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The data provides a collected set of information on electric power rating and efficiency for commercially available prime movers in waste to energy applications. The five main technologies in focus are: steam turbines, gas turbines, microturbines, reciprocating internal combustion engines, and fuel cells (specifically solid oxide). The technology data points collected are systems that can use fuel with components from biogas (methane, hydrogen, etc) generated during anaerobic waste treatment. The data set provides a commercial preview of each technology that could guide potential use in different ranges based on desired electric output (and required power input). The data here is compiled information on efficiency and electric power capacity of prime mover technologies commercially available. Information on five main technologies are provided- Steam Turbine, Gas Turbine, MicroTurbine, Reciprocating Internal Combustion Engine and Fuel Cell (mostly Solid Oxide). Both .csv files contain the same information, but organized differently.

Time series of data corresponding to Evans et al. (2022) "Natural variability and expansion of the nitrogen deficit within the Eastern Tropical North Pacific Oxygen Deficient Zone", containing secondary quality controlled data of 8 cruises in the ETNP ODZ, seven of which on the 110 W line, as well as supplemental sediment core data and CalCOFI oxygen data for comparison. Intermediate data products generated by the code used for this paper are also included, and the code to generate these intermediate products as well as the final outputs has been uploaded onto a separate Zenodo repository, "ETNP_ODZ_time_series_code" at https://doi.org/10.5281/zenodo.6519316. More detailed information is available in the README, but should you have any questions, please reach out to Allan Devol or Natalya Evans.

NestWatch is a citizen-science project operated by the Cornell Lab of Ornithology. Since 1965, members of the public have been following a standardized protocol for observing and reporting birds' nests in the United States and Canada (and more recently, other countries). This dataset contains raw nest records submitted to NestWatch. The metadata paper associated with this dataset is critical for understanding fields and their contents. The dataset contains millions of nest check observations from > 574,000 nest attempts (as of 2023). Details about NestWatch can be found on the project website: www.nestwatch.org. The dataset is scheduled for updates annually on or about January 31. The most up-to-date data files are on the NestWatch website here: https://nestwatch.org/explore/nestwatch-open-dataset-downloads/. See documentation at "Related links" on this page for further details.

The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown herein as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit boundary.

The data consists of annual measurements of standing aboveground plant biomass, annual aboveground net primary productivity and annual soil respiration between 1998 and 2012. Data were collected from seven European shrublands that were subject to the climate manipulations drought and warming. Sites were located in the United Kingdom (UK), the Netherlands (NL), Denmark ( two sites, DK-B and DK-M), Hungary (HU), Spain (SP) and Italy (IT). All field sites consisted of untreated control plots, plots where the plant canopy air is artificially warmed during night time hours, and plots where rainfall is excluded from the plots at least during the plants growing season. Standing aboveground plant biomass (grams biomass per square metre) was measured in two undisturbed areas within the plots using the pin-point method (UK, DK-M, DK-B), or along a transect (IT, SP, HU, NL). Aboveground net primary productivity was calculated from measurements of standing aboveground plant biomass estimates and litterfall measurements. Soil respiration was measured in pre-installed opaque soil collars bi-weekly, monthly, or in measurement campaigns (SP only). The datasets provided are the basis for the data analysis presented in Reinsch et al. (2017) Shrubland primary production and soil respiration diverge along European climate gradient. Scientific Reports 7:43952 https://doi.org/10.1038/srep43952 Standing biomass was measured using the non-destructive pin-point method to assess aboveground biomass. Measurements were conducted at the state of peak biomass specific for each site. Litterfall was measured annually using litterfall traps. Litter collected in the traps was dried and the weight was measured. Aboveground biomass productivity was estimated as the difference between the measured standing biomass in year x minus the standing biomass measured the previous year. Soil respiration was measured bi-weekly or monthly, or in campaigns (Spain only). It was measured on permanently installed soil collars in treatment plots. The Gaussen Index of Aridity (an index that combines information on rainfall and temperature) was calculated using mean annual precipitation, mean annual temperature. The reduction in precipitation and increase in temperature for each site was used to calculate the Gaussen Index for the climate treatments for each site. Data of standing biomass and soil respiration was provided by the site responsible. Data from all sites were collated into one data file for data analysis. A summary data set was combined with information on the Gaussen Index of Aridity Data were then exported from these Excel spreadsheet to .csv files for ingestion into the EIDC.

Although GPS coordinates for current populations are not included due to the potential threat of poaching, the climate variables for each species are provided. The records for extant gecko and skinks mainly came from the New Zealand's Department of Conervation Herpetofauna Database. After updating the taxonomy and cleaning the data to reflect the taxonomy as at 2019 of 43 geckos speceis recognised across seven genera and 61 species in genus, we then thinned the occurrence records at a 1 km resolution for all species then predicted distributions for those with > 15 records using species distribution models. The climate variables for each species were selected among annual mean temperature (bio1), maximum temperature of the warmest month (bio5), minimum temperature of the coldest month (bio6), mean temperature of driest quarter (bio9), mean temperature of wettest quarter (bio10), and precipitation of the driest quarter (bio17). To reduce multicollinearity in species distribution models for each species, we only retained climate variables with a variable inflation factor < 10. The climate variables were from the CHELSA database (https://chelsa-climate.org/), which can be freely downloaded for current and future scenarios. We also provide MCC tree files for the geckos and skinks. The phylogenetic trees have been constructed for NZ geckos by (Nielsen et al., 2011) and for NZ skinks by (Chapple et al., 2009). For geckos we used a subset of the sequences used by Nielsen et al. (2011) for four genes, two nuclear (RAG 1, PDC) and two mitochondrial (16S, ND2 along with flanking tRNA sequences). For skinks, we used sequences from Chapple et al. (2009) for one nuclear (RAG 1) and five mitochondrial (ND2, ND4, Cyt b, 12S and 16S) genes, and additional ND2 sequences for taxa not included in the original phylogeny (Chapple et al., 2011, p. 201). In total we used sequences for all recognised extant taxa (Hitchmough et al., 2016) as at 2019 except for three species of skink (O. aff. inconspicuum “Okuru”, O. robinsoni, and O. aff. inconspicuum “North Otago”) and two species of gecko (M. “Cupola” and W. “Kaikouras”) for which genetic data were not available. Aim: The primary drivers of species and population extirpations have been habitat loss, overexploitation, and invasive species, but human-mediated climate change is expected to be a major driver in future. To minimise biodiversity loss, conservation managers should identify species vulnerable to climate change and prioritise their protection. Here, we estimate climatic suitability for two speciose taxonomic groups, then use phylogenetic analyses to assess vulnerability to climate change. Location: Aotearoa New Zealand (NZ) Taxa: NZ lizards: diplodactylid geckos and eugongylinae skinks Methods: We built correlative species distribution models (SDMs) for NZ geckos and skinks to estimate climatic suitability under current climate and 2070 future-climate scenarios. We then used Bayesian phylogenetic mixed models (BPMMs) to assess vulnerability for both groups with predictor variables for life history traits (body size and activity phase) and current distribution (elevation and latitude). We explored two scenarios: an unlimited dispersal scenario, where projections track climate, and a no-dispersal scenario, where projections are restricted to areas currently identified as suitable. Results: SDMs projected vulnerability to climate change for most modelled lizards. For species’ ranges projected to decline in climatically suitable areas, average decreases were between 42–45% for geckos and 33–91% for skinks, although area did increase or remain stable for a minority of species. For the no-dispersal scenario, the average decrease for geckos was 37–52% and for skinks was 33–52%. Our BPMMs showed phylogenetic signal in climate change vulnerability for both groups, with elevation increasing vulnerability for geckos, and body size reducing vulnerability for skinks. Main conclusions: NZ lizards showed variable vulnerability to climate change, with most species’ ranges predicted to decrease. For species whose suitable climatic space is projected to disappear from within their current range, managed relocation could be considered to establish populations in regions that will be suitable under future climates.

The data comprise Climber3alpha+C simulations created by Matthias Hofmann (PIK) as part of the Work Package 2.1 of the COMFORT project as well as the PyFerret scripts (written by Ralf Liebermann and Matthias Hofmann) used for their evaluation. The simulation data consist of snap_*.nc files and history.nc files for ocean, atmosphere and mixed layer depth (hmxl) performed for different idealized scenarios: CONTROL, double and fourfold atmospheric CO2 (CO2X2 and CO2X4), also with additional Greenland freshwater influx (CO2X2_HOSING and CO2X4_HOSING). Furthermore, tracer simulations (CONTROL, CO2X4, CO2X4_HOSING) and simulations with constant scavenging (CO2X4) are also included. The aim was to analyse the simulations regarding climate change-induced changes in marine biogeochemistry and primary production, which will be published under the title "Shutdown of Atlantic overturning circulation could cause persistent increase of primary production in the Pacific" (see Related Work). Simulation data were generated with Climber3alpha+C (Earth system model of intermediate complexity) and evaluated with PyFerret v7.41. CDO was used to aggregate monthly simulation data into annual means.

This dataset and codebook correspond to the second round of survey data gathered in Denmark in 2023, within the project FULFILL - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes. As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from six countries: Denmark, France, Germany, Italy, Latvia, and India. The first round of the survey, consisted of recruiting a representative sample of approximately 2000 households in each country. In this second survey round, we recruit around 500 respondents from the initial survey round, ensuring representativity is maintained. This survey is very similar to the survey in the first round and includes a lot of identical items, including a quantitative assessment of the carbon footprint in the housing, mobility, and diet sectors, socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. Furthermore, the survey includes measures of quality of life, encompassing aspects such as health and well-being, environmental quality, financial security, and comfort. New for this second round, we have incorporated questions regarding the measures respondents adopted in response to the 2022 energy crisis.

Survey data used in a perception study of stalked barnacle harvesters on the effectiveness of fisheries management practices in Spain, Portugal and France. Harvesters from the following six regions along the Atlantic Arc participated: Morbihan in Brittany (France), Asturias-East, Asturias-West and Galicia (Spain), the Reserva Natural das Berlengas (RNB; Portugal) and the Parque Natural do Sudoeste Alentejano e Costa Vicentina (PNSACV; Portugal). We administered 184 surveys from October 2019 to September 2020 and each region was treated as an independent population. The data includes: general demographic data (Region, Age, Gender, Level of Education, Main income source, Years of Experience); perception data of the effectiveness of the currently implemented management strategies in each region (coded: e_name_of_strategy – using Likert Scale with scores ranging from 1 = completely ineffective to 5 = very effective); data of the willingness for change of the currently implemented management (Yes, No, NA); and data of harvesters’ perceptions regarding the most important strategy to achieve sustainability in the fishery. Because the surveys were conducted both before and during the Covid-19 pandemic (the column Covid indicates whether the data was collected before or during the pandemic), we had to make adjustments in our data collection methods. We provided the following options for survey completion (see the Recollection_of_data column): by hand in a written format, online, or via an oral interview conducted with the assistance of a scientist per telephone. Our results indicate that the majority of harvesters in the regions in Portugal and France were willing to make changes to current management strategies, reflecting their awareness of the need for improvement. Based on the AIC model selection analysis results, the model with the single variable region explained 83% of the cumulative model weight. The variable region was the best predictor of the trends in management strategy preferences, and presented a highly significant goodness-of-fit result (p<0.001), suggesting that regional differences play a significant role in shaping these preferences. No clear trend emerged regarding a single "optimal" management strategy preferred by harvesters across regions. Harvesters in less developed co-management systems favored general input and output restrictions and expressed a desire for greater involvement in co-management processes. Conversely, harvesters in highly developed co-management systems with Territorial User Rights for Fishers (TURFs) preferred the most restrictive and spatially explicit management strategies, such as implementing harvest bans and establishing marine reserves. Our findings emphasise that management strategies do not only need to be tailored to each region's particular practices, needs, and characteristics, but that resource users’ readiness for specific strategies also needs to be considered.