cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5285/d69e854a-f01d-4d5e-8819-219053e8d00c
title: Soil data from the South Fork McKenzie River in Oregon, USA before and after a wildfire event, 2020 to 2022

description: Soil data comprises sample depth, moisture content, % sand/silt/clay, texture, and various nitrate/nitrite/carbon metrics. These data were collected from the South Fork McKenzie River, Oregon, USA in July 2020, February 2021 and June 2021 following the Holiday Farm wildfire in Autumn 2020. Samples were collected from a restored and unrestored reach of the South Fork McKenzie River with a view to quantifying differences in soil response to wildfire in the restored vs. unrestored river reaches.
The study was conducted by the University of Nottingham, with data collected by partners from The US Forest Service, Portland State University, Washington State University and Colorado State University. Funding for the work was received from the Natural Environment Research Council grant NE/V021443/1.

description: Soil data collected at a range of transects across the South Fork McKenzie River (and Horse Creek reference site) using a T-bar sampling probe at three depths (0-30 cm, 30-60 cm, 60-90 cm). Soil samples subsequently processed by Ward Lab (www.wardlab.com) for carbon, nitrogen and grain size analysis.
DOI: 10.5285/4a08e6f1-e508-4bb6-b571-b3145dd1588e
title: Gridded rainfall depth-duration-frequency (DDF) estimates for the state of Kerala, India, 1979-2020

description: This data represents twenty-four modelled rainfall depth estimates by GridASCII files across the state of Kerala, India, for four durations (1, 6, 24 and 192 hours) and six return periods (2, 5, 10, 25, 50 and 100 years). The estimates were produced using a similar procedure to the Flood Estimation Handbook statistical method for flood frequency estimation: separately for each duration, the estimated median annual maximum (AMAX) rainfall was used as a standardizing “index” value and the estimated L-moments of the AMAX series were used to fit a generalized logistic distribution “growth curve”. The data are in units of mm at a spatial resolution of 0.12 degrees.
description: 1, 6, 24 and 192-hour annual maximum rainfalls were extracted from the gridded Indian Monsoon Data Assimilation and Analysis (IMDAA) hourly rainfall dataset for each year of 1979-2020 inclusive, for each grid cell in the state of Kerala, India. At each point, the median, and L-moments of the 42 annual maxima were calculated separately for each duration. At each location, linear regressions were created for the median using longitude, latitude, duration and mean annual rainfall. Linear regressions were created for the first two L-moments using these descriptors variables plus altitude and modelled median of annual maxima. The generalized logistic distribution was then used with the modelled median and L-moment ratios at each site to estimate the rainfall depths associated with specified durations and return periods.
DOI: 10.5285/52248442-a50f-4fc0-a73e-31c61cd27df9
title: Long-term multisite Scots pine single nucleotide polymorphism (SNP) genotypes and phenotypes for marker-trait association analysis

description: This dataset contains data pertaining to the phenotypes (height and budburst) and genotypes (via SNP array) for a subset of trees from a long term multi-site Scots pine experimental trial.
description: A multi-site field trial was established at three sites in Scotland in 2012 using five year old Scots pine trees.  Each site contains 8 families from each of 21 native Caledonian pinewood provenances. Two of the sites (Glensaugh and Yair) have four randomised blocks and the other (Inverewe) has three randomised blocks. 

This dataset relates to a subset of trees (N = 208) from two of the sites (Glensaugh, N = 108; Yair, N = 100).  Glensaugh (latitude 56.893567, longitude -2.535736) and Yair (latitude 55.603625, longitude -2.893025)

Phenotypes (height and budburst) were assessed in situ at each site in spring (budburst) and winter (height) between 2015 and 2020. 

Trees were genotyped using a multi-species SNP array (Perry et al., 2020). DNA was extracted from needles using a Qiagen DNeasy 96 kit following the manufacturer’s instructions. Needles were dried on silica gel prior to extraction and DNA was quantified using a Qubit spectrophotometer to ensure a minimum standardized concentration of 35ng/µl. The quality of genomic DNA was also checked visually for fragmentation on 1 % agarose gel.

Genotyping was done at Edinburgh Genomics following DNA amplification, fragmentation, chip hybridisation, single-base extension through DNA ligation and signal amplification performed according to the Affymetrix Axiom® Assay protocol. Genotyping was performed in 384-well format on a GeneTitan according to the manufacturer’s procedure. Genotype calls were performed using Axiom Analysis Suite software as recommended by the manufacturer (ThermoFisher). Samples were assigned to an analysis group based on their call rate (CR) and dish QC (DQC: a metric provided by ThermoFisher which is generated by measuring signals at multiple sites in the genome known not to vary among individuals), using the following thresholds: DQC ‘high’ ≥ 0.82; DQC ‘low’ < 0.82; CR ‘high’ ≥ 96; CR ‘low’ < 96. Analyses: 1) DQC high + CR high; 2) DQC high + CR low; 3) DQC low + CR low. High quality samples (N=529), with high CR and DQC, were used to set thresholds for allele calls. Posteriors for allele calls were subsequently used as priors for analyses 2 (N = 753) and 3 (N = 251).
DOI: 10.5285/0446bf29-8f7e-4132-9b34-1b66b9ff5b78
title:  Benthic organic matter biomass data for three intensively studied Wessex chalkstream sites, England, UK

description: Ash-free dry mass (AFDM) of three distinct size fractions of organic matter (>1cm, >1mm and > 0.025mm) collected in quantitative samples of the benthic substrate determined from up to 10 replicate samples at three chalkstream sites in the Wessex chalk area (Nine Mile River, River Till and River Wylye) on each sampling occasion. Data were collected on seven occasions on the Wylye and Nine Mile River and on five occasions on the Till, between October 2012 and October 2013. Data were collected to construct quantified food webs detailing the flux of mass and nutrients between nodes of the food web. This dataset was created as part of work package 3.2 of the Wessex Biodiversity & Ecosystem Service Sustainability (BESS) project.
description: Samples were collected on 7 separate occasions from Nine Mile River and the River Wyle.  However samples were only collected on five occasions from the River Till.  No sampling occurred in this river during the salmonid breeding season Dec-Mar so as not to disturb redds.  Ten replicate samples have been processed from Oct 12, Feb 13, Mar 13 and May 13 sampling.  Five replicate samples have been processed from Jul 13, Aug 13 and Oct 13 sampling.

Standard laboratory procedures for determining AFDM content were followed but there was no formal quality assurance procedure, such as repeat analysis of replicate samples from within a replicate Surber sample.  Final values were checked for marked outliers (none were found) before calculations were undertaken.
DOI: 10.5285/e6e835ae-99e6-445e-b0dc-0d0db44e310a
title: Automatic weather station (AWS) data from Sourhope field experiment site, Scotland, 1999-2002 [NERC Soil Biodiversity Programme]

description: Regular temperature, rainfall and other weather data, as collected between February 1999 and September 2002 from an on-site Automatic Weather Station, located on experimental plots at Sourhope, Scotland.  Data were collected as part of the NERC Soil Biodiversity Thematic Programme, which was established in 1999 and was centred upon the intensive study of a large field experiment located at the Macaulay Land Use Research Institute (now the James Hutton Institute) farm at Sourhope in the Scottish Borders (Grid reference: NT 8545 1963). During this time, the site was monitored to assess changes in above-ground biomass production (productivity), species composition and relative abundance (diversity). 




description: Regular temperature, rainfall and other weather data were collected between February 1999 and September 2002 from an on-site Automatic Weather Station (AWS), located on the experimental plots at Sourhope. This was regularly downloaded and transmitted to the Soil Biodiversity Data Manager at the Centre for Ecology & Hydrology.  The data are hourly summaries from 5-sec samplings collected by the AWS.
DOI: 10.5285/730086fd-80cd-4f44-8b13-b6168c07c7a8
title: Scanning Electron Micrographs of seed and fruits of plant species from the Cerrado region collected between 2014 and 2020

description: This dataset contains single scanning electron microscopy images for fruits/seeds collected between 2014 and 2020 from fourteen plant species found in Brazilian Cerrado as an indication of seed structure, morphology and size. The work was supported by NE/T004851/1 NERC-FAPESP Seedcorn Fund Collaboration Project "Fire-adapted seed traits in Cerrado species" between RHUL (UK) and UNESP (Brazil).
description: Collected seeds were stored below 40% relative humidity at 20°C. Seed samples were fixed, dehydrated in alcohol and critical point dried. Samples were fixed on Aluminium and coated with gold (30-40 nm) and imaged using a Scanning Electron Microscope (SEM) in 2020. SEM images include scale.
DOI: 10.5285/ac7ead61-e90b-4295-baad-9ed146f2993e
title: Banded mongoose morphometrics, life history and weather data, Queen Elizabeth National Park, Uganda, 2017-2020

description: The dataset includes life history, biometric and weather data collected for banded mongooses at Queen Elizabeth National Park, Uganda, in the period 2017-2020.
The data includes a list of experimental individuals, in addition to more general information collected in the field regarding the mongooses life history, body mass measured daily in the field, biometric data collected during trapping events and weather data.
description: Eight groups are completely habituated to human observers and visited daily. Over 95% of the population is marked, and regularly captured for collection of biometric data and blood samples. Most individuals are trained to sit on a digital balance to record body mass.
DOI: 10.5285/ae6f821a-35a1-4a37-af4d-ca7cb1c83a10
title: Bird breeding data for Wytham Woods, Oxfordshire, UK, 2020 and 2021

description: This dataset contains information on the following: nest building, identity of breeding pairs, date when the first egg is laid, number of eggs, hatch date and the number and condition of fledglings for great tits, blue tits, marsh tits and coal tits. The data presented were collected in Wytham Woods, Oxfordshire, UK during April and June in 2020 and 2021 by Keith McMahon, Sam Croft and Kristina Beck, as part of a long-term nestbox project on the breeding biology of birds. The work was supported by the Natural Environment Research Council (Grant NE/S010335/1), The ecology of behavioural contagion in natural systems.
description: During April - June, all nestboxes were visited weekly to obtain records of nest building, the identity of the breeding pair, the date when the first egg is laid, the number of eggs, hatch date, and the number and condition of fledglings. These data were entered into our internal Edward Grey Institute (EGI) Bird data Management Project application (EBMP), where they underwent a number of quality checks. The data provided here were extracted from EBMP. In csv format for deposit into the EIDC.
DOI: 10.5285/2ce166c7-bff2-419b-a17f-0a5d53896416
title: Loch Leven monitoring data (water quality, chemistry, crustacean zooplankton), 2020

description: This dataset contains weather conditions, water quality, water chemistry and crustacean zooplankton counts sampled at Loch Leven throughout the year 2020. Loch Leven is a lowland lake in Scotland, United Kingdom. The data were collected as part of a long-term monitoring programme, which began in 1968 and is still underway. Sampling occurs roughly every 2 weeks with laboratory analysis and data processing being performed at the UK Centre for Ecology & Hydrology Edinburgh site. The sampling and processing was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability.
description: The data was collected from fieldwork at Loch Leven during 2020 and recorded into a database with some samples being processed in the laboratory first. All data has been through a QA process, which is detailed in the metadata document.
DOI: 10.5285/ad7068c3-5225-437a-ae58-8422cb7e3454
title: Fish monitoring data from Akrotiri SBA, Cyprus, February 2018 to March 2019

description: This dataset contains the number and fork length of Aphanius fasciatus and Gambusia holbrooki individuals caught in traps within pools and channels in Akrotiri SBA, Cyprus between 26th February 2018 and 19th March 2019. Fish were collected from submerged and semi-submerged traps within six pools and three channels within the lake approximately every two weeks before being measured, sexed as obvious males and then released. Traps were deployed for 24 hours before data were collected. Water temperature, dissolved oxygen and conductivity were also measured. Data were collected as part of a Defra Darwin Initiative Plus project to ascertain baseline data on native and non-native fish populations in the Sovereign Base Area of Akrotiri.
description: All equipment and protocols were sourced and developed by an experienced (>25 years of relevant experience) staff member from the Centre for Ecology & Hydrology (CEH), who also led the initial field work which included specific training for a staff member from the local organisation.  All incoming data were reviewed and digitised by the CEH staff member. An exploratory series of trials was conducted to assess trap performance in different habitats and configurations using underwater video recording before the final configurations were decided.
DOI: 10.5285/ad86c0d3-624c-4ae3-9280-8d1ccbe14929
title: Water and carbon balance outputs of a mechanistic ecosystem model for a Caatinga dryland forest in north-east Brazil

description: This dataset contains half-hourly output data (for the year 2011) generated by a preliminary version of the Shrubland Ecosystem Assessment (SEcA) model. SEcA calculates the ecosystem processes for a semi-arid shrubland system, for this dataset the model has been configured for a Caatinga ecosystem. The model generates four output files, those generating the aerodynamic and surface resistances, state variables, energy balance fluxes, and carbon flux-related outputs. The data provided here relate to model runs with the JULES Farquhar model, with the Sinclair plant water stress switched on.

This work was funded by Newton/NERC/FAPESP Nordeste project: NE/N012488/1.
description: The main program (in Fortran) reads in the required input data (meteorological data, plant physiological parameters and soil hydraulic parameters) and calls the key subroutines that execute the calculation of the resistance network, fluxes (energy, water and carbon) and above-ground state variables. Input data have been quality-checked for outliers.
DOI: 10.5285/d366ed40-af8c-42be-86f2-bb90b11a659e
title: Methane and carbon dioxide fluxes from a permanent wetland in the Okavango Delta, Botswana, 2018-2020

description: The data resource consists of half hourly time series of heat (latent and sensible) and trace gas (carbon dioxide and methane) fluxes obtained by eddy-covariance, gas concentrations and ancillary meteorological data (e.g. air temperature, relative humidity, pressure, photosynthetically active radiation, total incoming radiation, wind speed and direction). The data were collected at Guma Lagoon (18°57'53.01"S;  22°22'16.20"E), in the perennially flooded area of the Okavango Delta, Botswana, for the purpose of quantifying greenhouse gas fluxes over a Cyperus papyrus stand. The measurement period was 01/01/2018 to 31/12/2020. The instrumentation was installed the UK Centre for Ecology and Hydrology; monthly maintenance and data collection visits were effected by the Okavango Research Institute, University of Botswana.

The research was funded through NERC grant reference NE/N015746/2 - The Global Methane Budget.
description: Raw data from the eddy-covariance instrumentation were processed into half-hourly fluxes using the EddyPro software package version 7.0.6 and quality controlled by Dr Helfter. An automatic weather station recorded recorded air temperature, pressure, relative humidity, wind speed and wind direction, total solar radiation and photosynthetically active radiation (PAR).
Data from both instruments were downloaded, processed and deposited into the EIDC as a comma separted value (.csv) file.
DOI: 10.5285/f08a5de5-55d4-44de-90e1-94bb4f814054
title: Boreal forest floor greenhouse gas emissions across a wildfire-disturbed chronosequence

description: Data collected during field experiments to investigate the effect of wildfires on greenhouse gas emissions across forests of differing ages in Sweden. Data comprise greenhouse gas measurements, soil horizon depth, bulk density, loss on ignition, pH, soil phosphorus, carbon and nitrogen, plant species cover, air temperature, soil temperature, soil moisture and surface leaf moisture. Greenhouse gas measurements were taken in the field. Soil cores were collected, then processed and analysed in the laboratory. Plant species cover was by visual estimate and final plant biomass was collected, dried, and weighed.  Weather stations were used to collect temperature and moisture data.

Data were collected from six Swedish boreal forest sites forming a chronosequence ranging in age from approximately 45 to 365 years since last wildfire. This part of the project used soil and plant property data, climatic data, and age since wildfire to investigate controls on forest floor greenhouse gas fluxes. The project ran from 2012 to 2015.
description: Field data was collected in June and September 2012, June 2013, and July 2014.  Greenhouse gas fluxes were measured using infrared gas analysers (PP Systems EGM4) for CO2 and manual samples from static chambers for CH4 and N2O, which were analysed on a gas chromatogram (PerkinElmer Autosystem XL).  Data were quality controlled by plotting all data points and removing any points showing signs of the system not being settled or properly sealed.  Calibration standards were used for both systems.  At the time of sampling, measures of air temperature and photosynthetically active radiation were taken using a temperature probe (TinyTag) and PAR Quantum Sensor (Skye Instruments), respectively.

Soil cores were collected either along a transect or from within the gas collars.  Cores were PVC pipes of 5 cm diameter and 10 cm length.  Manual processing (including oven-drying of samples) gave values for soil horizon depths, bulk density, and loss on ignition.  pH was determined by mixing 10 g fresh soil in 25 mL deionized water and measuring using a pH probe (Hanna Instruments).  Total phosphorous was obtained using a digestion method with analysis on a SEAL AutoAnalyser3 (Seal Analytical).  Total C:N was obtained by analysing samples on a Truspec C:N analyser (LECO Corp.).  Instruments were run with calibrated standards.

Estimates of cover by individual plant species were made visually and photos were used for quality control.  Plant material was collected from within the gas collars, dried at 60ºC, and weighed for total biomass.

Air temperature, soil temperature at 1 cm and 5 cm depth, soil moisture, and surface leaf moisture were measured using weather stations (Decagon Devices).  Data was plotted to quality control for errors caused by dead batteries, snow cover, etc.
DOI: 10.5285/e54069b6-71a9-4b36-837f-a5e3ee65b4de
title: Arable crop yield data for the Hillesden Experimental Platform, UK, collected between 2006 and 2011

description: This dataset contains yield data for wheat, oilseed rape and field beans grown in fields under different agri-environment practices. The fields were located at the Hillesden Estate in Buckinghamshire, UK, where a randomised block experiment had been implemented to examine the effects of converting differing proportions of arable land to wildlife habitat. The fields were planted with wheat (Triticum aestivum L.) followed by break crops of either oilseed rape (Brassica napus L.) or field beans (Vicia faba L.). Three treatments were applied at random: a control ("business as usual"), Entry Level Stewardship (ELS) treatment and ELS Extra treatment. The ELS treatment involved removing 1% of land to create wildlife habitats. The ELS Extra had a greater proportion of land removed (6%) and additional wildlife habitats included. The total yield of each crop was measured at the time of harvesting using a yield meter attached to the combine harvester. From these values, yield per hectare and the ratio of crop yield to regional average yield were calculated.
description: All crops were managed consistently across the farm regardless of experimental treatment with conventional inputs of fertilisers and pesticides which aimed to maximise yield. Data were collected by experienced personnel using standardised protocols. Data were checked for unusual values following input into electronic format.
DOI: 10.5285/4d30e697-6a97-45ed-95e6-ac4d66247284
title: Land cover maps of Sierra de San Javier, Tucumán, Argentina, 1986-2018

description: The data resource consists of a series of land cover maps built using raster and shapefiles to evaluate the expansion of the invasive Ligustrum lucidum forest cover in Sierra de San Javier (Yungas forest ecoregion). The classification was conducted to investigate the expansion of the invasion of non-native species Ligustrum at the landscape scale and to model future management strategies using RangeShifter software.
The data includes 4 maps with 8 classes of land used: Ligustrum forest; Subtropical montane forest; Dry forest; Montane grasslands; Anthropogenic grasslands and shrubland used for livestock and temporary agriculture, a mixed class including also herbaceous agriculture and low-density urban areas; Sugar cane;  Citrus plantations, mostly lemon; high/medium-density urban and build up areas. 
The work was carried out as part of NERC grant NE/S011641/1 Optimising the long-term management of invasive species affecting biodiversity and the rural economy using adaptive management

description: Land cover maps (LCM) were constructed by applying the machine learning algorithm Random Forest (RF), based on an ensemble of classification trees, fully run on the Google Earth Engine platform. For the classification of tree model runs, 315 points from Thematic Mapper (TM) were chosen by visual interpretation of satellite data. Training areas previously obtained and tested were used to build LCM series of the same study area.  All data collected in training areas is recorded onto GPS in the field. For the Ligustrum class, 52 training points were collected during fieldwork in 2010 and spectrally verified using vegetation indices. A total of 367 points for the 8 categories were used and then were transferred into spreadsheets and exported as comma-separated value (CSV) files for deposit into the EIDC. To classify 1986 LCM a subset of training points of L. lucidum was used, including only 28 areas that were identified as source points of dispersion.
The land cover maps obtained were downloaded in raster  (tif) format. All training points were presented as shape files.