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Research data keyboard_double_arrow_right Dataset 2019Publisher:Mendeley Authors: Lan, K;The upload research data are including the fishery data of area coordinate, fishing effort (number of hooks), fishing date, number of catches and CPUE of immature and mature cohorts, and marien environmental date of SST, Chl-a, SSHD and MIL for 5° spatial grid from 2003 to 2012.
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You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.17632/p46tx55872&type=result"></script>'); --> </script>
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You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.17632/p46tx55872&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2020Publisher:Mendeley This dataset contains global raster maps to calculate particulate matter life cycle impacts from primary fine particulate matter (PM2.5), sulfur dioxide (SO2), nitrogen oxides (NOx) and ammonia (NH3) emissions. It also includes the R source code to reproduce these maps, a case study on the particulate matter health impacts of global coal power generation, and aggregated characterization factor tables for several spatial resolutions that are commonly used in life cycle assessment (LCA) (for example in the ecoinvent life cycle inventory (LCI) database).
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You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.17632/8jnj4vzbh6.1&type=result"></script>'); --> </script>
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You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.17632/8jnj4vzbh6.1&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:The University of Hong Kong Authors: Lishan Ran (9057026);This is the dataset for our research on assessing CO2 emissions from Chinese inland waters, including streams, rivers, lakes and reservoirs. The dataset includes three parts, including Part 1: Lakes and Reservoirs_1980s, Part 2: CO2 Dataset_2010s, and Part 3: Water chemistry records. Detailed information on these data can be found from the 'README' text file.
https://dx.doi.org/1... arrow_drop_down Smithsonian figshareDataset . 2021License: CC BY NCData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.25442/hku.13560452.v1&type=result"></script>'); --> </script>
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visibility 33visibility views 33 download downloads 21 Powered bymore_vert https://dx.doi.org/1... arrow_drop_down Smithsonian figshareDataset . 2021License: CC BY NCData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:4TU.ResearchData Authors:Reijnders, Victor;
Gerards, Marco; Hurink, Johann;Reijnders, Victor
Reijnders, Victor in OpenAIREThe data in this dataset was collected during the GridFlex Heeten project, as part of Victor Reijnders' PhD project.The data was collected between August 2018 and August 2020 in 77 households all situated in Heeten (The Netherlands) and consists of electricity consumption and gas usage per minute per household. All participating households specified their anonymous data could be used in further research. The data of this project was collected in accordance with a privacy-by-design approach.
Smithsonian figshare arrow_drop_down Smithsonian figshareDataset . 2021License: CC BY ND SAData sources: Bielefeld Academic Search Engine (BASE)4TU.ResearchData | science.engineering.designDataset . 2021License: CC BY NC SAData sources: Datacite4TU.ResearchData | science.engineering.designDataset . 2021License: CC BY NC SAData sources: DataciteDANS (Data Archiving and Networked Services)DatasetData sources: DANS (Data Archiving and Networked Services)DANS (Data Archiving and Networked Services)DatasetData sources: DANS (Data Archiving and Networked Services)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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more_vert Smithsonian figshare arrow_drop_down Smithsonian figshareDataset . 2021License: CC BY ND SAData sources: Bielefeld Academic Search Engine (BASE)4TU.ResearchData | science.engineering.designDataset . 2021License: CC BY NC SAData sources: Datacite4TU.ResearchData | science.engineering.designDataset . 2021License: CC BY NC SAData sources: DataciteDANS (Data Archiving and Networked Services)DatasetData sources: DANS (Data Archiving and Networked Services)DANS (Data Archiving and Networked Services)DatasetData sources: DANS (Data Archiving and Networked Services)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.4121/14447257&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Embargo end date: 06 Jan 2022Publisher:Dryad Authors:Jarvie, Scott;
Ingram, Travis; Chapple, David; Hitchmough, Rodney; +2 AuthorsJarvie, Scott
Jarvie, Scott in OpenAIREJarvie, Scott;
Ingram, Travis; Chapple, David; Hitchmough, Rodney; Nielsen, Stuart; Monks, Joanne M.;Jarvie, Scott
Jarvie, Scott in OpenAIREAlthough 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.
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visibility 53visibility views 53 download downloads 15 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:figshare Authors: Jiming Hao (1407004); Dijuan Liang (9675638); Xi Lu (288663); Minghao Zhuang (2822963); +3 AuthorsJiming Hao (1407004); Dijuan Liang (9675638); Xi Lu (288663); Minghao Zhuang (2822963); Guang Shi (5048222); Chengyu Hu (6520775); Shuxiao Wang (1406992);It show point estimates of GHG emissions from pesticide production from 1990 to 2016 at provincial level in China.
figshare arrow_drop_down Smithsonian figshareDataset . 2021License: CC 0Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.6084/m9.figshare.13383071&type=result"></script>'); --> </script>
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more_vert figshare arrow_drop_down Smithsonian figshareDataset . 2021License: CC 0Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.6084/m9.figshare.13383071&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:GFZ Data Services Authors:Hofmann, Matthias;
Hofmann, Matthias
Hofmann, Matthias in OpenAIRELiebermann, Ralf;
Liebermann, Ralf
Liebermann, Ralf in OpenAIREdoi: 10.5880/pik.2023.003
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.
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You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5880/pik.2023.003&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023 NetherlandsPublisher:DANS Data Station Social Sciences and Humanities Authors:Gao, X.;
Gao, X.
Gao, X. in OpenAIREDe Hoge, I.E.;
De Hoge, I.E.
De Hoge, I.E. in OpenAIREFischer, A.R.H.;
Fischer, A.R.H.
Fischer, A.R.H. in OpenAIREFashion products made from repurposed materials (e.g., backpacks made from pineapple leaves) have become more prevalent nowadays, and their environmental sustainability is one of the core advantages. Yet, it is currently unclear how consumers respond to products made from repurposed materials. We conducted three experiments to examine the effects of three material features, namely function, sustainability, and distinguishability, on consumer preferences for fashion products made from repurposed materials. The results indicate that, when the function of repurposed materials is as good as that of conventional materials, consumers prefer a product made from repurposed materials over the same product made from conventional materials. Also, consumers in general prefer repurposed materials to be less visually distinguishable. Finally, when the sustainability of the repurposed products is emphasized, consumers appear more likely to choose products made from repurposed materials, even when these products have an inferior function. In conclusion, to promote fashion products made from repurposed materials, marketers may emphasize the function and sustainability of repurposed materials, and producers may manufacture repurposed materials that visually resemble conventional materials.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2017Publisher:Chalmers University of Technology Authors: Englund, Oskar;Brazil is home to the largest tracts of tropical vegetation in the world, harbouring high levels of biodiversity and carbon. Several biomass maps have been produced for Brazil, using different approaches and methods, and for different purposes. These maps have been used to estimate historic, recent, and future carbon emissions from land use change (LUC). It can be difficult to determine which map to use for what purpose. The implications of using an unsuitable map can be significant, since the maps have large differences—both in terms of total carbon storage and its spatial distribution. This dataset of aboveground carbon was created based on data from existing maps and an up-to-date LULC map. The map reflects current LULC, has high accuracy and resolution (50 m), and a national coverage. It can be a useful alternative for scientific studies and policy initiatives concerned with existing LULC and LUC outside of existing forests, especially at local scales when high resolution is necessary, and/or outside the Amazon biome. Map unit: tonnes of aboveground carbon per hectare. This dataset of aboveground carbon was created based on data from existing maps and an up-to-date LULC map. The map reflects current LULC, has high accuracy and resolution (50 m), and a national coverage. It can be a useful alternative for scientific studies and policy initiatives concerned with existing LULC and LUC outside of existing forests, especially at local scales when high resolution is necessary, and/or outside the Amazon biome. Map unit: tonnes of aboveground carbon per hectare. Data är baserade på befintliga kartor och en aktuell LULC-karta (änding av markanvändning) för bildandet av ovanjordiskt kol i Brasilien. Kartan speglar nuvarande LULC, har hög noggrannhet och upplösning (50 m) och en nationell täckning. Mer information på den engelska katalogsidan: https://snd.gu.se/en/catalogue/study/ecds0244 This dataset of aboveground carbon was created based on data from existing maps and an up-to-date LULC map. The map reflects current LULC, has high accuracy and resolution (50 m), and a national coverage. It can be a useful alternative for scientific studies and policy initiatives concerned with existing LULC and LUC outside of existing forests, especially at local scales when high resolution is necessary, and/or outside the Amazon biome. Map unit: tonnes of aboveground carbon per hectare.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:PANGAEA Authors:Pieck, Daniela;
Pieck, Daniela
Pieck, Daniela in OpenAIREThölen, Claudia;
Thölen, Claudia
Thölen, Claudia in OpenAIREHillebrand, Helmut;
Kleyer, Michael; +2 AuthorsHillebrand, Helmut
Hillebrand, Helmut in OpenAIREPieck, Daniela;
Pieck, Daniela
Pieck, Daniela in OpenAIREThölen, Claudia;
Thölen, Claudia
Thölen, Claudia in OpenAIREHillebrand, Helmut;
Kleyer, Michael;Hillebrand, Helmut
Hillebrand, Helmut in OpenAIRELõhmus, Kertu;
Lõhmus, Kertu
Lõhmus, Kertu in OpenAIREZielinski, Oliver;
Zielinski, Oliver
Zielinski, Oliver in OpenAIRELocal tide and wave conditions were recorded with a RBRduo TDǀwave sensor (RBR Ltd., Ontario/Canada). The sensor was bottom mounted in a shallow tidal creek (0.78 m NHN) through a steel girder (buried 0.3m deep in the sediment) and was positioned 10 cm above sediment surface, as was determined by using a portable differential GPS. This resulted in the sensor falling dry during low tide. For accurate depth calculations, raw pressure data were manually corrected for atmospheric pressure derived from a locally installed weather station. The sensor was pre-calibrated by the manufacturer and the sampling rate was 3 Hz with 1024 samples per burst at a sample interval of 10 min. Recorded data were internally logged until the readout with the Ruskin (V1.13.13) software. Date and time is given in UTC.Data handling was performed according to Zielinski et al. (2018): Post-processing of collected data was done using MATLAB (R2018a). Quality control was performed by (a) erasing data covering maintenance activities, (b) removing outliers, and (c) visually checks. Low-tide data is not removed, but were easily identified through the manually calculated water depth data, where all depths < 0.05m represented low tide data.
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