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GloGEM CMIP6 global glacier projections

The files contain the glacier evolution as modelled with the Global Glacier Evolution Model (GloGEM; Huss and Hock, 2015) under the CMIP6 (Coupled Model Intercomparison Project Phase 6) climate models. For details about the simulations, refer to Zekollari et al. (2024), where the model setup and the results are described. The files are provided at the regional scale, for each of the 19 glacier regions as defined in the Randolph Glacier Inventory v6.0 (RGI Consortium, 2017), for two variables (glacier volume and glacier area). The folder structure is as follows:  ‘Variable/RGIXX’, with: <ul> <li>‘Variable’: ‘Area’ or ‘Volume'</li> <li>‘RGIXX’: the region, where XX refers to the RGI v6.0 region number</li> </ul> Every file contains the evolution (of Volume [km3] or Area [km2]) for a given Shared Socioeconomic Pathways (SSP), with ssp119.csv corresponding to SSP1-1.9, ssp126 corresponding to SSP1-2.6,…etc. If you use these data, please cite the dataset as following: <ul> <li>Cite ZENODO</li> <li>Cite the corresponding publication (Zekollari et al., 2024)</li> </ul>   Somes notes/remarks: <ul> <li>In these GloGEM simulations, every glacier is calibrated to match the glacier-specific observed mass changes by Hugonnet et al. (2021).</li> <li>Until 2020, the glacier evolution forcing is from ERA5, while from 2020 onwards, the forcing comes from the respective climate models. Very slight differences can exist in the modelled glacier evolution prior to 2020 for the different climate models, because of randomly generated sub-monthly air temperature variability used to estimate monthly positive degree days more accurately.</li> <li>In the manuscript that describes the glacier evolution (Zekollari et al., 2024), the results are shown for SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 (for 12 climate models). In the data provided here on ZENODO, the evolution as modelled under SSP1-1.9 is also included, but these simulations are not described in the manuscript (owing to limited climate model ensemble size, n=3).</li> <li>For SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, the considered climate models are the same as in Rounce et al. (2023).</li> <li>The glacier evolution was modelled with the same climate model forcing as OGGM v1.6.1. The OGGM v1.6.1 data is available from Schuster et al. (2023) and these projections are also described in Zekollari et al. (2024).</li> </ul> References Hugonnet, R., McNabb, R., Berthier, E., Menounos, B., Nuth, C., Girod, L., Farinotti, D., Huss, M., Dussaillant, I., Brun, F., and Kääb, A.: Accelerated global glacier mass loss in the early twenty-first century, Nature, 592, 726–731, https://doi.org/10.1038/s41586-021-03436-z, 2021. Huss, M. and Hock, R.: A new model for global glacier change and sea-level rise, Frontiers in Earth Science, 3, 54, https://doi.org/10.3389/feart.2015.00054, 2015. RGI Consortium: Randolph Glacier Inventory – A Dataset of Global Glacier Outlines: Version 6.0: Technical Report, Global Land Ice Measurements from Space, Colorado, USA. Digital Media, , https://doi.org/10.7265/N5-RGI-60, 2017. Rounce, D. R., Hock, R., Maussion, F., Hugonnet, R., Kochtitzky, W., Huss, M., Berthier, E., Brinkerhoff, D., Compagno, L., Copland, L., Farinotti, D., Menounos, B., and McNabb, R. W.: Global glacier change in the 21st century: Every increase in temperature matters, Science, 379, 78–83, https://doi.org/10.1126/science.abo1324, 2023. Schuster, L., Schmitt, P., Vlug, A., and Maussion, F.: OGGM/oggm-standard-projections-csv-files: v1.0 (v1.0), https://doi.org/10.5281/zenodo.8286065, 2023. Zekollari, H., Huss, M., Schuster, L., Maussion, F., Rounce, D. R., Aguayo, R., Champollion, N., Compagno, L., Hugonnet, R., Marzeion, B., Mojtabavi, S., and Farinotti, D.: Twenty-first century global glacier evolution under CMIP6 scenarios and the role of glacier-specific observations, The Cryosphere, 18, 5045-5066, https://doi.org/10.5194/tc-18-5045-2024, 2024.

HPLC analysis of microalgal biomass produced in the ASTEASIER project

HPLC analysis of microalgal samples produced in the framework of ASTEASIER project (WP1). The datasets include HPLC chromatogram, peaks reports and quantification results. Samples: H. pluvialis: Haematococcus pluvialis (biomass produced at the University of Verona in 300L scale) S4 Whole cells: Nannochloropsis gaditana S4 strain whole cells (biomass produced at A4F facilities in 5000L scale) S4 Broken  cell: Nannochloropsis gaditana S4 strain broken cells (biomass produced at A4F facilities in 5000L scale) BC: Synechococcus PCC 11901 BC strain (biomass prodiced at A4F facilities in 1200L scale   Peaks Reports: analysis of the chromatograms peaks. The information reported includes: HPLC System Name : HPLC instrument used    Chromatogram Name  : sample analyzed  Acquisition Date: date of HPLC run     tR : retention time    Area : area of the peak    Height : height of the peak    Area% : percentage of the peak area compared to the overall chromatogram    Height% : percentage of the peak heigth compared to the overall chromatogram       Peak Width: width of the identified peak     Sample #  : number of the sample run  Volume: volume of the sample injected

Hesperomys Project v24.4.0

An export of data from the <a href="https://hesperomys.com/">Hesperomys Project</a> version 24.4.0. The Hesperomys Project is a database of taxonomy and nomenclature, focused on mammals but also covering some other groups, principally other fossil tetrapods. The database contains information such as: <ul> <li>Taxonomic classification for all mammals, living and extinct</li> <li>References to original citations for the vast majority of names</li> <li>Type specimens and type localities for numerous names</li> </ul> The full database is available online at hesperomys.com. This export contains: <ul> <li>name.csv: Data on names, including taxonomic context, authority, citation, type locality, type specimen, and classification of the etymology.</li> <li>taxon.csv: Data on taxa, including classification and authority</li> <li>collection.csv: Data on collections that contain type specimens, including name, location, and number of type specimens in the database</li> </ul> The code used to generate the exports is on <a href="https://github.com/JelleZijlstra/taxonomy/blob/9a68bec908264ddfcb1d623b4c427ebfb689396f/taxonomy/db/export.py">GitHub</a>. Release notes for version 24.4.0: This release focuses on compatibility with the Mammal Diversity Database (MDD) and improved automatic data extraction. More than a third of names are now linked directly to pages in the Biodiversity Heritage Library. <ul> <li>Database <ul> <li>Fix species assignment for many extant mammal names based on comparisons with data from the MDD. The database's classification for extant mammals now exactly matches the MDD (except for new changes that have not been published in the MDD's latest release yet).</li> <li>Set more precise page numbers instead of page ranges for a few dozen names.</li> <li>Add coverage of Cynognathia, a group of Triassic cynodonts closely related to mammals.</li> <li>Add full authority citations for many more names, notably including many publishedin the publications of the Geological Survey of India and many with type specimens in the ZMMU.</li> <li>Set the page_described field for several thousand more names.</li> <li>Move to a more consistent format for type specimens. All type specimen references now start with the institution code.</li> <li>Mark numerous names as junior homonyms.</li> <li>Add links to many more citations (primarily in the Biodiversity Heritage Library).</li> <li>Add direct page links to original citations in over 30,000 names.</li> <li>Add a few hundred more type locality coordinates and correct more than a hundred incorrect coordinates.</li> </ul> </li> <li>Backend <ul> <li>Add support for ORCID identifiers (though they are currently set for very few people)</li> <li>Enforce that collection labels consist only of letters and that type specimen references start with the corresponding collection label</li> <li>Automatically detect species-group homonyms. Distinguish between primary and secondary homonyms.</li> <li>Revamp system for computing the nomenclatural status of names.</li> <li>Fill the citation group column for names with a checked original citation in the exported data.</li> <li>Add support for linking names directly to an online resource (primarily the Biodiversity Heritage Library).</li> <li>Check that type locality coordinates are in the right country.</li> </ul> </li> <li>Frontend <ul> <li>Add option to show names in a taxon that are missing a field.</li> <li>Add tool for finding homonyms in the species group.</li> <li>Add frontend support for new information (authority page links and PhyloCode numbers on names; bibliographic notes and alternative URLs on articles; comments on citation groups).</li> <li>List basal, incertae sedis, and dubious child taxa separately.</li> </ul> </li> <li>Exported files <ul> <li>Add page links and type specimen links to Name export files.</li> </ul> </li> </ul> Thanks to Rudolf Haslauer, Severin Uebbing, and especially Connor Burgin for supplying information and literature.

First 25 books from each subcategory of the main categories shared in Project Gutenberg

El conjunto de datos extraído es la información de los libros electrónicos disponibles en las categorías principales del proyecto Gutenberg. Cada libro forma parte de una subcategoría, la cual, a su vez, forma parte de una categoría de la lista “Main categories” publicada en Project Gutenberg. Para cada libro, se obtiene información relevante tanto para su clasificación como para tener más detalle de este.

opencc-by-4.0Sep 2023View details →

Baseline of the WEAI for East and West African case studies within EWA-BELT Project

The Women’s Empowerment in Agriculture Index (WEAI) is a survey-based tool that measures women’s empowerment and inclusion in agricultural activities. This database contained the baseline "pre-project activities" collected from October 2022 to January 2023 in 40 households per each Country involved (Ethiopia, Kenya, Tanzania, Burkina, Ghana).  A total of 219 households were involved collecting 381 interviews, in 65 households the primary respondent was a woman, so we had not to interview a male secondary respondent.  There are 3 types of surveys to calculate the WEAI: the original WEAI, the Accelerated WEAI (A – WEAI) and the PRO WEAI. The original WEAI is the first developed by IFPRI, the A-WEAI is a shorter and faster version, while the PRO WEAI is an extended version aimed to cover more areas of empowerment. We chose A-WEAI because it was faster and simpler to administer, considering that it is the first time for most of the partners to work on this survey and that the participants have already answered to many surveys, hence they might answer with more attention to a short and fast survey.

F I G U R E 7 in Global warming is projected to lead to increased freshwater growth potential and changes in pace of life in Atlantic salmon Salmo salar

F I G U R E 7 Model prediction of the proportion of juvenile Atlantic salmon choosing to smolt as 1-year-olds (full saturation, black = historical, green = SSP1-RCP2.6, orange = SSP3-RCP7.0, and red = SSP5-RCP8.5), 2-year-olds (medium saturation, black = historical, green = SSP1-RCP2.6, orange = SSP3-RCP7.0, and red = SSP5-RCP8.5), and 3-year-olds (low saturation, black = historical, green = SSP1-RCP2.6, orange = SSP3-RCP7.0, and red = SSP5-RCP8.5). The red line is the point of reaction norm calibration to Piggins and Mills (1985).

F I G U R E 5 in Global warming is projected to lead to increased freshwater growth potential and changes in pace of life in Atlantic salmon Salmo salar

F I G U R E 5 Ensemble average daily water temperature by day of year for the future projections under the three shared socioeconomic pathways and representative concentration pathways (SSP1-RCP2.6 left, SSP5-RCP7.0 middle, and SSP5-RCP8.5 right). Each line represents the day of year average temperature for the climate forcing ensemble with colors transitioning from blue to red toward the end of the century (starting with 2020 and ending with 2100). The lower dashed line represents the lower growth threshold temperature of 7 C, and the upper dashed line represents the upper growth threshold temperature for 23 C (Elliott & Hurley, 1997).

F I G U R E 6 Projected change between 1960 and 2100 in Global warming is projected to lead to increased freshwater growth potential and changes in pace of life in Atlantic salmon Salmo salar

F I G U R E 6 Projected change between 1960 and 2100 in length-at-smoltification decision (a, b, and c), length-at-smoltification as 1-year-olds (d, e, and f), and length-at-smoltification as 2-year-olds (g, h, and i) under the three shared socioeconomic pathways and representative concentration pathways: SSP1-RCP2.6 (green), SSP3-RCP7.0 (orange), and SSP5-RCP8.5 (red) for juvenile Atlantic salmon in the Burrishoole. The gray-shaded area represents the historical reference (2000 to 2020), and the red vertical line represents the historical average.

F I G U R E 4 in Global warming is projected to lead to increased freshwater growth potential and changes in pace of life in Atlantic salmon Salmo salar

F I G U R E 4 Generalized linear model of body length (mm) as a function of cumulative growing degree days (CGDD, C day) for the 23 observed cohorts of juvenile Atlantic salmon in the Burrishoole watershed. The solid line represents the mean length, and the gray bands represent the 95% prediction interval. The outer lines represent the sample density.

F I G U R E 3 in Global warming is projected to lead to increased freshwater growth potential and changes in pace of life in Atlantic salmon Salmo salar

F I G U R E 3 The residual error between observed and predicted water temperature (top panel), and the in-situ water temperature (black line) and long short-term memory neural network water temperature prediction (red crosses) for the training (1961–1994) and validation (1995–2019) dataset in the Mill Race (bottom panel). Years excluded due to accumulation of internal sate (green), prolonged periods of missing data (blue shaded), and measurement error (red shaded) are shown in the top panel, and the delineation of the training and validation period is shown by the vertical dashed line in both panels.

F I G U R E 2 in Global warming is projected to lead to increased freshwater growth potential and changes in pace of life in Atlantic salmon Salmo salar

F I G U R E 2 The four-step model workflow for quantitatively estimating length-at-age and life history of juvenile Atlantic salmon in response to climate change. Step 1 describes the collation of necessary data and construction of the water temperature model. Step 2 details the data preparation and construction of the length-at-age model for juvenile Atlantic salmon. Step 3 shows the coupling of the ISIMIP phase 3B projections to the water temperature model, and the subsequent coupling with the length-at-age model. Step 4 shows the post-processing of length-at-age projections to estimate smoltification probability and proportion of 1-, 2- and 3-year-old smolts. Shapes are according to ISO 5807 standard.

F I G U R E 1 in Global warming is projected to lead to increased freshwater growth potential and changes in pace of life in Atlantic salmon Salmo salar

F I G U R E 1 Location of electrofishing sites (green circles) and fish traps (red circles) in the Burrishoole catchment, Co. Mayo, Ireland.

NYMPHE Horizon 2020 project Falasarna (GR) Test Sites preliminary data

The dataset is a preliminary vision on the Falasarna (GR) test site for bioremediation action in the EU funded Horizon 2020 project. The data set iwill be used to futher refine the datasets structure and to define the hyerachical data dependences.  The distribution of the typical species for phrigana habitat was investigated. The data were obtain prior application of the bioremediation measures. The data collection mission took place in October 2024. The valuee were obtaiing by direct measurement on site,

Geological areas of interest for Geothermal District Heating utilization in Europe - GeoDH project

The dataset includes four shapefiles showing the location data of geological areas of interest for Geothermal District Heating, including hot sedimentary aquifers and Neogene basins. The hot sedimentary aquifers layer represents areas where Neogene basin contours (sourced from the IGME Europe geological map at a scale of 1:5,000,000) overlap with regions where subsurface temperatures exceed 50°C at 1000m depth and/or 100°C at 2000m depth. This dataset was developed for assessing the potential of Geothermal District Heating in Europe as part of the GeoDH project (<a href="http://geodh.eu/" target="_new" rel="noopener">http://geodh.eu/</a>). Please note that this represents the state of the art as of 2014 and that geological, technological, and regulatory developments may have occurred since its creation, and users should verify if more recent data is available for their purposes.

Heat Flow Density in Europe - GeoDH project

The dataset includes a shapefile showing areas in Europe where the Heat Flow Density is greater than 90 mW/m².  This dataset was developed for assessing the potential of Geothermal District Heating in Europe as part of the GeoDH project (<a href="http://geodh.eu/" target="_new" rel="noopener">http://geodh.eu/</a>). Please note that this represents the state of the art as of 2014 and that geological, technological, and regulatory developments may have occurred since its creation, and users should verify if more recent data is available for their purposes.

Pan-European temperature distribution at depth - GeoDH project

The dataset includes two shapefiles showing the temperature distribution at depth in Europe, specifically areas with temperatures exceeding 50°C at 1000m depth and 90°C at 2000m depth. This dataset was developed for assessing the potential of Geothermal District Heating in Europe as part of the GeoDH project (<a href="http://geodh.eu/" target="_new" rel="noopener">http://geodh.eu/</a>). Please note that this represents the state of the art as of 2014 and that geological, technological, and regulatory developments may have occurred since its creation, and users should verify if more recent data is available for their purposes.

restrictedcc-by-4.0Nov 2024View details →

Information and questionnaire associated with GENEActiv accelerometer files collected in Vanuatu during FALAH project (non-anonymized information)

Questionnaire and additional restricted participant information of the following datasets: <a title="GENEActiv accelerometer files collected in Vanuatu during FALAH project (non-anonymized version - first part)" href="https://doi.org/10.5281/zenodo.14043547" target="_blank" rel="noopener">GENEActiv accelerometer files collected in Vanuatu during FALAH project (non-anonymized version - first part)</a> <a title="GENEActiv accelerometer files collected in Vanuatu during FALAH project (non-anonymized version - second part)" href="https://doi.org/10.5281/zenodo.14089527" target="_blank" rel="noopener">GENEActiv accelerometer files collected in Vanuatu during FALAH project (non-anonymized version - second part)</a> <a title="GENEActiv accelerometer files collected in Vanuatu during FALAH project (anonymized version - first part)" href="https://doi.org/10.5281/zenodo.14043331" target="_blank" rel="noopener">GENEActiv accelerometer files collected in Vanuatu during FALAH project (anonymized version - first part)</a> <a title="GENEActiv accelerometer files collected in Vanuatu during FALAH project (anonymized version - second part)" href="https://doi.org/10.5281/zenodo.14089477" target="_blank" rel="noopener">GENEActiv accelerometer files collected in Vanuatu during FALAH project (anonymized version - second part)</a> Participant characteristics: 13 to 17 years old students. Number of participants: 72. Year of the study: 2023. Place of the study: Vanuatu.

Fighting the School-to-Prison Pipeline with Excellence Project

Join the movement of <a href="https://www.excellenceproject.org/take-action">organizations fighting school to prison pipeline</a> to transform education and opportunities for students at risk. Excellence Project works tirelessly to combat the school-to-prison pipeline by fostering equitable learning environments, mentorship programs, and advocacy for restorative justice.

Future Projections of Temperature Extremes and Urban Heat Island in Paris using Deep Learning

Future projections of 2-meter maximum and minimum temperature and land surface temperature in Paris, France, using Deep Learning, under four Shared Socioeconomic Pathways. ERA5 and GCM ensemble data at their original resolution are also included. The DL (Convolutional Neural Network) model architecture and trained weights are also available. The Python script to generate the boxplots of the future projections is also included.

Metadata of interviewees in Work Packages 4&5 of the CLEVER project

This Excel database lists metadata of interviewees that participated in the data collection of the CLEVER (Creating leverage to enhance biodiversity outcomes of global biomass trade) project in its Work Packages 4 and 5.