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In a forward chemical equilibrium problem (FCEP), the state of minimum Gibbs energy for a chemical system is sought, in which temperature, pressure, elemental amounts, and thermodynamic model parameters are prescribed. We herein present a mathematical framework for characterizing and solving inverse chemical equilibrium problems (ICEP), a class of problems for which one or more of those prescribed conditions in a FCEP are unknown in advance. In an ICEP, complementary conditions must be imposed, which are referred to here as equilibrium constraints. Examples of ICEPs include those in which a certain property is known at equilibrium (e.g., volume is specified instead of pressure; enthalpy is specified instead of temperature; pH is specified instead of the amount of element H). The equilibrium constraints may also be specified by equations that govern the relationship between several equilibrium properties (e.g., the equations relating temperature, pressure, density, energy, and velocity of the gases produced during the detonation of an explosive). Chemical Engineering Science, 252 ISSN:0009-2509

Dataset abstract This dataset contains quality-checked meteorological observations of air temperature, relative humidity, dew point, barometric pressure and observations of downwelling solar radiation and ultraviolet radiation. Further it contains the wind speed and direction relative to the ship but not corrected for air-flow distortion, and translated into the earth reference frame. For each of these variables observations are available from a portside and starboard side sensor. The dataset also contains, cloud base height and sky cover at three levels measured with a Ceilometer. As additional information the solar azimuth and altitude angle have been calculated for the ship’s position every five minutes and have been added as a one-minute time series using the nearest value. The ship’s position, heading, course and speed over ground are also provided. The wind speed measurements were made at a height of approximately 30.5 meters above sea level. The measurement height of the temperature and humidity probes is 23.7 meters above sea level. The barometric pressure was measured at 20 meters above sea level. The observations have been screened for implausible values and on some occasions despiking based on visual inspection and a rolling interquartile range filter have been applied. Solar radiation measurements are affected by shadowing of the ship, and the air temperature and humidity by the heating of air that passes over the ship. Masks are provided to flag affected observations. The wind speed readings are affected by airflow distortion and should be used with consideration until a dataset of corrected wind speeds is published. More details on airflow distortion can be requested from the contact person. Dataset contents ACE_filtered_meteorological_data_1min.csv, data file, comma-separated values diff_TA1_TA3_WDR2_5min_1.png, metadata, portable network graphics ratio_SR1_SR3_solangle_5min_1.png, metadata, portable network graphics data_file_header.txt, metadata, text README.txt, metadata, text ace_filtered_meteorological_data_change_log.txt, metadata, text Change log v1.1 - The range check for skycover (SC) and cloudlevel (CL) was added to the quality-checking routines. 53 data points violated the range check for these variables: these have now been marked as NaN. v1.0 - Initial release of verified meteorological data. Dataset license This meteorological dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full description can be found at https://creativecommons.org/licenses/by/4.0/ {"references": ["Smith, Shawn R., Mark A. Bourassa, and Ryan J. Sharp. Establishing More Truth in True Winds. JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY 16 (1999): 14.", "Stull, Roland. Wet-Bulb Temperature from Relative Humidity and Air Temperature. 2011. Journal of Applied Meteorology and Climatology 50, no. 11 (9 September 2011): 2267\u201369. https://doi.org/10.1175/JAMC-D-11-0143.1."]} The Antarctic Circumnavigation Expedition was made possible by funding from the Swiss Polar Institute and Ferring Pharmaceuticals. Antarctic Circumnavigation Expedition – Delivering Added value To Antarctica (ACE-DATA) is funded by the Swiss Data Science Center as Project number 17-02.

# Long-term changes in taxonomic and functional composition of European marine fish communities The GitHub linked repository is here: [European_demersal_fish_assemblages (](https://github.com/auroreRECE/European_demersal_fish_assemblages)DOI [10.5281/zenodo.11190119](https://zenodo.org/doi/10.5281/zenodo.11190119)) ## Overview This project is dedicated to studying the influence of environmental conditions and fishing on the functional and taxonomic structure of a demersal fish community in Europe. This GitHub repository provides the code of the Receveur et al. (2024) publication in Ecography. ## Data files description ### df\_MFA.csv This file contains the coordinates resulting from the Multiple Factor Analysis (MFA): * X : the row numbers ; * ID_unique : a unique ID number corresponding to the trawls ; * Dim.1 : the coordinate of each trawl on the first MFA dimension ; * Dim.2 : the coordinate of each trawl on the second MFA dimension ; * Dim.3 : the coordinate of each trawl on the third MFA dimension ; ### df\_PCA.csv This file contains the coordinates * X : the row numbers ; * ID_unique : a unique ID number corresponding to the trawls ; * Dim.1 : the coordinate of each trawl on the first PCA dimension ; * Dim.2 : the coordinate of each trawl on the second PCA dimension ; * Dim.3 : the coordinate of each trawl on the third PCA dimension ; ### df\_env.csv This file contains the following environmental parameters: * X : the row numbers ; * ID_unique : a unique ID number corresponding to the trawls ; * Year : the Year of each trawl ; * Quarter : the Quarter of each trawl ; * Ecoregion : the Ecoregion where each trawl has been done; * Survey : the name of the Survey ; * x_my_spatial_id : the longitude of the ICES rectangle where the trawl has been done ; * y_my_spatial_id : the latitude of the ICES rectangle where the trawl has been done ; * my_spatial_id : an ID for the ICES rectangle where the trawl has been done ; * depth : the bottom depth (meters) ; * depth_span : the bottom depth variability (maximum depth of the ICES cell - minimum depth) (meters) ; * chloro_mea: the mean chlorophyll-a concentration (mg/m³) ; * mlotst_mea : the mean mixed layer depth (meters) ; * oxy_bottom_mea : the mean bottom dissolved oxygen (umol/l) ; * oxy_surf_mea : the mean surface dissolved oxygen (umol/l) ; * temp_bottom_mea : the mean bottom temperature (°C) ; * temp_surf_mea : the mean surface temperature (°C) ; * curr_surf_mea : the mean surface current strength (m/s) ; * curr_bottom_mea : the mean bottom current strength (m/s) ; * sal_surf_mea : the mean surface salinity (PSU) ; * chloro_std : the standard deviation of chlorophyll-a concentration (mg/m³) ; * mlotst_std : the standard deviation of mixed layer depth (meters) ; * oxy_bottom_std : the standard deviation of bottom dissolved oxygen (umol/l) ; * oxy_surf_std : the standard deviation of surface dissolved oxygen (umol/l) ; * temp_bottom_std : the standard deviation of bottom temperature (°C) ; * temp_surf_std : the standard deviation of surface temperature (°C) ; * curr_surf_std : the standard deviation of surface current strength (m/s) ; * curr_bottom_std : the standard deviation of bottom current strength (m/s) ; * sal_surf_std : the standard deviation of surface salinity (PSU). ## Raw Data sources ### Biological data Trawls content is publicly available for the North East Atlantic (DATRAS database). Mediterranean data (MEDITS database) are available upon request to Maritime Affairs and Fisheries (MARE DATACOLLECTIONFRAMEWORK). The project uses the following surveys: | Survey Code | Survey name | Area | Period | References | | :---------- | :----------------------------------------------------- | :------------------------------------- | :-------: | :--------: | | BITS | Baltic International Trawl Survey | Baltic Sea | 1994-2019 | 4 | | BTS | Beam Trawl Survey | Celtic Sea; English Channel; North Sea | 1997-2019 | 7 | | BTS-VIII | Beam Trawl Survey – Bay of Biscay | Bay of Biscay | 2011-2019 | 7 | | DWS | Deepwater Survey | Irish Sea | 2006-2007 | 8 | | DYFS | Inshore Beam Trawl Survey | Southern North Sea | 2002-2019 | 7 | | EVHOE | French Southern Atlantic Bottom trawl Survey | Bay of Biscay and Celtic Sea | 2003-2019 | 1 | | FR-CGFS | French Channel ground Survey | English Channel | 1997-2019 | 2 | | IE-IAMS | Irish Anglerfish and megrim Survey | Scottish rockall and Irish Sea | 2016-2019 | 2 | | IE-IGFS | Irish Groundfish | Ireland Shelf Sea | 2003-2019 | 2 | | MEDITS | International bottom trawl survey in the Mediterranean | Mediterranean Sea | 1994-2018 | 9 | | NIGFS | Northern Ireland Groundfish Survey | Irish Sea | 2009-2019 | 2 | | NS-IBTS | North Sea International Bottom Trawl Survey | North Sea | 1997-2019 | 2 | | PT-IBTS | Portuguese International Bottom Trawl Survey | Portugal Shelf Sea | 2003-2017 | 2 | | ROCKALL | Scottish Rockall Survey (until 2010) | Rockall plateau | 2003-2009 | 2 | | SCOROC | Scottish Rockall Survey (from 2011) | Scottish plateau | 2011-2019 | 2 | | SCOWCGFS | Scottish West Coast Groundfish Survey | Scottish west coast | 2011-2019 | 2 | | SNS | Sole Net Survey | Southern North Sea | 2002-2019 | 7 | | SP-ARSA | Spanish Gulf of Cadiz Bottom Trawl Survey | Spain | 2003-2019 | 6 | | SP-NORTH | Spanish North Bottom Trawl Survey | North of Spain | 2003-2019 | 2 | | SP-PORC | Spanish Porcupine Bottom Trawl Survey | Irish Sea | 2003-2019 | 5 | | SWC-IBTS | Scottish West Coast International Bottom Trawl Survey | Scotland Shelf Sea | 1999-2010 | 2 | ### Trait data The complete traits data table is available upon request. It is a combination of the publicly available PANGAEA database, Fishbase information, and inference based on the FISHLIFE project. ### Environmental variables The data used are all publicly available on the Copernicus website. ### Fishing data The data used are all publicly available on the Global Fishing Watch website. ## Recommended Citation Please use the following citation: Receveur, A., Leprieur F., Ellingsen K., Keith D., Kleisner K., McLean M., Mérigot B., Mills K., Mouillot D., Rufino M., Trindade-Santos I., Van Hoey G., Albouy C., Auber A. Data for “Long-term changes in taxonomic and functional composition of European marine fish communities.” Dryad Digital Repository. (2024). doi.org/10.5061/dryad.x69p8czsj ## Acknowledgments This research is a product of the MAESTRO group funded by the synthesis center CESAB of the French Foundation for Research on Biodiversity (FRB). We thank France Filière Pêche (FFP) who founded the MAESTRO project. We also warmly thank all those who have contributed in any way to the scientific surveys and data collection/provision (European Institutions and scientists implicated in DATRAS-BTS, MEDITS, and DCF). ## References 1. ICES. The EVHOE survey (France). ICES Documents. (1997). Available at: https://archimer.ifremer.fr/doc/00036/14707/12013.pdf 2. ICES. Manual of the IBTS North Eastern Atlantic Surveys. Series of ICES Survey Protocols SISP 15 (2017). doi:10.17895/ices.pub.3519 3. ICES. Manual for the International Bottom Trawl Surveys Revision VIII. Series of ICES Survey Protocols SISP 10 - IBTS IX. (2015). 4. https://ices-library.figshare.com/articles/report/SISP_7_-*Manual_for_the_Baltic_International_Trawl_Surveys_BITS*/19050986 5. https://gis.ices.dk/geonetwork/srv/api/records/ce94a257-c8b3-44f7-9fd0-6bd7449ce073 6. http://ices.dk/sites/pub/CM%20Doccuments/2002/D/D0302A.pdf 7. https://ices-library.figshare.com/articles/report/SISP_14_-*Manual_for_the_Offshore_Beam_Trawl_Surveys_WGBEAM*/19051328 8. https://gis.ices.dk/geonetwork/srv/api/records/936b4fb7-9baa-4dbc-abd0-b1b7bda16406 9. https://archimer.ifremer.fr/doc/00117/22783/20585.pdf Evidence of large-scale biodiversity degradation in marine ecosystems has been reported worldwide, yet most research has focused on few species of interest or on limited spatiotemporal scales. Here we assessed the spatial and temporal changes in the taxonomic and functional composition of fish communities in European seas over the last 25 years (1994-2019). We then explored how these community changes were linked to environmental gradients and fishing pressure. We show that the spatial variation in fish species composition is more than two times higher than the temporal variation, with a marked spatial continuum in taxonomic composition and a more homogenous pattern in functional composition. The regions warming the fastest are experiencing an increasing dominance and total abundance of r-strategy fish species (lower age of maturity). Conversely, regions warming more slowly show an increasing dominance and total abundance of K-strategy species (high trophic level and late reproduction). Among the considered environmental variables, sea surface temperature, surface salinity, and chlorophyll-a most consistently influenced communities’ spatial patterns, while bottom temperature and oxygen had the most consistent influence on temporal patterns. Changes in communities’ functional composition were more closely related to environmental conditions than taxonomic changes. Our study demonstrates the importance of integrating community-level species traits across multi-decadal scales and across a large region to better capture and understand ecosystem-wide responses and provides a different lens on community dynamics that could be used to support sustainable fisheries management.

Dataset associated with the publication "Absolute Sustainability Assessment of Flue Gas Valorization to Ammonia and Synthetic Natural Gas" by Sebastiano C. D'Angelo, Julian Mache, and Gonzalo Guillén-Gosálbez, available at https://doi.org/10.1021/acssuschemeng.3c05246. The dataset includes the numeric data required to plot all the figures embedded in the main manuscript and in the Electronic Supplementary Information (ESI). The dataset contains two Excel files with the raw numerical data and two pieces of code to generate the associated plots included in the publication: Impacts_InputFile_v1: numerical values associated with the total share of safe operating space for all the assessed control variables of the seven planetary boundaries quantified in the study, for all the considered scenarios. The results are presented for the two different downscaling approaches considered in the study. The ReCiPe 2016 (hierarchist approach) midpoint and endpoint impacts for all the scenarios are here reported, as well. The single sheets are organized as follows: LCI: tables with life cycle inventories for all the reported scenarios. PB-LCIA: life cycle impact assessment results for the quantified planetary boundaries, using the nonegalitarian downscaling. ReCiPe16-MP: life cycle impact assessment results for the ReCiPe 2016 midpoints. ReCiPe16-EP: life cycle impact assessment results for the ReCiPe 2016 endpoints. Results_NotAggr: life cycle impact assessment results for all the quantified planetary boundaries, with full breakdown, using the nonegalitarian downscaling. Results_NotAggr_PopDS: life cycle impact assessment results for all the quantified planetary boundaries, with full breakdown, using the egalitarian downscaling. Results-NotAggr_ReCiPe16-MP: life cycle impact assessment results for the ReCiPe 2016 midpoints, with full breakdown. Results-NotAggr_ReCiPe16-EP: life cycle impact assessment results for the ReCiPe endpoints, with full breakdown. Environmental_plots_v1: code to generate plots associated with environmental results. Note that the path in the first jupyter cell needs to be adjusted to match the user's local working folder. Economics_InputFile_v1: numerical values associated with the breakdown of the economic impacts reported in the main manuscript and in the Supplementary Information, for all the assessed scenarios. The single sheets are organized as follows: Revenue: calculations to determine the revenues from (synthetic) natural gas and ammonia. CAPEX: breakdown of capital expenditure for all the components of all the investigated scenarios. OPEX: breakdown of operating expenditure for all the components of all the investigated scenarios. LCA: global warming impacts of the investigated scenarios (used to calculate the carbon abatement cost). Wind_CapacityFactors: wind capacity factors used for the economic calculations. Economic_plots_v1: code to generate plots associated with economic results. Note that the path in the first jupyter cell needs to be adjusted to match the user's local working folder.

General description of wind turbine: The ETH owned wind turbine is Aventa AV-7, manufactured by Aventa AG in Switzerland and was commissioned in December 2002. The turbine is operated via a belt-driven generator and a frequency converter with a variable speed drive. The rated power of the Aventa AV-7 is 7 kW, beginning production at a wind speed of 2 m/s and having a cut-off speed of 14 m/s. The rotor diameter is 12.8 m with 3 rotor blades, and a hub height is 18m. The maximum rotational speed of the turbine is 63 rpm. The tower is a tubular steel-reinforced concrete structure, supported on concrete foundation, while the blades are made of glassfiber with a tubular steel main-spar. The turbine is regulated via a variable-speed and variable pitch control system. Location of site: The wind turbine is located in Taggenberg, about 5 km from the city centre of Winterthur, Switzerland. This site is easily accessible by public transport and on foot with direct road access right next to the turbine. This prime location reduces the cost of site visits and allows for frequent personal monitoring of the site when test equipment is installed. The coordinates of the site are: 47°31'12.2"N 8°40'55.7"E. Control and measurement systems and signals: The turbine is regulated via a variable-speed and collective variable pitch control system. SHM Motivation: Designed and commissioned in 2002, the Aventa wind turbine in Winterthur is soon reaching its end of design lifetime. In order to assess the various techniques of predicting the remaining useful lifetime, a Structural Health Monitoring (SHM) campaign was implemented by ETH Zurich. The monitoring campaign started in 2020, and is still ongoing. In addition, the setup is used as a research platform on topics such as system identification, operational modal analysis, faults/damage detection and classification. We analyze the influence of operational and environmental conditions on the modal parameters and to further infer Performance Indicators (PIs) for assessing structural behavior in terms of deterioration processes. Data Description: The tower and nacelle have been instrumented with 11 accelerometers distributed along the length of the tower, nacelle main frame, main bearing and generator. Two full bridge strain gauges are installed on the concrete tower based measuring fore-aft and side-side strain (and can be converted to bending moments) – all acceleration and strain signals sampled at 200Hz. Temperature and humidity are measured at the tower base – 1Hz data. In additional we are collecting operational performance data (SCADA), namely: wind speed, nacelle yaw orientation, rotor RPM, power output and turbine status – SCADA signals are sampled at 10Hz. See appendix for further details of the sensors layout. The measurements/instrumentation setup, type and layout is provided in the pdf files. The data: the data is provided in zip files corresponding to four use-cases as follows: Normal operation data for system identification Aerodynamic imbalance on one blade Rotor icing event Failure of the flexible coupling of the linear drive of the collective pitch system The data for each of the four uses-cases is organized in zip files. The content of each zip file is as follows: Time-series data in HDF5 format Metadata: Turbine specification (Aventa-AV-7.json and Aventa-AV-7.yaml) Sensor specification (Aventa_sensors.json ) Unstructured description of the Aventa Turbine and the installed sensors (Aventa_Sensors_Specs.xlsx) Semantic artifacts: WindIO Wind Turbine YAML schema describing turbine specifications (IEAontology_schema.yaml) Sensor specification JSON schema (sensors_schema.json) Media: Pictures of leading edge roughness and a clip of wind turbine operation Code: Jupyter notebook containing example code to load metadata from JSON and data from HDF5 files (example.ipynb) Additional data is available upon request, please contact: Prof. Dr. Eleni Chatzi (chatzi@ibk.baug.ethz.ch) Dr. Imad Abdallah (ai@rtdt.ai , abdallah@ibk.baug.ethz.ch) For further details or questions, please contact: Prof. Dr. Eleni Chatzi Chair of Structural Mechanics & Monitoring ETH Zürich http://www.chatzi.ibk.ethz.ch/

Dataset associated with the publication "The role of hydrogen in heavy transport to operate within planetary boundaries" by Antonio Valente, Victor Tulus, Galán-Martín, Mark A. J. Huijbregts, and Gonzalo Guillén-Gosálbez, available at https://doi.org/10.1039/D1SE00790D. The dataset includes the numeric data associated with the plots described in the main manuscript, as well as the tables presented in the main manuscript converted in a machine-readable format. The structure of the dataset is here elucidated sheet by sheet: Tables: tables 3 and 4, as reported in the main manuscript, with evolution considered for the main technical parameters and the values of the parameters used in the baseline, best and worst scenario. Plots: numerical values associated with figures 2, 3, and 4, as reported in the main manuscript.

This dataset contains the underlying data for the following publication: Song, L., Lieu, J., Nikas, A., Arsenopoulos, A., Vasileiou, G., & Doukas, H. (2020). Contested energy futures, conflicted rewards? Examining low-carbon transition risks and governance dynamics in China's built environment. Energy Research & Social Science, 59, 101306., https://doi.org/10.1016/j.erss.2019.101306. Full details of methods used to create the dataset and provided within this publication.

Input data and model results of Huss&Förster (2019), L'avancée et le recul des glaciers pendant le Petit Age Glaciaire. Actes du colloque du Giétro, 14./15.juin, 2018. In: Annales Valaisannes, publication of the SHVR. Refer to file header for the description of all variables.