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# Heatwave grazing kelp microbes sequences [https://doi.org/10.5061/dryad.vhhmgqns7](https://doi.org/10.5061/dryad.vhhmgqns7) We experimentally simulated ocean warming and marine heatwaves (MHWs) to quantify effects on two dominant temperate seaweed species and their microbiota, as well as grazing by a tropical herbivore. The kelp *Ecklonia radiata*’s microbiota in sustained warming and MHW treatments were enriched with microorganisms associated with seaweed disease and tissue degradation. In contrast, the fucoid *Sargassum linearifolium*’s microbiota was unaffected by temperature\*.\* Consumption by the tropical sea-urchin *Tripneustes gratilla* was greater on *Ecklonia* where the microbiota had been altered by higher temperatures, while *Sargassum*’s consumption was unaffected. Elemental traits (carbon, nitrogen), chemical defences (phenolics) and tissue bleaching of both seaweeds were generally unaffected by temperature. ## Description of the data and file structure Juvenile *Ecklonia radiata* (length \~15cm; N=140) and *Sargassum linearifolium* (length \~10cm; N=140) were collected haphazardly (>2m apart) at Cronulla rocky reef, Sydney, Australia. We exposed seaweeds to one of four temperature profiles over seven weeks: Ambient, Warming, marine heatwave MHW, MHW variable. After seven weeks of exposure to temperature treatments, a subset of individuals from each species/temperature treatment (*Ecklonia*: n=4-6; *Sargassum*: n=3) were randomly selected. Sterile cotton swabs were used to sample microbiota on algal surfaces, with the same area (20cm2) and swabbing time (30s) sampled for all individuals. Swabs were immediately stored in liquid nitrogen and transported to the University of New South Wales (UNSW, Sydney) and kept at -80°C until DNA extraction. DNA was extracted from swabs using the DNeasy PowerSoil Kit (Qiagen) and amplified using Polymerase Chain Reaction (PCR) primers 341F (5’-CCTACGGGNGGCWGCAG-3’) and 785R (5’-GACTACHVGGGTATCTAATCC-3’), targeting the 16S rRNA gene V3-V4 regions (bacteria and archaea), and were sequenced with a 2x250bp MiSeq reagent kit v2 on the Illumina MiSeq2000 Platform. The range-expansion of tropical herbivores due to ocean warming can profoundly alter temperate reef communities by overgrazing the seaweed forests that underpin them. Such ecological interactions may be mediated by changes to seaweed-associated microbiota in response to warming, but empirical evidence demonstrating this is rare. We experimentally simulated ocean warming and marine heatwaves (MHWs) to quantify effects on two dominant temperate seaweed species and their microbiota, as well as grazing by a tropical herbivore. The kelp Ecklonia radiata’s microbiotain sustained warming and MHW treatments were enriched with microorganisms associated with seaweed disease and tissue degradation. In contrast, the fucoid Sargassum linearifolium’s microbiota was unaffected by temperature. Consumption by the tropical sea-urchin Tripneustes gratilla was greater on Ecklonia where the microbiota had been altered by higher temperatures, while Sargassum’s consumption was unaffected. Elemental traits (carbon, nitrogen), chemical defences (phenolics) and tissue bleaching of both seaweeds were generally unaffected by temperature. Effects of warming and MHWs on seaweed holobionts (host plus its microbiota) are likely species-specific. The effect of increased temperature on Ecklonia’s microbiota and subsequent increased consumption suggest that changes to kelp microbiota may underpin kelp-herbivore interactions, providing novel insights into potential mechanisms driving change in species’ interactions in warming oceans.

This dataset and codebook correspond to the initial round of survey data gathered in Latvia in 2022, 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. In the first round of the survey, we recruited a representative sample of approximately 2000 households in each country, taking into account both the individual and household perspectives. The survey includes a quantitative assessment of the carbon footprint in various domains of life, such as housing, mobility, and diet. In addition to this, the survey also measures 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.

Global change has dramatic impacts on grassland diversity. However, little is known about how fast species can adapt to diversity loss and how this affects their responses to global change. Here, we performed a common garden experiment testing whether plant responses to global change are influenced by their selection history and the conditioning history of soil at different plant diversity levels. Using seeds of four grass species and soil samples from a 14-year-old biodiversity experiment, we grew the offspring of the plants either in their own soil or in soil of a different community, and exposed them either to drought, increased nitrogen input, or a combination of both. Under nitrogen addition, offspring of plants selected at high diversity produced more biomass than those selected at low diversity, while drought neutralized differences in biomass production. Moreover, under the influence of global change drivers, soil history, and to a lesser extent plant history, had species-specific effects on trait expression. Our results show that plant diversity modulates plant-soil interactions and growth strategies of plants, which in turn affects plant eco-evolutionary pathways. How this change affects species' response to global change and whether this can cause a feedback loop should be investigated in more detail in future studies.

Data and R code for "Global change drives modern plankton communities away from pre-industrial state" by Lukas Jonkers, Helmut Hillebrandt and Michal Kucera (https://doi.org/10.1038/s41586-019-1230-3). Compare planktonic foraminifera species assemblages from sediments and sediment traps. Scripts written by Lukas Jonkers DATA SOURCES * HadISST: Rayner, N. A. et al. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. Journal of Geophysical Research: Atmospheres 108, doi:10.1029/2002JD002670 (2003). * ERSST v5: Huang, B. et al. NOAA Extended Reconstructed Sea Surface Temperature (ERSST), Version 5. Monthly mean. NOAA National Centers for Environmental Information. doi:10.7289/V5T72FNM. Access date: 14 Sep 2018. (2017). * sediment assemblages: Siccha, M. & Kucera, M. ForCenS, a curated database of planktonic foraminifera census counts in marine surface sediment samples. Scientific Data 4, 170109, doi:10.1038/sdata.2017.109 (2017). * sediment traps: citations provided in data files DATA 1. Planktonic foraminifera shell flux time series 1.1. all data: dat_sel.RDS 1.2. time series with >125 and >150 micron data: dat_small.RDS 1.3. shell flux data in csv format: shell_flux_data.csv 2. ForCenS core top sediment assemblages 2.1. all data (excluding duplicates and samples with incomplete taxonomy): forcens_trimmed_compare.RDS 2.2. all data, split by region: species_domains_compare.RDS 2.3. indices of samples: domain_indeces_compare.RDS 3. SST 3.1. average SST for each sample in ForCenS for 1870-1899 period based on HadISST: forcens_HadSST_1870-1899.RDS 3.2. average SST for each sample in ForCenS for 1854-1883 period based on ERSST v5: forcens_ERSST_1854-1883.RDS 3.3. average SST for each sediment trap site for 1870-1899 period based on HadISST: traps_HadSST_1870-1899.RDS 3.4. average SST for each sediment trap site for 1854-1883 period based on ERSST v5: traps_ERSST_1854-1883.RDS 3.5. average SST for each sediment trap site for deployment period based on HadISST: traps_HadSST_period.RDS 3.6. average SST for each sediment trap site for deployment period based on ERSST v5: traps_ERSST_period.RDS 3.7. linear SST trend between 1870 and 2015 based on HadISST: hadisst_trend_1870-2015.RDS 3.8. average SST for period of sediment trap observations: hadisst_mean_1978-2013.RDS CODE 1. get_ForCenS.R: selection of ForCenS data. Used to generate data 2.1-2.3 2. make_polygons.R: make circles with 100 km radius around trap and core tope sites used in 4 and 5 3. make_polygon_function.R: used by 2 4. extract_HadSST.R: extraction of data 3.1, 3.3, 3.5, 3.7, 3.8 5. extract_ERRSTv5.R: extraction of data 3.2, 3.4, 3.6 6. make_annual_assemblages.R: process shell flux time series (data 1.1 and 1.2) 7. make_annual_fluxes_function.R: used by 6 8. compare_trap_sed_publish.R: code to compare sediment trap and core top assemblages 9. compare_figs.R: code to create figures 10. maps_robinson.R: code to create maps

lncRNAs are at the core of many regulatory processes and have also been recognized to be involved in various complex diseases. They affect gene regulation through direct interactions with RNA, DNA or proteins. Accordingly, lncRNA structure is likely to be essential for their regulatory function. Point mutations, which manifest as SNPs (single nucleotide polymorphisms) in genome screens, can substantially alter their function and, subsequently, the expression of their downstream regulated genes. To test the effect of SNPs on structure, we investigated lncRNAs associated with dilated cardiomyopathy. Among 322 human candidate lncRNAs, we demonstrate first the significant association of an SNP located in lncRNA H19 using data from 1084 diseased and 751 control patients. H19 is generally highly expressed in the heart, with a complex expression pattern during heart development. Next, we used MFE (minimum free energy) folding to demonstrate a significant refolding in the secondary structure of this 861 nt long lncRNA. Since MFE folding may overlook the importance of sub-optimal structures, we showed that this refolding also manifests in the overall Boltzmann structure ensemble. There, the composition of structures is tremendously affected in their thermodynamic probabilities through the genetic variant. Finally, we confirmed these results experimentally, using SHAPE-Seq, corroborating that SNPs affecting such structures may explain hidden genetic variance not accounted for through genome wide association studies. Our results suggest that structural changes in lncRNAs, and lncRNA H19 in particular, affect regulatory processes and represent optimal targets for further in-depth studies probing their molecular interactions.

{"references": ["Deb, P., Knapp, D, Marquart, G., & Montegrossi, G., 2019. Thermal model of the Los Humeros super-hot geothermal system, Mexico (Version D6.6_Report_Local). Zenodo. https://doi.org/10.5281/zenodo.3723224", "Deb, P., Knapp, D., Marquart, G., & Clauser, C. ,2019. Thermal model of the Los Humeros super-hot geothermal system, Mexico (Version D6.3_Report_Regional). Zenodo, doi: 10.5281/zenodo.3719193", "Deb et al, 2019, Modeling natural steady-state of super hot geothermal rerservoir at Los Humeros, Mexico, European Geothermal Congress, 2019, The Hague, The Netherlands, doi: 10.5281/zenodo.3719198", "Calcagno, P., Evanno, G., Trumpy, E., Guti\u00e9rrez-Negr\u00edn, L. C., Mac\u00edas, J. L., Carrasco-N\u00fa\u00f1ez, G., Liotta, D., 2018. Preliminary 3-D geological models of Los Humeros and Acoculco geothermal fields (Mexico) \u2013 H2020GEMex Project., Advances in Geosciences, 45, 321 \u2013 333.", "Calcagno, P., Trumpy, E., Guti\u00e9rrez-Negr\u00edn, L. C., Norini, G., Mac\u00edas, J. L., Carrasco-N\u00fa\u00f1ez, G., Liotta, D., Gardu\u00f1o-Monroy, V. H., Hersir, G. P., Vaessen, L., Evanno, G., Arango Galv\u00e1n, C.: Updating the 3D Geomodels of Los Humeros and Acoculco Geothermal Systems (Mexico) \u2013 H2020 GEMex Project, World Geothermal Congress proceedings, 2020 (submitted)", "B\u00e4r, K., Weydt, L., Deb, P., Kummerow, J.,Ferrero, A., Vagnon, F., Colombero, C., Chicco, J., Vinciguerra, S., Siegfried, R., Comina, C., Mandrone, G., Laciska, A., Kemp, S., Kilpatrick, A., Rochelle, C., Milsch, H., Vacha, D., Rushton, J., 2019. Comprehensive report on the rock and fluid samples and their physical properties in the Acoculco and Los Humeros regions. GEMex \u2013 H2020 project report D6.1, https://data.d4science.net/zVLc"]} The dataset contains 3D thermal model (format - .vtk and .h5) of Los Humeros geothermal system at a local scale (extent defined in Calcagno et al., 2018). The boundary conditions used for this thermal model are obtained from Scenario 3b of regional model discussed in EU Deliverable D6.3 (10.5281/zenodo.3723039) and D6.6 (10.5281/zenodo.3723224). Before using the results of the model, the user is advised to carefully read the model parameters, assumptions and uncertainties associated with the model as reported in Deliverable D6.3 and Deliverable D6.6. The .h5 file contains data and attributes (quantity, unit) The .vtk files contains the following information x, y, z UTM coordinates (m) temp Temperature (°C) head Hydraulic head (m) pres Pressure (MPa) por Porosity (-) q Heat flow (W m-2) kx, ky, kz Permeability (m2) vx, vy, vz Specific discharge or Darcy velocity (m s-1) lx, ly, lz Thermal conductivity (W m-1 K-1) Additional information regarding the model is presented in the PDF document. Incorrect Upload. Latest model will be uploaded soon

The ozone (O3) susceptibility of cv. Williams was tested in nine independently controlled and monitored open-top chambers (OTC) built at the UK University of Exeter’s TropOz Research facility located at James Cook University’s Environmental Research Complex (ERC) on the Nguma-bada campus in far-north Queensland, Australia (www.tropoz.org). The plants (27 cv. Williams) were grown under O3 fumigation in OTCs for about three months. At the end of the O3 fumigation period and when the plants were on average 97 cm in height, two leaves were collected from every plant, specifically the third most recently expanded and therefore newly mature leaf (new leaf) and the eighth-most recently expanded (old leaf) both new and old leaves having fully developed under O3 fumigation. From each leaf, two mid-lamina leaf sections ~300 cm2 from both sides of the midrib were taken and measured for total fresh weight. After weighing, and scanning to determine area, one section was, wrapped in tinfoil, snap-frozen in liquid N2 and stored at –20°C before freeze-drying for biochemical analyses; the other was dried at 70 °C to account for leaf mass lost to sampling. At the end of the experiment, leaves, midrib, pseudostem, corm and small suckers were harvested separately, and dried in an oven at 70 °C until constant weight for biomass determination. For all lamina samples collected from the OTC experiment, leaf mass per area (LMA) was calculated using the leaf dry mass (DM) obtained on freeze-dried samples and the leaf area (LA) determined by image analyser software (Image-J, NIH, Bethesda, Maryland, USA). LMA was calculated as LMA= DM/LA in units of g m−2. Freeze-dried leaf samples were subsequently ground into fine powder (Rocklabs Bench Top Ring Mill) and stored in airtight vials until determination of leaf biochemistry and stable isotope concentrations. Powdered leaf samples (~30 mg) were extracted in cold 50% acetone (Ritmejerytė et al. 2019). Total antioxidant capacity (TAC) was determined in the leaf extract by the ferric reducing antioxidant power (FRAP) assay. The assay was carried out according to Benzie and Strain (1996) with some modifications. Ascorbic acid was used as the standard and TAC was expressed as ascorbic acid equivalents (mg AAE g−1 dry weight). Total phenolic content (TPC) was measured in the same leaf extract by the Folin–Ciocalteau method (Cork and Krockenberger 1991; Singleton and Rossi 1965) with some modifications (Ritmejerytė et al. 2019). Gallic acid was used as a standard and TPC was expressed as Gallic acid equivalents (mg GAE g–1 dry weight). The carbon stable isotope ratio (δ13C, ‰) and weight percent (%C) were determined using a Costech Elemental Analyser fitted with a zero-blank auto-sampler coupled via a ConFloIV to a ThermoFinnigan DeltaVPLUS using Continuous-Flow Isotope Ratio Mass Spectrometry (EA-IRMS) at James Cook University’s Advanced Analytical Centre. Stable isotope results are reported as per mil (‰) deviations from the VPDB reference. Precisions (S.D.) on internal standards were better than 0.1‰ for δ13C. The iWUE was calculated from δ13C according to the equation of Farquhar et al. (1989). Environmental variables such as air temperature (T), air relative humidity (RH), shortwave radiation and photosynthetically active radiation (PAR) were monitored using a single meteorological monitoring station (Campbell Scientific, Logan, UT, USA) established in the central OTC. Hourly values of O3 and meteorological conditions were measured for the DO3SE (Deposition of O3 for Stomatal Exchange) model. The DO3SE model was used to estimate the O3 flux into leaves. References Benzie IF, Strain JJ (1996). The ferric reducing ability of plasma (FRAP) as a measure of “antioxidant power”: the FRAP assay. Analytical biochemistry, 239(1), 70-76. Cork SJ, Krockenberger AK (1991). Methods and pitfalls of extracting condensed tannins and other phenolics from plants: insights from investigations on Eucalyptus leaves. Journal of chemical ecology, 17(1), 123-134. Farquhar GD, Ehleringer JR, Hubick KT (1989). Carbon isotope discrimination and photosynthesis. Annual review of plant biology, 40(1), 503-537. Ritmejerytė E, Boughton BA, Bayly MJ, Miller RE (2019). Divergent responses of above-and below-ground chemical defence to nitrogen and phosphorus supply in waratahs (Telopea speciosissima). Functional Plant Biology, 46(12), 1134-1145. Singleton VL, Rossi JA (1965). Colorimetry of total phenolics with phosphomolybdic-phosphotungstic acid reagents. American Journal of Enology and Viticulture, 16(3), 144-158.  # Examining ozone susceptibility in the genus Musa (bananas) [https://doi.org/10.5061/dryad.fbg79cp26](https://doi.org/10.5061/dryad.fbg79cp26) The ozone (O3) susceptibility of cv. Williams was tested in nine independently controlled and monitored open top chambers (OTC) built at the UK University of Exeter’s TropOz Research facility located at James Cook University’s Environmental Research Complex (ERC) on the Nguma-bada campus in far-north Queensland, Australia ([www.tropoz.org](https://tropoz.org/)). The plants (27 cv. Williams) were grown under O3 fumigation in OTCs for about three months. At the end of the O3 fumigation period, two leaves were collected from every plant, specifically the third most recently expanded and therefore newly mature leaf (new leaf) and the eighth-most recently expanded (old leaf) both new and old leaves having fully developed under O3 fumigation from every plant for the determination of leaf functional traits such as leaf mass per area (LMA), intrinsic water-use-efficiency (iWUE), total antioxidant capacity (TAC), and total phenolic contents (TPC). LMA was calculated using the leaf dry mass (DM) obtained on freeze-dried samples and the leaf area (LA) determined by image analyser software (Image-J, NIH, [Bethesda, Maryland](https://en.wikipedia.org/wiki/Bethesda,_Maryland), USA). LMA was calculated as LMA= DM/LA in units of g m−2. TAC was determined in the leaf extract by the ferric reducing antioxidant power (FRAP) assay. TAC was expressed as ascorbic acid equivalents (mg AAE g−1 dry weight). Ascorbic acid was used as the standard. TPC was measured in the same leaf extract by the Folin–Ciocalteau method with some modifications. Gallic acid was used as a standard and TPC was expressed as Gallic acid equivalents (mg GAE g–1 dry weight). The carbon stable isotope ratio (δ13C, ‰) was determined using a Costech Elemental Analyser fitted with a zero-blank auto-sampler coupled via a ConFloIV to a ThermoFinnigan DeltaVPLUS using Continuous-Flow Isotope Ratio Mass Spectrometry (EA-IRMS) at James Cook University’s Advanced Analytical Centre. The δ13C values were used to calculate the iWUE. At the end of the experiment, leaves, midrib, pseudostem, corm and small suckers were harvested separately, and dried in an oven at 70 °C until constant weight for biomass determination. Values (dataset 1) represent OTC of three plants (n=3). Environmental variables such as air temperature (T), air relative humidity (RH), shortwave radiation and photosynthetically active radiation (PAR) were monitored using a single meteorological monitoring station (Campbell Scientific, Logan, UT, USA) established in the central OTC. Hourly values of O3 concentration and meteorological conditions were measured for the DO3SE (Deposition of O3 for Stomatal Exchange) model. The DO3SE model was used to estimate the O3 flux into leaves. ## Description of the data and file structure Dataset was uploaded in three different excel sheets, Data1, Data2 and Data3 with their metadata (Data1\_Metadata, Data2\_Metadata, and Data3\_Metadata). Metadata sheets represents the parameter names, description, and units. Data1 sheet contains open top chamber averages data. Data2 sheet contains environmental variables during experimental period. Data3 sheet contains hourly values of O3 concentration and meteorological conditions that were measured for the DO3SE (Deposition of O3 for Stomatal Exchange) model. ## Sharing/Access information Data was produced from our own experimental open top chambers (OTC) built at the UK University of Exeter’s TropOz Research facility located at James Cook University’s Environmental Research Complex (ERC) on the Nguma-bada campus in far-north Queensland, Australia ([www.tropoz.org](https://tropoz.org/)). ## Code/Software Tropospheric ozone (O3) is a global air pollutant that adversely affects plant growth and productivity. While the impacts of O3 have previously been examined for some tropical commodity crops, no information is available for the pantropical crop, banana (Musa spp.). In this study, we exposed Australia’s major banana cultivar, Williams, to a range of [O3] in open-top chambers. In addition, we examined 46 diverse Musa lines growing in a common garden for variation in traits that are hypothesized to shape responses to O3: leaf mass per area, intrinsic water-use-efficiency, and total antioxidant capacity. Banana cv. Williams showed substantial susceptibility to O3. Combined our results from open-top chambers and common garden conditions suggest a substantial risk of O3 to banana production and food security throughout the tropics.

General Description The simulated process describes the production of hinges. It begins with a steel coil which is split into steel sheets which are heated, formed and coated before being split into the male and female parts. Subsequently, both components are assembled with a steel pin and packed. The process is describes using events and objects This is an artificial event log according to the OCEL 2.0 Standard defined in Berti et al. simulated using an extended version of CPN-Tools with a connector to climatiq API. The extension to CPN-Tools and the event log simulation are results of the Bachelor Thesis project of Marco Heinisch at the Chair for Process and Data Science at RWTH Aachen University. Process Overview In this example scenario, hinges are produced in a German facility. The production process is divided into three workstations, all supervised by a single worker. The production line is started by this employee workstations for workstations every morning at 7:30. The employee pauses work for a lunch break at 12:00 pm and then continues working from 1:00 pm until 3:00 pm. The production process starts with two primary inputs: steel coils and steel pins. The steel pins, also known as steel rods, are each 3.0 cm long and are used to hold the leaves together. The steel coils hold a rolled steel strip that is approximately 0.3 centimeters thick and 3.0 centimeters wide. This steel strip is then processed into hinge leaves through the following steps. In the first operation of our model, one steel coil at a time is processed at the first workstation. Each coil is continuously cut into steel sheets that are 3 cm in length. As these strips are cut, we assign each resulting steel sheet an identification number. This process continues until there is only an unusable steel remainder of the steel coil, which we do not track further in this process. This remainder needs to be classified as waste in preprocessing. The amount of waste created during the production of rolled steel strips varies depending on the initial length and could be optimized. The cut steel sheets are handled in a line and heat-treated in a gas oven. A different oven technology could reduce impacts. Then, each 3.0 cm steel sheet is rolled on one side to form a barrel for the insertion of hinge pins. This operation is performed using a hydraulic press powered by electricity. In our model, we refer to these produced parts as FormedParts. Finally, in a very energy intense step, plasma coating is conducted using a specialized machine. The FormedParts are automatically collected in a transport carriage. If the carriage reaches full capacity, an alert signals the operator to manually move the batch of FormedParts to the second workstation. At the second workstation, FormedParts are shaped into alternating male and female hinge leaves (MalePart and FemalePart) using a laser cutting machine. This is achievedby cutting out specific sections from each part. Notably, the method generates more waste compared to an alternative process where the male and female parts are directly cut out from the coil and then shaped further. The metal waste containing the coat is hypothetically not recyclable, which is reflected in a separate impact indicator. The waste created during this operation is not recorded, however, the weight of the workpiece object before and after the material removal is recorded. In the next step, the worker manually checks both FemalePart and MalePart for quality criteria, which is mass, representing various possible measurable attributes, including geometrics and material properties. If everything is within tolerance, the parts are placed on a moving band to the third workstation. If not, the part represents waste, which again is to be quantified in a preprocessing step. At the next station, an assembling machine uses one MalePart, one FemalePart, and one SteelPin to create a Hinge. The material origin of a hinge or its parts could potentially be discovered. A set of 10 hinges is automatically buffered and placed into a cardboard box by a packaging machine. Sustainability Data The event log is enhanced with sustainability-related attributes for objects and events. These attributes can be identified by their naming structure as they begin with either i, p or s and include a unit in square brackets at the end of the attribute name structure, i.e., "i_electricity[kWh]". These additional attribute are classifies into three different categories of data: Impact Indicators ("i_"): contain information on all different impacts caused by an event, object, or process. Impact Indicators should be based on some sort of sustainability framework, such as ReCiPe2016 standard. In this event log, all relevant impact indicators considered in reviewed literature on manufacturing assessments are included according to the Climatiq-API specification to show the potential of automated assessment. Indicators further represent LCI-Items according to the LCA standard. However, to both indicate their relevance and a possible inability to get this data from the process directly some of the values read "??" as they have to be estimated using given impact paqrameters. Impact Parameters ("p_"): As data availability issues may lead to an inavailability to determine impact indicators directly, impact parameters can be included into the event log to support the estimation of impact indicators. These process-specific parameters include, e.g., material, mass, volume, geometric properties, and operation duration. Impact Scores ("s_"): Impact scores describe the overall effect of a system or event on the environment in a standardised manner. Multiple impact scores for different impact categories, such as climate change or toxicity, can be calculated and represented as impact scores. This process involves aggregating and weighting individual impact indicators according to a standardized methodology, such as ReCiPe2016. Climatic API (used for the simulation of the event log) uses values for impact indicators to return impact scores. General Properties An overview of log properties is given below. Property Value Event Types 11 Object Types 12 Events ~3850 Objects ~23700 Process Simulation Design Process Our result is a simplified hinge production line, modeled as a Colored Petri Net (CPN) in CPN Tools, a tool for editing and creating colored Petri nets. This model is an idealized representation of an exemplary manufacturing process. It is designed to examine and present the use case of SD in OCPM and does not realistically represent a specific manufacturing process in detail. The model’s design process was structured into four steps. First, the workpiece flow was outlined, identifying the sub-products used in hinge production.Next, control flow elements were incorporated, adding batching and queuing behaviors as well as quality control procedures. Sustainability data relevant to this example were integrated into events and objects following the sOCEL specification. Finally, the simulation of values such as waiting times and weights now includes randomness and deviations. Simulation Model The repository with the CPN-Tools model as well as the CPN-Tools extension including the connector to Climatiq API can be found in the following Repositiry: https://github.com/rwth-pads/sOCEL Further information on the simulated process, the simulation model and the CPN-Tools extension can be found in Marco's thesis entitled "Integrating Sustainability Data into Event Logs for Object-Centric Process Mining". Acknowledgements Funded under the Excellence Strategy of the Federal Government and the Länder. Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC-2023 Internet of Production - 390621612. We also thank the Alexander von Humboldt (AvH) Stiftung for supporting our research. Special thanks are also dedicated to Marco's Family and friends Lennart, Franziska, and Maxim for their support and valuable feedback.