cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5285/dae416b4-3762-45bd-ae14-c554883d482c
title: Leaf phenology synchrony for Meso- and South America v2

description: The leaf phenology product presented here shows the amplitude of annual cycles observed in MODIS (Moderate Resolution Imaging Spectroradiometer) normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) 16-day time-series of 2000 to 2013 for Meso- and South America. The values given represent a conservative measure of the amplitude after the annual cycle was identified and tested for significance by means of the Lomb-Scargle Transform. The amplitude was derived for four sets of vegtation indices (VI) time-series based on the MODIS VI products (500m MOD13A1; 1000m MOD13A2). The amplitude value can be interpreted as the degree in which the life cycles of individual leaves of plants observed within a pixel are synchronised. In other words, given the local variation in environment and climate and the diversity of species leaf life cycle strategies, an image pixel will represent vegetation communities behaving between two extremes: 
* well synchronized, where the leaf bud burst and senescence of the individual plants within the pixel occurs near simultaneously, yielding a high amplitude value. Often this matches with an area of low species diversity (e.g. arable land) or with areas where the growth of all plants is controlled by the same driver (e.g. precipitation).
* poorly synchronized, where the leaf bud burst and senescence of individual plants within a pixel occurs at different times of the year, yielding a low amplitude value. Often this matches with an area of high species diversity and/or where several drivers could be controlling growth.
description: The key underlying method is the Lomb-Scargle (LS) a well established and efficient methodology to analyse time series data. The LS can be used for the detection of regular cycles, even when hidden in a noisy background and when the time series in question contains data gaps. The LS estimates a power spectrum that indicates the average variance in a time series at different time scales or frequencies. Standard linear de-trending of the data before application of the LS allows an analysis of a 'stationary' time series as the mean and variance should ideally be more-or-less constant.

We used two approaches to evaluate the amplitude values. The first involved comparing the full extent of the amplitude data grids with the independent International Geosphere-Biosphere Programme (IGBP) land cover map. This was done with histograms showing the distribution of the amplitude values found within each of the IGBP cover classes. The second approach compared published in situ site observations that describe canopy leaf life cycles through leaf flushing and falling, LAI and litter fall or other observations, with the corresponding amplitude values of these sites.
DOI: 10.5285/13aed7ac-334f-4bb7-b476-4f1c3da45a13
title: Flower-insect timed count data from the UK Pollinator Monitoring Scheme, 2017-2020 version 2

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 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). This is version 2 of the dataset; the previous version contained some duplicate data rows, which have been deduplicated in version 2.

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 (see separate dataset).

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/fc9423d6-3d54-467f-bb2b-fc7357a3941f
title: Gridded estimates of hourly areal rainfall for Great Britain 1990-2016 [CEH-GEAR1hr] v2

description: The dataset contains 1km gridded estimates of hourly rainfall for Great Britain for the period 1990-2016. The estimates are derived by applying the nearest neighbour interpolation method to a national database of hourly raingauge observations collated by Newcastle University and the UK Centre for Ecology & Hydrology (UKCEH). These interpolated hourly estimates were then used to temporally disaggregate the CEH-GEAR daily rainfall dataset. The estimated rainfall on a given hour refers to the rainfall amount accumulated in the previous hour.

The dataset also contains data indicating the distance between the grid point and the closest recording raingauge used in its interpolation. When this distance is greater than 50km, or there is zero rainfall recorded in the closest gauge, the daily value is disaggregated using a design storm. The dataset therefore also contains a flag indicating if the design storm was used. These data are provided as an indicator of the quality of the estimates.
description: The hourly rainfall estimates are derived from the temporal disaggregation of the CEH-GEAR daily data using hourly gauge data. This methodology ensures that the daily totals of the CEH-GEAR daily and 1hr datasets are consistent. The hourly gauge data is from the Met Office, the England Environment Agency (EA), Natural Resources Wales (NRW) and the Scottish Environmental Protection Agency (SEPA). The CEH-GEAR daily data is derived from the Met Office national database of observed precipitation. The nearest neighbour interpolation methodology was used to generate the gridded hourly estimates which were subsequently used to disaggregate the daily data.

A quality control protocol was established in order to identify and reject erroneous hourly values in the gauge rainfall input dataset. The quality control procedure is a three step process:
1) The gauge data is compared to the gridded CEH-GEAR daily dataset to identify suspect gauges
2) A series of quality control tests are applied to identify suspect values at all gauges which are marked with a quality control flag
3) Combinations of quality control flags for a given hourly accumulation are used to determine which flagged data are treated as erroneous values and thus excluded from the gauge records.
DOI: 10.5285/2c43ba3c-d821-442c-989b-754451d72091
title: Pan-trap survey data from the UK Pollinator Monitoring Scheme, 2017-2020 version 2

description: This dataset contains samples and occurrences of insects and flowers collected for the 1 km square survey 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). This is version 2 of the dataset; the previous version contained some incorrect values for the summed totals of counted insect specimens, which have been corrected in this version.

This survey uses pan-traps positioned on transects within a randomly allocated set of 1 km squares in England, Scotland and Wales. The sample data contains information on the location of the traps, various environmental variables, and the total number of insect specimens collected. The occurrence data for insects contains records of all hoverflies and bees from the traps, identified (wherever possible) to species level. The occurrence data for flowers contains records of all forbs that are in flower within 2 metres of each pan-tap station. (In addition, Flower-Insect Timed Counts are carried out in the same 1 km squares, see separate dataset for these data.)

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 in 2017-2020 has been jointly funded by Defra, the Welsh and Scottish Governments, JNCC and project partners.
description: Data is collected by PoMS volunteers and staff from 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. At each visit, five pan-traps are positioned in a transect across the 1 km square, and left in position for approx. six hours. The insects trapped are returned to the PoMS laboratory, where all insects are sorted into main taxonomic groupings and counted, and all bees and hoverflies are passed to taxonomic experts for identification to species level where possible.

Data is entered using online forms that promote consistency in data formats. QA checks are carried out on any photos provided that show plants in flower in the vicinity of the pan-traps; bee and hoverfly identifications are cross-checked by multiple taxonomists for all rare and apparently out-of-range occurrences, plus a random sample of other occurrences.
DOI: 10.5285/23f194b0-1e21-4f14-be9c-6eabdcc1feb7
title: Macroinvertebrate taxonomic abundance, water quality, river flow, air temperature and environmental site descriptors from English rivers, 1965-2018, version 2.0

description: This data product combines macroinvertebrate taxonomic abundance for 1,519 monitoring sites across English rivers for the period between 1965 and 2018, with matched concentrations of 41 water quality determinands, river flow exceedance statistics, and air temperature values. It also includes site variables such as sewage effluent exposure, habitat quality, land cover in the upstream catchment, and other physical parameters measured at the sampling point, such as altitude, slope, distance from source, width and depth of the channel, and type of substrate.

Developed as part of the ChemPop (2018-2023), a NERC-funded project investigating the impact of chemical exposure on wildlife populations in rivers, this research output is an open data product for use in a broad spectrum of environmental data and modelling analyses.

description: This data product was created by preprocessing and combining survey data of national extent collected by the Environment Agency (EA), such as freshwater macroinvertebrate survey data, water quality data, habitat quality metrics, and river flow; as well as datasets produced by UKCEH, such as the Land Cover Map 2015, CHESS-met air temperature, and estimated sewage effluent exposure.

The spatial distribution of this product matches the spatial extent of the macroinvertebrate data. Its time stamps correspond to the dates of macroinvertebrate sampling. The completeness of this product is determined by the spatio-temporal availability of the data sources.

Due to the different structures and file formats of the data sources, as well as the varied range of technical skills within our team, a range of file manipulation tools were used. However, the same data processing and data integration methodology was followed in all instances. This involved three steps: first, spatial matching of sampled locations along the river network; second, spatial integration of model-generated variables, including wastewater effluent, land cover, and air temperature; and third, temporal matching of environmental observations, such as water quality concentrations and river flow, along with modelled variables such as air temperature.
DOI: 10.5285/673b2b31-562c-4604-8776-4a92988867b8
title: Campanula rotundifolia cytotype, distribution and first flowering dates v2

description: Data were collected to determine the geographic distribution of different cytotypes of Campanula rotundifolia L. Most sampling concentrated on Britain and Ireland, but samples from mainland Europe, the Russian Federation and North America were also analysed. Following these observations, a common garden study of tetraploid, pentaploid and hexaploid cytotypes representative of Britain and Ireland was set up at a CEH Edinburgh (where the local cytotype is tetraploid), to determine whether climatic factors were limiting the distribution of the hexaploid cytotype through effects on growth, survival or flowering phenology.
The geographic distribution study ran from 2006-2019. The common garden study ran from 2008-2010. 

description: For Britain and Ireland, field collection were guided by use of Botanical Society of Britain and Ireland  distribution maps and an understanding of habitat preferences. Cytotypes were determined by flow cytometry and supported by chromosome counts. Data were partitioned into diploid, tetraploid, pentaploid and hexaploid, and intermediate aneuploids. All samples were run with internal reference standards, and analyses were re-run or discarded if the cv > 5%. In the common garden study shoot dry weights and numbers of flowers, flower buds, seed heads and flowering stems were determined. Flowering phenology was assessed by observation of first flowering date, when the first flower was sufficiently open to be entered by bees.
DOI: 10.5285/efa10c00-d43a-4c94-b2f9-0708755e7d80
title: Wood specific gravity for trees in Ankeniheny Zahamena forest corridor, Madagascar v2

description: Data comprise wood specific gravity (WSG) and density at 12% moisture content (D12) in wood cores sampled from trees in the Ankeniheny Zahamena forest corridor, the remains of the evergreen forest of eastern Madagascar. The data also include date of sample collection, zone identifier, core identifier, local and scientific tree name, tree height and diameter. Samples were analysed by UFR Sciences du bois de l'ESSA-forêts, Université d'Antananarivo.

Data were collected as part of a project funded under the Ecosystem Services for Poverty Alleviation (ESPA) programme. Work package 4, P4GES project, grant references: NE/K008692/1, NE/K010115/1, and NE/K010220/1.
description: In the field.
Following discussion with local botanist (to find trees that grow locally and to check that local tree names matched scientific tree names) healthy trees, i.e. trees that were neither hollow nor suffering from decay inside the trunk were selected.
15 mm-diameter wood cores were collected using an electric drill powered by a generator (to avoid harvesting trees). Samples were taken at 1.3 m above ground.
In the lab.
To determine the wood specific gravity (WSG) and density at 12% moisture content in the wood cores, a one-centimetre long segment (without bark) from each core was cut. Segments were stabilised in a climatic chamber at 20°C and 65% relative humidity for 7 days to obtain 12% moisture content (MC). Samples were considered stable at 12% moisture content when the weights were constant for 3 consecutive days.
Full materials and methods available from Supporting Information (Manual_4_Wood_Specific_Gravity).
DOI: 10.5285/76057b0a-b18f-496f-891c-d5b22bd0b291
title: Grid-to-Grid model estimates of river flow for Northern Ireland driven by UK Climate Projections 2018 (UKCP18) Regional (12km) data (1980 to 2080) v2

description: Gridded hydrological model river flow estimates on a 1km grid over Northern Ireland for the period Dec 1980 - Nov 2080. The dataset includes monthly mean river flow, annual maxima of daily mean river flow (water years Oct - Sept), along with the date of occurrence, and annual minima of 7-day mean river flow (years spanning Dec-Nov), along with the date of occurrence (units: m3/s). The data are provided in gridded netCDF files.

There is one file for each variable and ensemble member. To aid interpretation, two additional spatial datasets are provided: a) digitally-derived catchment areas and b) estimated locations of flow gauging stations both on the 1km x 1km grid and c) a 1km x 1km grid identifying majority lake cells.

The data were produced as part of UK-SCAPE (UK Status, Change And Projections of the Environment, Work Package 2: Case Study - Water) a NERC-funded National Capability Science Single Centre award.
description: This dataset is a model output from the Grid-to-Grid hydrological model (Bell et al. 2009; https://doi.org10.1016/j.jhydrol.2009.08.031) driven by meteorological data derived from the United Kingdom Climate Projections 2018 (UKCP18) Regional (12km) projections (Met Office Hadley Centre 2018, catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604). The projections comprise a 12-member perturbed parameter ensemble (PPE) of the Met Office Hadley Centre Regional Climate Model (RCM) under representative concentration pathway (RCP) scenario RCP8.5. The meteorological variables used were daily precipitation, daily min and max temperature, and daily PE derived from other meteorological variables (using the Penman-Monteith scheme). See Supporting Information for more details.
DOI: 10.5285/6077cd88-ad40-44bd-806a-fa3cebaa29d7
title: UK Environmental Change Network (ECN) Cairngorm spider data 2004-2021 v2

description: Data was collected to look at long-term trends in invertebrate ground predators.  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 2021.

Spiders were collected in pitfall traps on a two-weekly basis between March and early November.  Each habitat contained ten 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 (2004-2021).
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: 10.5285/17b51437-0231-4eac-a176-1277185ba2e9
title: Carbon concentrations in natural and restoration pools in blanket peatlands in Scotland, 2013-2015 v2

description: Dataset contains water chemistry data (pH, conductivity, dissolved carbon, dissolved gases, absorbance) from natural and artificial/restoration peat pools in Scottish peatlands. Water samples were collected seasonally from three sites, and more frequently (up to weekly) from one site, in within the Flow Country.  
description: Data were collected quarterly and fortnightly between 2013 and 2015 from peatland pools in Scotland. Data were quality checked (detection limits of equipment) and analysed between 2018 and 2021.
DOI: 10.5285/a48c07c0-ae32-4df1-8e32-5eec44c683b3
title: Peatland stream water chemistry data from the MERLIN project, Hare Moss, Scotland, 2022-2023 v2

description: This dataset comprises water chemistry data (dissolved organic carbon (DOC), total dissolved nitrogen (TdN), particulate organic carbon (POC), total phosphorus (TP), soluble reactive phosphorus (SRP), absorbance at 254nm (Abs 254), specific ultraviolet absorbance (SUVA), and dissolved CH4, CO2, N2O) collected bi-weekly from peatland streams near Penicuik, Scotland, in 2022 and 2023. Data were obtained via collection of water samples in situ, and concentrations were derived via subsequent sample processing and analysis.
The objective of this dataset is to provide background data to characterise differences in the baseline period before restoration at the sampling site occurs.

description: Fortnightly sampling took place at two locations from December 2021 to May 2022. An additional two locations were sampled from May 2022 to August 2022. One of the original sites was discontinued in August 2022 and the remaining three locations continued to be sampled until December 2023.
Samples were collected directly from each stream using a 60ml syringe and were either dispensed directly unfiltered or, filtered with a cellulose acetate filter into their relevant containers before being returned to the lab and either frozen or refrigerated before analysis.
Dissolved greenhouse gas samples were collected using the headspace method. This involves filling a 60ml syringe with 30ml of stream water and 30ml of ambient air and agitating it for one minute until equilibration of gases has occurred. The air sample is then injected into a sealed vial and returned to the lab for analysis.
All data were generated by the authors. During the generation of water chemistry data, calibration standards of known concentrations were used for quality control of instrument outputs.