cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5285/422adbc3-306e-437a-9fe1-c7b53a374f87
title: Occurrence data at 10km resolution for terrestrial mammals in Great Britain and Northern Ireland, 1960-1992 and 2000-2016

description: This dataset contains 10km summarised occurrence data for terrestrial mammals as mapped in the Atlas of Mammal of Great Britain and Northern Ireland published by the Mammal Society in 2020. For each species 10km grid squares were categorised based on whether the species was recorded in that square only the current atlas time period (2000-2016), only the previous atlas time period (1960-1992) or in both time periods. The dataset contains data for all species mapped in the atlas, with the exception of cetaceans which are not included in the dataset.
description: This dataset is a summarised product of the production of the Mammal Atlas and represents the 10km resolution data plotted in the species maps in the atlas.

The underlying data used in the production of the atlas to determine the summarised distributions came from a wide number of sources, such as the iRecord, National Biodiversity Network (NBN) gateway (now the NBN atlas), local biological records centres, national and local monitoring schemes. Data was provided to the Mammal Society from these sources for the purpose of producing the mammal atlas. Data from iRecord and the Mammal Society’s online recording tools & mammal tracker app were verified and validated by Mammal Society verifiers. The majority of other data had undergone verification and validation prior to being provided to the Mammal Society but the level and extent of QA differed between sources and the QA processes were often not specified in detail.

The majority of data is opportunistic sightings data, however the dataset does also contain data from structured and or professional surveys.
DOI: 10.5285/4fc8a62b-c971-433e-acc3-23423bb75022
title: Carbon and nitrogen contents of soil organic matter fractions, soil organic carbon, total soil nitrogen, and soil organic matter of 100 topsoil samples from the UKCEH Countryside Survey, Great Britain, 2019-2020

description: Carbon and nitrogen content (g per g soil) of the free-light fraction (LF), the occluded particulate fraction (OF), the mineral associated organic matter fraction (MAOM), the particulate organic matter (POM = LF+OF), soil organic carbon (SOC), total soil nitrogen (TN) and soil organic matter (SOM from loss-on-ignition) of 100 topsoil samples (0-15 cm) from the UKCEH Countryside Survey 2019/20 are presented.


description: Archived Countryside survey topsoil samples (0-15 cm) were used. These were sieved (2 mm) and airdried prior to archiving.
Thermo-Gravimetric Analysis was performed in four steps using a LECO TGA701. 10 g of samples were heated from 25oC to 105oC (kept constant for 3 h) to determine the hygroscopic moisture. The mass loss between 105oC and 375oC (kept constant for 15.75 h) was used to calculate the soil organic matter content (SOM), the mass loss between 650oC and 100oC gives information on calcium carbonate content of the soils.
Soil organic matter fractions were separated via density fractionation using sodium polytungstene at 1.8 g cm-3). In the first step, the free light fraction (LF-fraction) was separated using 2.5 g archived soil. The pellet was re-suspended, and the occluded fraction was separated and discarded. The residual material was associated with the mineral associated organic matter fraction (MAOM fraction) after re-suspension and cleaning. Two internal standards were used in each batch as well as 10% of replicated samples to account for accuracy and precision.
Carbon and nitrogen contents were measured on a subsample of archived soil, and the LF and MAOM fractions using an Elementar Vario-Elemental analyser (Elementaranalysensysteme GmbH, Hanau, Germany). The UKAS accredited method SOP3102 was used.
Carbon and nitrogen mass fractions were calculated for the LF and MAOM fractions, and the soil. Mass fractions of the occluded fraction (OF) and the particulate organic matter fraction (POM) were calculated using the mass balance equation.
Total soil carbon and MAOM-C are corrected for calcite-C content; LF, OF and POM fractions are not corrected for calcite content because no minerals are expected to be present in organic matter.
LF, MAOM, total soil, and SOC (total soil carbon minus calciate-carbon) were corrected for hygroscopic water content. The OF and POM fractions are automatically corrected for hygroscopic water as they are calculated from corrected masses.
DOI: 10.5285/53251b3c-6c79-4aeb-a0de-fc63b9350cc1
title: The Isle of May long-term study (IMLOTS) seabird annual return rate 1988-2016

description: This dataset contains calculated return rates for five seabird species from representative colonies on the Isle of May, off the East coast of Scotland. Annual return rates are measured as the number of individually colour marked individuals seen in any one year that were also observed in the previous year for the Atlantic puffin (Fratercula arctica), common guillemot (Uria aalge), razorbill (Alca torda), European shag (Phalacrocorax aristotelis) and black-legged kittiwake (Rissa tridactyla). Not every individual is seen in any one year and the data set does not take into account those missed in any previous years hence these data are not to be treated as survival estimates.

The Isle of May long-term study (IMLOTS) aims to identify the impact of environmental change on seabirds and their associated ecosystems. Understanding the mechanisms underlying variation in seabird population size requires a thorough knowledge of demographic parameters, namely birth rates, death rates, immigration and emigration. The effects of environmental change are likely to be different according to which demographic parameter or life history stage is being considered. This complexity means that only long-term monitoring, such as that carried out on the Isle of May, will allow us to understand the functioning of bird populations and their responses to environmental change.    
description: Data collected by CEH staff. All fieldwork, sampling and data handling was carried out by experienced and trained staff to defined protocols agreed to meet the objectives of the work. All data was collected in the field in notebooks and field data sheets.  These were transcribed to spreadsheets after each check was made and files backed up to CEH directories. All notebooks and fieldsheets are archived at CEH Edinburgh.

The Isle of May is one of four key UK seabird monitoring sites supported by the JNCC (Joint Nature Conservation Committee). CEH has an on-going contract with JNCC to collect data on seabird demography and diet. The data, including that on annual breeding success, feed into the national Seabird Monitoring Programme network.
DOI: 10.5285/beb62085-ba81-480c-9ed0-2d31c27ff196
title: Potential evapotranspiration derived from HadUK-Grid 1km gridded climate observations 1969-2022 (Hydro-PE HadUK-Grid)

description: Gridded potential evapotranspiration calculated from HadUK-Grid gridded observed meteorological data at 1 km resolution over the United Kingdom for the years 1969-2022. This dataset contains two potential evapotranspiration variables: daily total potential evapotranspiration (PET; kg m-2 d-1) and daily total potential evapotranspiration with interception correction (PETI; kg m-2 d-1). The units kg m-2 d-1 are equivalent to mm d-1. The data are provided in gridded netCDF files. There is one file for each variable, for each calendar month.
description: This dataset was derived from the HadUK-Grid meteorological dataset v1.0.3.0 on a 1km grid over the UK for 1969-2020 inclusive, v1.1.0.0 for 2021, and v1.2.0 for 2022. (https://catalogue.ceda.ac.uk/uuid/4dc8450d889a491ebb20e724debe2dfb). HadUK-Grid is a gridded dataset derived from observed meteorological station data. The daily total potential evapotranspiration (PET; kg m-2 d-1) was calculated using the Penman-Monteith equation parameterised for a well-watered grass surface. The daily total potential evapotranspiration with interception correction (PETI; kg m-2 d-1), was calculated as PET then a correction added for interception by a grass canopy on days with non-zero precipitation. The HadUK-Grid meteorological variables used were: daily maximum and minimum air temperature at 1.5 m (°C), daily precipitation (mm d-1), monthly mean water vapour pressure (hPa), monthly mean sea level pressure (hPa), monthly total sunshine hours (hours) and monthly mean wind speed at 10 m (m s-1). Other variables used were the surface elevation (m) and Ångström coefficients a, b and c. A description of the methodology is available in the supporting documentation.
DOI: 10.5285/d251b39a-a5be-4193-89dc-6a7be9059d42
title: UK Environmental Change Network (ECN) Cairngorm spider data 2004-2019

description: This dataset consists of count data (by gender) for all species of spider collected from three habitats (mire, dwarf-shrub heath, pine woodland) at the Cairngorms Environmental Change Network (ECN) site between 2004 and 2019. Spiders were collected in pitfall traps on a two-weekly basis between March and early November.  Each habitat contained 10 pitfall traps, spaced 10 m apart.  Samples were aggregated by habitat and collection date prior to analysis.  The number of male and females of each species was recorded by the same expert araneologist for the duration (2004-2019). Data was collected to look at long-term trends in invertebrate ground predators.  

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: Data was collected and processed using ECN standard methodology (available from the ECN website).  

Sampling data was crosschecked with returned species data to ensure trapping dates and locations were correct within the dataset.  Additional data was added to clearly label transect age (see supporting documentation).  

The data was manually checked for errors but has not been through an exhaustive QC process which is recommended as good practice prior to use.
DOI: 10.15156/bio/sh0169970.10fu
title: SH0169970.10FU

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/e4c300b1-8bc3-4df2-b23a-e72e67eef2fd
title: Weekly water quality data from the River Thames and its major tributaries (2009-2013) [CEH Thames Initiative]

description: This data set comprises of weekly water quality monitoring data of seven sites along the River Thames, UK, and fifteen of its major tributaries from February 2009 to February 2013.  Parameters measured were phosphorus and nitrogen species, dissolved reactive silicon, water temperature, pH, Gran alkalinity, suspended solids, chlorophyll and major dissolved anions (fluoride, chloride, bromide, sulphate) and cations (sodium, potassium, calcium, magnesium, boron).  Dissolved and total iron, manganese, zinc, copper concentrations have also been produced from August 2010 to February 2013.  The accompanying daily river flow data are also supplied.  

Samples were taken as part of the Centre for Ecology & Hydrology's Thames Initiative monitoring programme.
description: All water samples were collected by fieldworkers from the Centre for Ecology & Hydrology (CEH), Wallingford.  All chemical analysis was carried out by the Nutrient Chemistry Laboratories at CEH Wallingford.  Daily mean flow data that accompany the data set were produced by the Environment Agency, and downloaded from the National River Flow Archive in January 2017. 

Interpretation of this data set can be found in the following papers:

 [1] Bowes MJ, Gozzard E, Johnson AC, Scarlett PM, Roberts C, Read DS, et al. Spatial and temporal changes in chlorophyll-a concentrations in the River Thames basin, UK: Are phosphorus concentrations beginning to limit phytoplankton biomass? Sci Total Environ (2012) 426: 45-55.
 [2] Bowes MJ, Jarvie HP, Naden PS, Old GH, Scarlett PM, Roberts C, et al. Identifying priorities for nutrient mitigation using river concentration-flow relationships: The Thames basin, UK. J of Hydrol (2014) 517: 1-12. 
[3] Bowes MJ, Loewenthal M, Read DS, Hutchins MG, Prudhomme C, Armstrong LK, et al. Identifying multiple stressor controls on phytoplankton dynamics in the River Thames (UK) using high-frequency water quality data. Sci Total Environ (2016) 569-570: 1489-1499.
DOI: 10.15156/bio/sh0169971.10fu
title: SH0169971.10FU

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: 10.5285/5060cc27-0b5b-471b-86eb-71f96da0c80f
title: Daily and sub-daily hydrometeorological and soil data (2013-2022) [COSMOS-UK]

description: This dataset contains daily and sub-daily hydrometeorological and soil moisture observations from COSMOS-UK (cosmic-ray soil moisture) monitoring network from October 2013 to the end of 2022. These data are from 51 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 sensors 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.

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 UKCEH 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. Snow water equivalents are derived by Python scripts at the end of the day from the hydrometeorological data according to Wallbank et al., 2020. 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 Crop evapotranspiration - Guidelines for computing crop water requirements - FAO Irrigation and drainage paper 56). All data (including raw, quality controlled and derived) is saved in an Oracle database.
DOI: 10.15468/dl.rn5xk4
title: Occurrence Download

description: A dataset containing 1470 records in GBIF matching the query:

 SELECT gbifid, datasetkey, specieskey, kingdom, phylum, class, "order", family, genus, species, scientificname, decimallatitude, decimallongitude, eventdate, institutioncode, "year" AS obs_year, basisofrecord, coordinateuncertaintyinmeters, issue
 FROM occurrence
 WHERE
 decimallatitude IS NOT NULL
 AND decimallongitude IS NOT NULL
 AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID')
 AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH')
 AND "year" &gt;= 2015
 AND "year" &lt;= 2026
 AND coordinateuncertaintyinmeters IS NOT NULL
 AND coordinateuncertaintyinmeters &gt; 0
 AND coordinateuncertaintyinmeters &lt;= 200.0
 AND decimallatitude &gt;= 61.2993700571118
 AND decimallatitude &lt;= 66.79314043790278
 AND decimallongitude &gt;= -71.84031383705663
 AND decimallongitude &lt;= -55.37297038330212
 AND GBIF_Within('POLYGON ((-55.372970 61.299370, -55.372970 66.793140, -71.840314 66.793140, -71.840314 61.299370, -55.372970 61.299370))', decimallatitude, decimallongitude) = TRUE
 

 The dataset's 1470 records were derived from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005618-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c36qdj
title: Occurrence Download

description: A dataset containing 11514 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Magnolia virginiana"
 ]
}

 The dataset includes 11514 records from 235 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005636-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14280884
title: MopoHeatSectorDataProcessing input data

description: # MopoHeatSectorDataProcessing/input-data


Input data required by

[MopoHeatSectorDataProcessing](https://github.com/spine-tools/MopoHeatSectorDataProcessing).


Please refer to the following subsections for more detailed information on the exact

data sources and their licenses.


## `abm-raw-data`


Raw [ArchetypeBuildingModel](https://github.com/vttresearch/ArchetypeBuildingModel)

output from the [Mopo AmBIENCE2ABM Demo](https://zenodo.org/records/10518294) workflow.

Licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode).


## `dea-technology-catalogues`


Relevant

[Danish Energy Agency technology catalogue datasets](https://ens.dk/en/our-services/technology-catalogues),

licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode).


## `ENER-C2-2014-614`


Relevant results from the

[Mapping and analyses of the current and future (2020 - 2030) heating/cooling fuel deployment (fossil/renewables)](https://energy.ec.europa.eu/publications/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment-fossilrenewables-1_en)

study, published by the Directorate-General for Energy of the European Commission.

Unfortunately, the license isn't crystal clear, but it is clearly intended for publication.


## `eurostat`


[Cooling and heating degree days by country - annual data](https://data.europa.eu/data/datasets/20yo9cdnqch1ilu65ktsig?locale=en).

Licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode).


## `JRC-IDEES-2021`


[The JRC-IDEES 2021 dataset](https://joint-research-centre.ec.europa.eu/scientific-tools-databases/potencia-policy-oriented-tool-energy-and-climate-change-impact-assessment-0/jrc-idees_en).

Licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode).


## `scen_ambitious_building_demand`


The corresponding

[Hotmaps project GitLab repository](https://gitlab.com/hotmaps/scen_ambitious_building_demand).

Licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode).


## `scen_current_building_demand`


The corresponding

[Hotmaps project GitLab repository](https://gitlab.com/hotmaps/scen_current_building_demand).

Licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode).


## `when2heat`


A part of the

[PECD consistent heat demand and COP time series from when2heat](https://doi.org/10.5281/zenodo.13962245)

dataset.

Licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode).