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3,101 results for “historic”

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zenodo44/100

Soybean yield projections in Europe under historical (1981-2010) and future climate (2050-2059 and 2090-2099 for RCP4.5 and RCP8.5)

General information This dataset contains soybean yield projections in Europe under historical (1981-2010) and future climate with moderate (RCP 4.5) to intense (RCP 8.5) warming, up to the 2050s and 2090s time horizons. The data has been generated by Guilpart et al. (2022) Data-driven projections suggest large opportunities to improve Europe's soybean self-sufficiency under climate change, Nature Food. All details can be found in this paper. A brief summary is provided below. Summary of soybean yield projections methodology Yield projections have been performed using data-driven relationships between climate and soybean yield derived from machine-learning (Random Forest). The Random Forest model was trained using (i) the the global dataset of historical yields updated version (Iizumi et al. 2014a), which includes grid-wise soybean yields worldwide with the grid size of 1.125 degree over 1981-2010, and (ii)  the global retrospective meteorological forcing dataset tailored for agricultural application (GRASP, Iizumi et al. 2014b), which covers the period 1961–2010 at the same spatial resolution as yield data, i.e. a grid size of 1.125 degree. Time-detrended soybean yield data was related (using Random Forest) to 35 climate variables defined at a monthly time step over the seven months of the soybean growing season, plus the fraction of irrigated area, i.e. a total of 36 variables. The 35 climate variables are monthly mean daily minimum and maximum temperatures (Tmin and Tmax, degree Celsius), monthly total precipitation (rain, mm month-1), monthly mean daily total solar radiation (solar, MJ m-2 day-1), monthly mean air vapor pressure (VP, hPa). The fitted model showed high R² (higher than 0.9) and low RMSE (0.35 t ha-1) between observed and predicted yields based on cross-validation. Then, soybean yield projections under historical over whole Europe have been performed using the GRASP climate data, and yield projections under future climate have been performed using 16 climate change scenarios consisting of bias-corrected data of eight Global Circulation Models (GCM; GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC5, MIROC-ESM, MIROC-ESM-CHEM, MRI-CGCM3, and NorESM1-M, used in the Coupled Model Intercomparison phase 5 (CMIP5) and two Representative Concentration Pathways (RCPs; 4.5 and 8.5 W m-2). Soybean growing season used for projections is April to October. All projections assumed irrigated fraction equals to zero. Projections are shown only on agricultural area (cropland plus pasture), in the year 2000. Soybean yield is expressed in tons per hectare. Files description <ul> <li>RF_soybean_historical_GRASP_median_1981_2010.nc : random forest projections of soybean yield in Europe for the historical (1981-2010) period using GRASP climate data. This file contains the median yield (in tons per hectare) over 1981-2010.</li> <li>RF_soybean_rcp45_median_2050_2059.nc : random forest projections of soybean yield in Europe for the 2050-2059 time period under RCP4.5. This file contains the median yield (in tons per hectare) over 2050-2059 and the 8 GCMs.</li> <li>RF_soybean_rcp45_median_2090_2099.nc : random forest projections of soybean yield in Europe for the 2090-2099 time period under RCP4.5. This file contains the median yield (in tons per hectare) over 2090-2099 and the 8 GCMs.</li> <li>RF_soybean_rcp85_median_2050_2059.nc : random forest projections of soybean yield in Europe for the 2050-2059 time period under RCP8.5. This file contains the median yield (in tons per hectare) over 2050-2059 and the 8 GCMs.</li> <li>RF_soybean_rcp85_median_2090_2099.nc : random forest projections of soybean yield in Europe for the 2090-2099 time period under RCP8.5. This file contains the median yield (in tons per hectare) over 2090-2099 and the 8 GCMs.</li> </ul> References Guilpart N. et al. (2022) Data-driven projections suggest large opportunities to improve Europe's soybean self-sufficiency under climate change, Nature Food. Iizumi T. et al. (2014a) Historical changes in global yields: Major cereal and legume crops from 1982 to 2006. Glob. Ecol. Biogeogr. 23, 346–357. Iizumi T. et al. (2014b). A meteorological forcing data set for global crop modeling: Development, evaluation, and intercomparison. J. Geophys. Res. Atmos. Res. 119, 363–384.

opencc-by-4.0Feb 2022View details →

zenodo44/100

Historical Person Register, Tyrol 15th, 16th century

The dataset consists of a file in CSV format (UTF 8). The historical person register contains late medieval / ENHG person names within the Tyrolean mining documents Hs. 37 and Hs. 1587 (15th and 16th century) as well as a modern standardisation of the names. The persons received unique Identifiers. The names are also split into title, first name, last name, occupation and descriptor. Furthermore, the year is added, provenance if available, and alternate writings of the names within the text. The project “Text Mining Medieval Mining Texts” (2019-2022) processed two historical mining sources: “Verleihbuch der Rattenberger Bergrichter” ( Hs. 37, 1460-1463) and “Schwazer Berglehenbuch” (Hs. 1587, approx. 1515) stored by the Tyrolean Regional Archive, Innsbruck (Austria). The central research objective of T.M.M.M.T. is the extraction and representation of the legal relationships between people, claims and mines over space and time. Furthermore it deals with the semantically opening and visualisation of the montanistic network of two tyrolean mining regions. Citeable Transcripts are online available: Hs. 37 DOI: 10.5281/zenodo.6274562 Hs. 1587 DOI: 10.5281/zenodo.6274928 View also the facsimiles and transcripts on Mining Hub: https://transkribus.eu/r/mining-hub/#/ The research project (2019-2022) was carried out at the university of Innsbruck and funded by go!digital next generation programme of the Austrian Academy of Sciences.

opencc-by-4.0Feb 2022View details →

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Historical Place Gazetteer and Historical Mine Register, Tyrol 15th, 16th century

The dataset is split in two separated files in CSV format (UTF 8): 1) Historical place gazetteer: It contains historical tyrolean names of places (core region: districts Kufstein, Schwaz; Austria) within the mining documents Hs. 37 and Hs. 1587 and their modern equivalents enriched with unique Identifiers. Furthermore, Places were localised and georeferenced as far as current scientific data and knowledge permit. A final column shows corresponding IDs between Hs. 37 and Hs. 1587. 2) Historical mine register: The file contains historical  Tyrolean names of mines and pits (core region: district Kufsten, Schwaz) within the mining documents Hs. 37 and Hs. 1587 as well as a modern standardisation of the names. The mines also received unique Identifiers. The registers were generated by the research team of the project “Text Mining Medieval Mining Texts” (2019-2022). Two historical mining sources were processed: “Verleihbuch der Rattenberger Bergrichter” ( Hs. 37, 1460-1463) and “Schwazer Berglehenbuch” (Hs. 1587, approx. 1515) stored by the Tyrolean Regional Archive, Innsbruck (Austria). The central research objective of T.M.M.M.T. is the extraction and representation of the legal relationships between people, claims and mines over space and time. Furthermore it deals with the semantically opening and visualisation of the montanistic network of two tyrolean mining regions. Citeable Transcripts are online available: Hs. 37 DOI: 10.5281/zenodo.6274562 Hs. 1587 DOI: 10.5281/zenodo.6274928 The research project (2019-2022) was carried out at the University of Innsbruck and funded by go!digital next generation programme of the Austrian Academy of Sciences.

opencc-by-4.0Feb 2022View details →

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Motion Capture Benchmark of Industrial Tasks for Ergonomic Assessment and European Historic Crafts

General Info: This benchmark provides motion capture (MoCap) files in .bvh form. The recordings were done in the span of May 2019 to January 2020 for the needs of the <a href="https://collaborate-project.eu/">CoLLaboratE</a> and <a href="http://www.mingei-project.eu/">MINGEI</a> H2020 projects funded by the European Commission. The tasks included are: <ul> <li>TV assembling</li> <li>Airplane component manufacturing</li> <li>High ergonomic hazard motions </li> <li>Silk-Weaving</li> <li>Glassblowing</li> <li>Mastic Cultivation</li> </ul> The TV assembly and airplane component manufacturing tasks were recorded in real-world conditions inside the factory during the actual production of the items. The high ergonomic hazard motions were recorded in a controlled lab environment and serve as baseline/prototype motions for ergonomic risk assessment. The silk-weaving, glassblowing, and mastic cultivation data sets were created, corresponding to movements performed by skilled craftsmen and mastic farmers. These data sets were produced in order to extract the expert's gestural knowledge and analyze their dexterity while doing their crafts. Naming Convention: All files in this benchmark follow a strict naming convention to allow for easier parsing by scripts. The names have a total of 12 or 13 characters that convey the following information: <ul> <li>The first three or fours characters label the recording session (e.g., LAB, PLN, GBBC, MCSN, etc.)</li> <li>The next three characters label the subject number (e.g., S01, S02, S03, etc.)</li> <li>The next three characters label the posture or gesture number (e.g., P01, P02, G01, G02, etc.)</li> <li>The final three characters label the repetition number (e.g., R01, R02, R03, etc.)</li> </ul> For example, LABS02P03R01 denotes a lab recording of the second subject, performing the third posture for the first time. Recording Sessions: There are six recording sessions in this benchmark, the ergonomic risk motion recorded in the lab (denoted as "LAB"), the construction of an airplane component (denoted as "PLN"), and the assembling and packaging of TVs (denoted as "TV*"), the silk weaving (denoted as "SW*"), glassblowing (denoted as "GB*"), and mastic cultivation (denoted as "MC*"). The postures are the following: LAB: <ul> <li>Standing: <ul> <li>P01: The subject stays in I-pose</li> <li>P02: The subject rotates his/her torso to the left as far the person can</li> <li>P03: The subject will laterally bend his/her torso to the left for 6 seconds</li> <li>P04: The subject bends more than 20° but less than 60°</li> <li>P05: The subject bends more than 20° but less than 60° while rotating and laterally bending the torso to the left</li> <li>P06: The subject stretches his/her arms, and bends forward more than 20° but less than 60° while rotating and laterally bending the torso to the left</li> <li>P07: The subject bends more than 60°</li> <li>P08: The subject bends more than 60° while rotating and laterally bending the torso to the left</li> <li>P09: The subject stretches his/her arms, and bends forward more than 60° while rotating and laterally bending the torso to the left</li> <li>P10: The subject upright, raises the elbows above the shoulder level with the forearms bent 90° (</li> <li>P11: The subject raises the elbows above the shoulder level with the forearms bent 90° while rotating and laterally bending the torso to the left</li> <li>P12: The subject raises the elbows above the shoulder level with the arms stretched while rotating and laterally bending the torso to the left</li> <li>P13: The subject upright, raises the hands above the head</li> <li>P14: The subject raises the hands above the head with the arms stretched while rotating and laterally bending the torso to the left</li> </ul> </li> <li>Sitting on a chair: <ul> <li>P15: The subject sits upright</li> <li>P16: The subject bends forward more than 60°</li> <li>P17: The subject bends forward more than 60° while rotating and laterally bending the torso to the left</li> <li>P18: The subject stretches the arms, and bends forward more than 60° while rotating and laterally bending the torso to the left</li> <li>P19: The subject raises the hands above the head with arms stretched</li> <li>P20: The subject raises the hands above the head with the arms stretched while rotating and laterally bending the torso to the left</li> </ul> </li> <li>Kneeling: <ul> <li>P21: The subject stays upright</li> <li>P22: The subject rotates the torso to the left as far he/she can</li> <li>P23: The subject will laterally bend the torso to the left for 6 seconds</li> <li>P24: The subject bends more than 60°</li> <li>P25: The subject bends more than 60° while rotating and laterally bending the torso to the left</li> <li>P26: The subject stretches the arms, and bends forward more than 60° while rotating and laterally bending the torso to the left</li> <li>P27: The subject upright, raises the elbows to the shoulder level with the arms stretched</li> <li>P28: The subject raises the elbows to the shoulder level with the arms stretched while rotating and laterally bending the torso to the left</li> </ul> </li> </ul> The TV assembling tasks are further divided. The subtasks are: packing the TVs on a stack for shipping (denoted as "TVP" for medium-sized TVs and "TVL" for larger TVs), placing assembling and placing electronic circuit boards on the chassis (denoted as "TVB"), and screwing the boards on the TV chassis (denoted as "TV_"). Each task is comprised of a number of postures.   TV Assembling: <ul> <li>Assembling the board and placing it on the TV chassis (TVB): <ul> <li>P01: Reaching high, above the shoulder level, to pick one component</li> <li>P02: Reaching low, below the knee level, to pick up the second component</li> <li>P03: Connecting the components and placing the board on the chassis to be screwed</li> </ul> </li> <li>Screwing an electrical circuit board on the TV chassis (TV_) : <ul> <li>P01: A screw is placed on a power tool and it is being screwed on the chassis. The process is repeated four times</li> </ul> </li> <li>Preparing TVs for Shipping (TVP & TVL): <ul> <li>P01: Placing TVs on a wooden pallet (bottom level)</li> <li>P02: Preparing to wrap the bottom level with a membrane</li> <li>P03: Wrapping the bottom level</li> <li>P04: Placing TVs on top of the bottom level (second level)</li> <li>P05: Placing TVs on top of the second level (third level)</li> <li>P06: Wrapping the second level with a plastic membrane</li> <li>P07: Wrapping the third level with a plastic membrane</li> <li>P08: Placing TVs on top of the third level (fourth level)</li> <li>P09: Wrapping the fourth level with a plastic membrane</li> </ul> </li> </ul> Riveting of an airplane floater (PLN): <ul> <li>P01: Rivet with the pneumatic hammer.</li> <li>P02: Prepare the pneumatic hammer and grab rivets. </li> <li>P03: Place the bucking bar to counteract the incoming rivet.</li> </ul> The tasks recorded for silk weaving, glassblowing, and mastic cultivation data sets were segmented by gestures (e.g., G01, G02, etc.) . The tasks recorded for these three data sets are the following: Silk weaving (SW*): <ul> <li>The creation of the punch cards (SWPC).</li> <li>Preparation of the beam (SWPB).</li> <li>Wrapping of the beam (SWWB).</li> <li>Jacquard weaving with small loom (SWSL).</li> <li>Jacquard weaving with medium size loom (SWML).</li> <li>Jacquard weaving with large loom (SWLL).</li> </ul> Glassblowing (GB*): <ul> <li>Beak cutting (GBBC).</li> <li>Blowing and shaping (GBBS).</li> <li>Cervix refining (GBCR).</li> <li>Cord laying (GBCL).</li> <li>Finish details (GBFD).</li> <li>Handle laying (GBHL).</li> <li>Transfer to punty (GBTP).</li> <li>Leg and foot laying (GBLF).</li> </ul> Mastic Cultivation (MC*): <ul> <li>Scrapping with new tool (MCSN).</li> <li>Scrapping with old tool (MCSO).</li> <li>Sweeping (MCSW).</li> <li>Dusting (MCDU).</li> <li>Embroidery A (MCEA).</li> <li>Embroidery B (MCEB).</li> <li>Embroidery with an axe (MCEX).</li> <li>Gathering (MCGA).</li> <li>Harvesting (MCHA).</li> <li>Wiping (MCWI).</li> <li>Shifting A (MCSA).</li> <li>Shifting B (MCSB).</li> <li>Cleaning with the wind (MCCW).</li> </ul> The motion capture files were processed and segmented with a 3D character animation software (MotionBuilder, Autodesk Inc., San Rafael, CA. USA) and exported to Biovision Hierarchy (BVH) files.

opencc-by-4.0Aug 2021View details →

zenodo44/100

Estimating historical air-sea CO2 fluxes: Incorporating physical knowledge within a data-only approach

Reconstructed surface ocean pCO2 and air-sea CO2 fluxes for 1990-2019 using the pCO2-Residual Approach (JAMES 2021MS002960, in review) Surface ocean pCO2 (spo2) and resulting estimates of the air-sea CO2 flux (fCO2) are included in the netcdf file at monthly temporal resolution and for 1x1 grid cell spatial resolution. SeaFlux (https://zenodo.org/record/5482547#.YlT72y-B0_U) variables are used to calculate the fluxes from surface ocean pCO2.

opencc-by-4.0Apr 2022View details →

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Historic building's interior (iPhone LiDAR scan)

This model shows a LiDAR-scan of selected room interiors of a historic building located in Lower Austria (AUT) recorded during building-archaeological measures by the archaeological company <a href="https://www.ardig.at/">ARDIG</a>, triggered by recent remodeling work in the course of house renovation. Scanning was done in two rounds using 3dScannerApp on an iPhone 13 Pro. Each round took approx. < 5 min of capturing and another < 5 min of on-board processing time. After the first room was fully captured in 3D and with textures in the first round, the scan was (nearly) seamlessly extended by another room in a second round using the corresponding app function. Therefore, within less than approx. 20 minutes, both rooms could be fully documented in 3D by a scaled and textured 3D model. Considering the extreme flexibility and fast acquisition and processing time, the method holds tremendous future potential for archaeological work, even if still several minor errors occur in the final product.

opencc-by-4.0Jun 2022View details →

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HISTORIAN: a large-scale HISTORIcal film dataset with cinematographic ANnotation

Developing automated tools for sustainable film preservation of extensive historical film collections assumes an understanding of fundamental cinematographic settings. In order to be able to investigate new approaches to detect and classify cinematographic settings, this paper proposes a novel large-scale historical film dataset with cinematographic annotations (HISTORIAN), i.e., shot boundaries, shot types, camera movements. The dataset consists of 98 digitized original analog film reels related to the Second World War and 10593 film shots manually annotated by human film experts. Moreover, annotations for overscan areas such as sprocket holes are included. A baseline film analysis pipeline is introduced and evaluated. To the best of our knowledge, HISTORIAN is the first dataset that covers the challenges and characteristics of historical film documentaries and provides novel possibilities for exploring automatic film analysis tools. This repository presents a tiny set including a few examples for demonstration. A link to the Github repository (including helper scripts and readme) can be found <a href="https://github.com/dahe-cvl/historian_dataset">here</a>

opencc-by-4.0Dec 2021View details →

zenodo44/100

HadCM3 historical simulation with transient tracers

Excess temperature, CFCs, SF6 and bomb C14 from the HadCM3 historical simulation (1860-2008) (_hist files). These tracers are also integrated using the control ocean transport (_ctrl files). The experiment is designed to test the Green's function method for estimating excess heat in the ocean.

opencc-by-4.0Jul 2022View details →

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Historic manuscript page images with noisy labels

Images of digitised manuscript pages sourced from <a href="https://iiif.biblissima.fr/collections/">https://iiif.biblissima.fr/collections/</a>. This dataset aims to facilitate experiments using existing data/metadata to train computer vision models. In particular, using 'noisy' labels in some capacity. Each image is taken from a page of a manuscript listed on <a href="https://iiif.biblissima.fr/collections/">https://iiif.biblissima.fr/collections/</a>. Each example includes the labels included in the IIIF manifests for these images. The data includes the following columns: <ul> <li>image: an IIIF URL for the image</li> <li>manifest_url: A URL for the IIIF manifest for the image</li> <li>license: for each image</li> <li>label: the text found in the manifest 'label' field.</li> <li>attribution: which institution the image comes from</li> </ul>

opencc-by-4.0Jun 2022View details →

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Supplementary Materials for "Food security in Roman Palmyra (Syria) in light of paleoclimatological evidence and its historical implications"

Contained here are the SI files for the article "Food security in Roman Palmyra (Syria) in light of paleoclimatological evidence and its historical implications". With all the materials contained here, as well as the openly accessible datasets cited in S1_File, every step of the study can be reproduced. Detailed instructions are contained within. Includes code for Data Analysis. Article DOI: [forthcoming]

opencc-by-4.0Aug 2022View details →

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Historical Local Climate Zones (LCZ) maps for Hong Kong (1845-2000)

This dataset contains GIS maps of the historical urbanization of Hong Kong at 12 timeslices from 1845-2000 (1845, 1888, 1895, 1913, 1938, 1945, 1952, 1961, 1975, 1984, 1995, and 2000). These maps were compiled through classification of historical map material, photographs, paintings, and contemporary textual accounts. The maps describe the land cover of the historical urban core of the territory of Hong Kong: Victoria City on Hong Kong Island, and the Kowloon Peninsula on the mainland. Urban form is described in terms of the Local Climate Zones (LCZ) scheme presented by Stewart & Oke (2012). The dataset is currently in the format of a QGIS archive, but other file formats will be made available in the future and can be requested from the authors at any time. For further details on the process of generating the maps and their application, please see Yee and Kaplan (2022).   Yee, M., & Kaplan, J. O. (2022). Drivers of urban heat in Hong Kong over the past 116 years. Urban Climate. doi:10.1016/j.uclim.2022/101308 Stewart, I. D., & Oke, T. R. (2012). Local Climate Zones for Urban Temperature Studies. Bulletin of The American Meteorological Society, 93(12), 1879-1900. doi:10.1175/bams-d-11-00019.1

opencc-by-4.0Oct 2022View details →

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Historical_Images_complementory_Mingei

Documentation material from the Glass pilot of the Mingei project

opencc-by-sa-4.0Oct 2022View details →

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Historical_Images_Mingei

Documentation material from the Glass pilot of the Mingei project

opencc-by-sa-4.0Oct 2022View details →

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Near-surface Temperature from CMIP6 NCAR CESM2 historical monthly dataset for CLIVAR CMIP6 Bootcamp

This dataset has been created from CMIP6 data through CMIP6 online catalog. It is meant to be used for training purposes only.   Data is from CESM2 (NCAR) and is a monthly dataset from 1850 to 2014 containing near-surface temperature (TAS).

opencc-by-4.0Oct 2022View details →

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State level income distributions for net income deciles for the US for historical years (2011-2014) and projections for different SSP scenarios (2015-2100)

This dataset is documented in this manuscript here- https://iopscience.iop.org/article/10.1088/1748-9326/acf9b8/meta Income distributions are a growing area of interest in the examination of equity impacts brought on by climate change and its responses. We project US state level income distributions using a PCA-based approach, applying a downscaled version of the approach employed by Narayan et al. (2022, in-prep). A state-level dataset had to be synthesized and projected based on existing sources. We apply a PC-based model to our derived state-level dataset, employing projected GINI’s from the SSP scenarios. We produce projected income distribution by income decile for three SSPs to year 2100. For the purpose of the projections, we developed a consistent set of tax adjusted net income deciles for all states from 2011 to 2014. This dataset was used for initialization of the projections and for validation. If/when using this dataset, please cite this paper- https://iopscience.iop.org/article/10.1088/1748-9326/acf9b8/meta

opencc-by-4.0Jul 2022View details →

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Historical Annual Revenue of Energy Storage on European Electricity Markets

This dataset provides the optimized annual revenue for 96 generic storage technologies (12 efficiency x 8 discharge duration).  It covers 17 European electricity markets for up to 16 years (Austria, AT; Belgium, BE;  Switzerland, CH; Czech Republic, CZ; German, DE; Spain, ES; France, FR; Italy, IT; Ireland, IR; Netherlands, NL; Nordpool which includes Denmark, Estonia, Finland, Latvia, Lithuania, Norway, Sweden, NP; Poland, PL; Portugal, PT; Romania, RO; Slovakia, SK; United Kingdom, UK). The optimization is an adaptation of the model presented in Gaudard et al. [2013]. It assumes perfect foresight assumption and a stochastic algorithm. Therefore, the results are an approximation of the maximum rather than the absolute optimum. Further information is provided in "Gaudard L. and Madani K., Energy storage race: Has the monopoly of pumped-storage in Europe come to an end?, forthcoming".  The following information is provided: Country: Code of the specific market (also the filename) Currency: The currency in which the results are expressed Discharge duration [hours]: The time required to empty at full nominal power a device that is fully charged.  Efficiency: Ratio between the amount of discharged and charged energy during a full cycle. Year: From January 1st to December 31st. The given numbers are in euros or GBP per year and normalized to 1kWh of energy storage. This means that for a specific device, the given figures must be multiplied by the volume of energy storage (in terms of kWh). As an example, for an energy device with the efficiency of 0.95, discharge duration of 6h and volume of energy storage of 2000kWh, the revenues in 2003 in Austria would be 9.26 x 2000=18520 euros.  For any questions or further requirements, please feel free to get in touch with the authors.

opencc-by-4.0Aug 2017View details →

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Historical phenology data from Hough (1864) and datasheets for phenometric analyses

This dataset contains a zip file of phenology data from Hough (1864) converted from tables in that source to a format usable for analyses.  This dataset also contains the summary datasheet for a phenometric analysis for an in process manuscript, 'Phenological response to climatic change depends on seasonal warming velocity and species traits' by Robert Guralnick, Erin Grady, Theresa Crimmins, and Lindsay Campbell.

opencc-by-4.0May 2024View details →

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Exploration of historical mining site - Akersberg silver mines (St. Hanshaugen, Oslo, Norway, 04/11/2024)

Exploration of the historical mining site of Akesrberg (St. Hanshaugen, Oslo, Norway, 04/11/2024) - Main ore minerals: pyrite, sphalerite, galena, chalcopyrite, argentite, stembergite - Provisional References: <ul> <li>https://www.researchgate.net/publication/330971315_Akersberg_gruver_Akersberg_Silver_Mines_pages_168-174_in_Arnesen_R_Gjemte_og_glemte_steder_-_urban_utforsking_i_Oslo_og_omradet_rundt_In_Norwegian</li> <li>https://foreninger.uio.no/ngf/ngt/pdfs/NGT_71_2_121-128.pdf</li> <li>https://www.mindat.org/loc-37117.html</li> <li>https://no.wikipedia.org/wiki/Akersberg_gruver</li> </ul>

opencc-by-4.0Jun 2024View details →

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Exploration of historical mining site - Mezzano iron mines (San Bartolomeo, Cavargna Valley, Italy, 06/01/2024)

Exploration of the historical mining site of Mezzano (San Bartolomeo, Cavargna Valley, Italy, 06/01/2024) - Main ore minerals: pyrite, chalcopyrite, siderite, aragonite - Provisional References: <ul> <li>https://www.valcavargna.org/luoghi_di_interesse/miniere-di-mezzano/#:~:text=Le%20miniere%20di%20Mezzano&text=A%20partire%20dagli%20ultimi%20anni,Fratelli%20Campioni%20l'anno%20seguente.</li> <li>https://www.isprambiente.gov.it/it/attivita/museo/regioni/musei/miniera-di-mezzano</li> <li>https://www.valcavargna.org/tradizioni_popolari/vecchi-mestieri/siderurgia/</li> <li>https://www.research.unipd.it/handle/11577/3465257</li> <li>http://www.cmalpilepontine.it/cmvlarcer/zf/index.php/servizi-aggiuntivi/index/index/idtesto/13</li> </ul>

opencc-by-4.0Jun 2024View details →

zenodo44/100

Historical Reconstruction Dataset of Hourly Expected On-Shore Wind Generation in Japan

<h2>Description</h2> This is a historical reconstruction dataset of hourly expected wind generation based on dynamically downscaled atmospheric reanalysis for assessing the spatio-temporal impact of on-shore wind in Japan. The dataset consists of a set of <a href="https://www.unidata.ucar.edu/software/netcdf/">netCDF</a> files with yearly archives of reconstruction results from 1958 to 2012; hourly expected on-shore wind power potential in Japan with a spatial resolution of approximately 5 km mesh has been reconstructed from the numerical weather model reanalysis results. The expected per-unit output values at each location were calibrated using a nonparametric machine learning model that learns statistical relationships between spatial/meteorological features of target locations and actual wind farm outputs. A convenient way to handle this dataset would be to use a tool for manipulating netCDF files, such as <a href="https://code.mpimet.mpg.de/projects/cdo">CDO: Climate Data Operators</a>. <h2>Associated Publication</h2> <ul> <li>Yu Fujimoto, Masamichi Ohba, Yujiro Tanno, Daisuke Nohara, Yuki Kanno, Akihisa Kaneko, Yasuhiro Hayashi, Yuki Itoda, and Wataru Wayama, "Historical Reconstruction Dataset of Hourly Expected Wind Generation Based on Dynamically Downscaled Atmospheric Reanalysis for Assessing Spatio-Temporal Impact of On-Shore Wind in Japan", Big Earth Data, doi: 10.1080/20964471.2024.2374044 </li> </ul> <h2>Version history</h2> <ul> <li>Ver. 1.0: Released.</li> <li>Ver. 1.1: The preprocessing of the source information used for dataset preparation has changed.</li> <li>Ver. 1.2: The hyperparameter tuning scheme for the post-processing model has changed.</li> </ul>

opencc-by-4.0Oct 2023View details →

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