected is sent to CEH in Wallingford, where it is subject to quality control. Soil volumetric water content data are derived from 30 minute hydrometeorological data and data from the cosmic-ray sensing probe by Python scripts run every hour. The data are calculated using the method outlined in Evans et al. (2016, https://doi.org/10.1002/hyp.10929). Snow water equivalent are derived by Python scripts at the end of the day from the hydrometeorological data according to Wallbank et al. (2020, https://ui.adsabs.harvard.edu/abs/2019EGUGA..2116420W/abstract). Potential evapotranspiration data are derived by Python scripts at the end of the day from the hydrometeorological data according to the Penman-Monteith method (FAO 56, http://www.fao.org/3/X0490E/x0490e00.htm).

All data, that is raw, quality controlled and derived, is saved in an Oracle database.

See supporting documentation for further information.
DOI: 10.15156/bio/sh0169953.10fu
title: SH0169953.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.