Find research datasets worth reusing

opencc-by-4.0Jan 2023View details →

Load and generation time series for German federal states: Static vs. dynamic regionalization factors (data)

This dataset contains regionalization factors for electricity generation and demand time series in Germany for the years 2019 - 2022. The factors can be used to distribute national generation and demand time series available from SMARD or ENTSO-E  to federal state level. The methods underlying the regionalization factors are described in [1], with a focus on the year 2021. However, an extended version of the dataset covering the years 2019-2022 is also included for comprehensive analysis. Moreover, the dataset comprises the corresponding regionalized generation and demand time series at the federal state level of Germany. This time series has been generated using the provided distribution factors for the years 2019-2022 and corresponding generation and demand time series from SMARD [2]. Addtionally, the regionalization methodology for the distributed generation and demand data for the year 2021 has been supplemented with validation data, as described in [1]. This data has been cross-checked against the available SMARD Transmission System Operator (TSO) data. A description of the preprocessing required to obtain the TSO data comparison is provided in a separate .txt file. A PDF document has been prepared, which includes scatter plots that illustrate a comparison between actual and allocated generation per production type or demand data for TSOs on an hourly basis for the year 2021. "static_regionalization_factors.2021[csv, xlsx]" Each column corresponds to one factor per federal state and per production type or demand. Regionalization factors are based on share of generation capacity in each state (generation) or population and GDP (demand). "dynamic_regionalization_factors_2021.[csv, xlsx]" “dynamic_regionalization_factors_all.[csv, xlsx]” Each column corresponds to one factor per federal state and per production type or demand. Each row corresponds to a specific hour of the years 2019 through 2022. Regionalization factors are based on a combination of per unit generation data and share of generation capacity in each state, simulated renewable generation data based on spatio-temporal weather data and distribution of wind and solar generation capacities, and a regionalized load dataset for 2015 [3]. “time_series_federal_states_all.[csv, xlsx]” Each column corresponds to the allocated electricity generation or demand per federal state per production type or demand in units of MWh. Each row corresponds to a specific hour of the years 2019 through 2022. The regionalized generation and demand time series has been created by utilizing the dynamic regionalization factors provided in the dataset, in conjunction with the national electricity generation and demand data of Germany as provided by SMARD [2]. “TSO_actual.[csv, xlsx]” “TSO_allocated.[csv, xlsx]” Each column corresponds to the spatially aggregated electricity generation per type or demand per TSO in units of GWh. Each row corresponds to one hour of the year 2021. The TSOs in Germany do not hold direct responsibility for individual federal states, but rather for specific regions. In order to assess the validity of the regionalization methodology employed, it was necessary to generate data at the NUTS3 level and subsequently aggregate it to correspond with the relevant TSOs. The data is pre-processed at NUTS3 level and then undergoes the same methodology as outlined in [1]. The preprocessing steps required to map the installed capacity to the TSO level are explained in the accompanying .txt file. The allocated generation and demand data are aggregated to correspond to the TSO level using a shapefile of mapped regions in Germany that correspond to the TSOs [4]. The actual TSO data is generation and demand as published by SMARD [2]. The accompanying PDF presents scatter plots that showcase the actual vs allocated hourly generation types or demand per TSO, expanding on the information provided in the article. [1] M. Sundblad, T. Fürmann, A. Weidlich and M. Schäfer, "<a href="https://arxiv.org/abs/2304.02951">Load and generation time series for German federal states: Static vs. dynamic regionalization factors</a>," 2023 Open Source Modelling and Simulation of Energy Systems (OSMSES), Aachen, Germany, 2023, pp. 1-6, doi: 10.1109/OSMSES58477.2023.10089686. [2] Bundesnetzagentur | <a href="https://www.smard.de/home">SMARD.de</a> [3] Matthias Kühnbach, Anke Bekk, and Anke Weidlich (2021). <a href="https://www.forecast-model.eu/forecast-en/content/publications.php">Prepared for regional self-supply? On the regional fit of electricity demand and supply in Germany</a>. Energy Strategy Reviews, 34:100609, 20 [4] Frysztacki, Martha Maria. (2023). Mapping of districts to control zones of German Transmission System Operators (TSOs) (v0.1) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7530196">https://doi.org/10.5281/zenodo.7530196</a>

opencc-by-4.0May 2023View details →

Data from the Bioradar Work Package of the German BMBF Research Project SORTIE

This research is part of the German research project SORTIE and is supported by the German Federal Ministry of Education and Research (BMBF) under grant number 13N15189. Related journal articles: [1] Shi, D.; Gidion, G.; Aftab, T.; Reindl, L.M.; Rupitsch, S.J. Frequency Comb-Based Ground-Penetrating Bioradar: System Implementation and Signal Processing. Sensors 2023, 23, 1335. <a href="https://doi.org/10.3390/s23031335">https://doi.org/10.3390/s23031335</a>. [2] Shi, D.; Gidion, G.; Reindl, L.M.; Rupitsch, S.J. Automatic life detection based on efficient features of ground-penetrating rescue radar signals. Submitted to Sensors.

opencc-by-4.0May 2023View details →

German NFDI, FAIRmat-NFDI, NOMAD, NOMAD OASIS, pynxtools, example datasets for atom probe microscopy and electron microscopy

The following repository contains a collection of data and metadata files in different vendor formats which were collected in the fields of atom probe microscopy (LEAP instruments) and electron microscopy (Nion instruments). These files are meant for development and testing purposes of the nomad north-remote-tools-hub and the related nomad-nexus-parser software tools within the FAIRmat project.FAIRmat is a consortium lead by the Humboldt-Universität zu Berlin. FAIRmat is a member of the German Research Data Infrastructure (NFDI) initiative. A detailed description of the background and content of the individual files follows: ger_berlin_haas_nionswift_multimodal.zip This is a dataset for testing how to load entire data and metadata from compressed NionSwift project files directly. This is a dataset for testing the em_nion reader which handles files from Nion microscopes and NionSwift software. The data were collected by Benedikt Haas from Humboldt-Universität zu Berlin. The parser was developed together with Sherjeel Shabih also from Humboldt-Universität zu Berlin. Both work in the group of Prof. Christoph Koch. EM.STEM.Nion.Dataset.1.zip is a dataset we used for an earlier version of this parser APM.LEAP.Datasets.*.zip: This is a collection of two datasets for testing the generic nx_apm reader which handles commercial and community file formats for reconstructed ion position and ranging data from atom probe microscopy experiments. The datasets were collected by different authors. APM.LEAP.Datasets.1.zip: R31_06365-v02.pos, was shared by Jing Wang and Daniel Schreiber (both at PNNL). Details to the dataset are available under the following DOIs: https://doi.org/10.1017/S1431927618015386 https://doi.org/10.1017/S1431927621012241 70_50_50.apt, was a shared by Xuyang Zhou at his time with the Max-Planck-Institut für Eisenforschung GmbH as a open-source test data to the publication he lead on machine-learning-based techniques for composition profiling. The dataset and publication is available via the following DOI and resources: https://doi.org/10.1016/j.actamat.2022.117633 The dataset specifically is also available here: https://github.com/RhettZhou/APT_GB/tree/main/example/Cropped_70_50_50 The range files *.rng and *.rrng range serve as examples to develop tools for parsing them and handle the formatting of range files. The scientific content of the range files was inspired by experiments but is not related to the above-mentioned atom probe datasets and should not be used to analyze these test data for more than pure development purposes. Use instead your own data and matching range files for scientific analyses. APM.LEAP.Datasets.2.zip R18_53222_W_18K-v01.epos, was shared with Markus Kühbach by Andrew Breen during their time at the Max-Planck-Institut für Eisenforschung GmbH. We would like to invite the community to use the nomad infrastructure and support us with sharing data and dataset which we can then use to improve the file format parsing, the reading capabilities, and analyses services of the nomad infrastructure so that the community can profit again from these developments. aut_leoben_leitner.zip is the dataset associated to the grain boundary solute segregation case study discussed in https://arxiv.org/abs/2205.13510 usa_portland_wang.zip is the dataset associated with the ODS steel specimen dataset, which is a good example for testing and learning iso-surface based analyses with the paraprobe-toolbox. This dataset was mentioned as one of the test cases in https://arxiv.org/abs/2205.13510 ger_erlangen_felfer_ck10.zip is the Ck10 for fundamentals dataset from the atom-probe-toolbox https://github.com/peterfelfer/Atom-Probe-Toolbox/tree/master/test%20data/Ck%2010%20steel%20for%20fundamentals usa_denton_smith_apav_gbco.zip is the GBCO-type dataset from J. Smith and M. Young discussed in their following publications: https://doi.org/10.1017/S1431927621012794 and https://github.com/openjournals/joss-reviews/issues/4862 usa_denton_smith_apav_si.zip is a very small dataset in POS, ePOS, APT, RNG, and RRNG for development and testing purposes. The dataset is a part of APAV mentioned here https://gitlab.com/jesseds/apav/-/tree/JOSS/apav/tests

opencc-by-4.0May 2023View details →

German Renewable Energy Unit Register

The UnitRegister dataset is a comprehensive compilation of all renewable energy units connected to the grid in Germany. It combines data from two primary sources: the <a href="https://www.marktstammdatenregister.de/MaStR/Datendownload">Marktstammdatenregister</a> published by Germany's Federal Network Agency (BNetzA), and individual publications from the four German Transmission System Operators (TSOs) on their conjoint reporting <a href="https://www.netztransparenz.de/EEG/Anlagenstammdaten">webpage</a>. The dataset encompasses active, decommissioned, and planned units, resulting from the merging and cleaning of these two unit registers. The Marktstammdatenregister dataset is regularly updated and was last downloaded in December 2022. The TSOs' unit register is updated annually around August, with a one-year lag, and the last data extraction took place in 2022, providing information until 2021. While both datasets are similar, the main difference lies in the reporting format. The TSOs report units as bundles of installations under a common payment code (Unit_ID), whereas BNetzA publishes each installation as a separate unit with an individual identification code (BNetzA_Unit_ID). For example, a wind park with multiple turbines is reported as a single unit by the TSOs, whereas BNetzA provides information on each turbine separately. The main dataset, "UnitRegister.csv," is located in the "3Output" folder. It comprises more than 2.4 million units categorized into ten technologies, including Geothermal, Hydropower, Gas_landfill, Gas_mine, Gas_sewage, Biomass, Wind_on, Wind_off, Solar_GM, and Solar_RM. This dataset consists of 13 columns/variables, including "Unit_ID," "BNetzA_EEG_ID," "BNetzA_Unit_ID," "Technology," "Capacity_kW," "Commissioning," "Decommissioning," "State," "PLZ," "Source," "RefYield," "Depth," and "Coast_dist." In addition to the main dataset, this publication provides separate files for different stages of data processing. For example, "UnitRegister_TSOs.csv" and "UnitRegister_BNetzA.csv" offer clean datasets for the TSOs and BNetzA sources individually. The file "UnitRegister_BNetzA_Raw.csv" contains the compiled BNetzA register in its raw form, comprising 82 columns/variables. Detailed information on these variables can be found in the BNetzA user manual, which is included in the "Dokumentation MaStR Gesamtdatenexport" folder within the BNetzA directory. Notes: The dataset has undergone merging and cleaning processes aligned with the research objectives of the Chair of Energy Economics at BTU. It is important to note that these objectives may differ from other research purposes. Specifically, our research focused on analyzing payments associated with renewable energy projects, giving priority to the TSO data. The BNetzA data set was used to supplement the unit stock and fill in data gaps for TSO units whenever possible. Users should exercise caution when selecting the data source that best aligns with their research goals. To assist in this decision-making process, we have included the R-code used for data processing. By downloading the data package and modifying the top-level directory (line 22), users can run the code. However, it is crucial to verify that any updated datasets maintain consistency with the previous file structure (which has not always been the case in the past) and update the file reading sections in the code accordingly. This publication is part of a series of datasets on German Renewable Energy Sources Data, which also includes a <a href="https://zenodo.org/record/8010410">PaymentRegister</a>, a <a href="https://zenodo.org/record/8013071">TariffRegister</a> and the combination of this information to analyze individual bids in renewable auctions. To gain a comprehensive understanding of the datasets and their relationships, we recommend referring to two related publications: •    Batz Liñeiro, T. B., Müsgens, F. (2023). "Evaluating the German onshore wind auction programme: An analysis based on individual bids." Energy Policy, 172, 113317. <a href="https://doi.org/10.1016/j.enpol.2022.113317">https://doi.org/10.1016/j.enpol.2022.113317</a> | <a href="https://ssrn.com/abstract=4130232">Preprint</a> •    Batz Liñeiro, T., Müsgens, F. (2021). "Evaluating the German PV auction program: The secrets of individual bids revealed." Energy Policy, 159, 112618. <a href="https://doi.org/10.1016/j.enpol.2021.112618">https://doi.org/10.1016/j.enpol.2021.112618</a> | <a href="http://arxiv.org/abs/2104.07536">Preprint</a>

German Renewable Energy Tariff Register

The TariffRegister compiles all tariff types paid to renewable energy units in Germany under the RES Act 2000 and its subsequent amendments. This dataset is based on the tariff files reported by the four German Transmission System Operators (TSOs) on their conjoint reporting <a href="https://www.netztransparenz.de/EEG/Verguetungs-und-Umlagekategorien">webpage</a>.  It includes over 6500 different tariff types applicable from 2000 to 2023. The file consists of 19 descriptors, the tariff identification code (Tariff_ID), publication year, associated technology, commissioning year of applicable units, unit characterization criteria (Criteria1 and Criteria2), bonus details (Bonus_name and Bonus_start), fuel type (Fuel), tariff values (Tariff), management premium, "Ausfallvergütung" value, “Mieterstromzuschlag”, and date of tariff introduction or change. Additionally, two tariff categorization levels (Category1 and Category2) group tariffs into 29 and 17 respective categories, while the KWK descriptor indicates CHP-biomass plant subsidies and the "Sign" descriptor signifies positive or negative tariffs. Notes: The TariffRegister.csv can be matched with the <a href="https://zenodo.org/record/8010410">PaymentRegister</a> dataset to identify the payment types received by renewable energy units. The provided categorization process reflects the specific research needs of the Chair of Energy Economics at BTU and does not affect the original data, allowing users to choose whether to utilize the categorization descriptors. Users can update the dataset by following the outlined steps: <ol> <li>Update the “Original files” by downloading the latest published data (<a href="http://www.netztransparenz.de/EEG/Verguetungs-und-Umlagekategorien">here</a>)</li> <li>Clean the files from any row that is not including a Tariff_ID</li> <li>Include the categorization descriptors by matching the categories based on the Tariff_ID and update the categories for new tariffs.</li> <li>Paste this information into a CSV file and store it under “Cleaned files” and check that the column names and data types match the information from previous files.</li> <li>Run the provided R code after modifying the top-level directory.</li> </ol> This publication is part of a series of datasets on German Renewable Energy Sources Data, which also includes a <a href="https://zenodo.org/record/7945029">UnitRegister</a>, a <a href="https://zenodo.org/record/8010410">PaymentRegister</a>, and the combination of this information to analyze individual bids in renewable auctions. For a comprehensive understanding of the datasets and their relationships, we recommend referring to two related publications: <ul> <li>Batz Liñeiro, T. B., Müsgens, F. (2023). "Evaluating the German onshore wind auction programme: An analysis based on individual bids." Energy Policy, 172, 113317. <a href="https://doi.org/10.1016/j.enpol.2022.113317">https://doi.org/10.1016/j.enpol.2022.113317</a> | <a href="https://ssrn.com/abstract=4130232">Preprint</a></li> <li>Batz Liñeiro, T., Müsgens, F. (2021). "Evaluating the German PV auction program: The secrets of individual bids revealed." Energy Policy, 159, 112618. <a href="https://doi.org/10.1016/j.enpol.2021.112618">https://doi.org/10.1016/j.enpol.2021.112618</a> | <a href="http://arxiv.org/abs/2104.07536">Preprint</a></li> </ul>

German Renewable Energy Payment Register

The PaymentRegister dataset offers a comprehensive compilation of subsidies and tariffs paid to renewable energy units installed in Germany between 2000 and 2021. The dataset is constructed based on the yearly statements provided by the four German Transmission System Operators (TSOs) on their conjoint reporting webpage. These statements are updated annually around August, with a one-year lag, and the dataset encompasses yearly statements from 2007 to 2021. Containing over 53 million observations, the dataset comprises 12 variables/descriptors. It includes a unit identifier (Unit_ID) that can be matched with the <a href="https://zenodo.org/deposit/7945029">UnitRegister</a> dataset, along with information on generation (G_kWh) and associated payments (P_Euro). Additionally, descriptors related to the associated tariff are provided, such as the Tariff identification code (Tariff_ID) and the regulated tariff value (Tariff). The dataset also includes the technology of the unit, a tariff categorization (Ref1, Ref2) based on the payment type (e.g., full feed-in tariff, market premium, bonus payment, reduced payment due to sanction), the year of payment (Year), and the name of the TSO under which the unit is registered (TSO). Notably, two additional descriptors, "Tariff1" and "P_Euro1," are included to highlight any discrepancies between reported tariff values and actual payments. Notes: The PaymentRegister.csv dataset was compiled to meet the research needs of the Chair of Energy Economics at BTU. Payments were categorized based on their tariffs, resulting in a more specific first categorization process with 29 categories, followed by a second categorization level grouping the tariffs into 17 categories. However, these categorizations are solely descriptive and do not impact the original data, allowing users to choose whether to utilize the categorization descriptors or not. To assist users in utilizing the dataset, we have provided the R-code used for data processing. By downloading the data package and modifying the top-level directory (line 13), users can run the code. To update the dataset, the following steps are recommended: <ol> <li>Update the TariffRegister following this publication.</li> <li>Download the new year statements from the TSOs into each TSO folder and run the individual TSO data analysis R-codes. Please modify the top-level directory for these codes as well and update the sections for the new data. Ensure that any updated datasets maintain consistency with the previous file structure (which has not always been the case in the past).</li> <li>Execute the main code to bind all the files together.</li> </ol> This publication is part of a series of datasets on German Renewable Energy Sources Data, which also includes a <a href="https://zenodo.org/deposit/7945029">UnitRegister</a>, a <a href="https://zenodo.org/record/8013071">TariffRegister</a> and the combination of this information to analyze individual bids in renewable auctions. For a comprehensive understanding of the datasets and their relationships, we recommend referring to two related publications: •    Batz Liñeiro, T. B., Müsgens, F. (2023). "Evaluating the German onshore wind auction programme: An analysis based on individual bids." Energy Policy, 172, 113317. <a href="https://doi.org/10.1016/j.enpol.2022.113317">https://doi.org/10.1016/j.enpol.2022.113317</a> | <a href="https://ssrn.com/abstract=4130232">Preprint</a> •    Batz Liñeiro, T., Müsgens, F. (2021). "Evaluating the German PV auction program: The secrets of individual bids revealed." Energy Policy, 159, 112618. <a href="https://doi.org/10.1016/j.enpol.2021.112618">https://doi.org/10.1016/j.enpol.2021.112618</a> | <a href="http://arxiv.org/abs/2104.07536">Preprint</a>

Input and output data for the paper "Evaluating the German PV auction program: The secrets of individual bids revealed"

Batz Liñeiro, T., Müsgens, F., (2021). Energy Policy <a href="http://doi.org/10.1016/j.enpol.2021.112618">doi.org/10.1016/j.enpol.2021.112618</a> ABSTRACT Auctions have become the primary instrument for promoting renewable energy around the world. However, the data published on such auctions are typically limited to aggregated information (e.g., total awarded capacity, average payments). These data constraints hinder the evaluation of realisation rates and other relevant auction dynamics. In this study, we present an algorithm to overcome these data limitations in German renewable energy auction programme by combining publicly available information from four different databases. We apply it to the German solar auction programme and evaluate auctions using quantitative methods. We calculate realisation rates and—using correlation and regression analysis—explore the impact of PV module prices, competition, and project and developer characteristics on project realisation and bid values. Our results confirm that the German auctions were effective. We also found that project realisation took, on average, 1.5 years (with 28% of projects finished late and incurring a financial penalty), nearly half of projects changed location before completion (again, incurring a financial penalty) and small and inexperienced developers could successfully participate in auctions. Description The data package offered in this publication comprises input, processing, and output files, accompanied by the corresponding R-codes used for data processing at different stages. Among the various data outputs, the "Auctions" sheet within the file "2 Auction Realizations Solar-2" holds particular significance for users. Within this sheet, users can identify the realized projects, their respective IDs, and the reported individual bid values. However, it is recommended to refer to the attached publication to gain a comprehensive understanding of the bid-value identification process. For users seeking to update the results, the input files can be easily updated by referring to partner publications that share the same file names. These partner publications include the <a href="https://zenodo.org/record/7945029">UnitRegister</a>, <a href="https://zenodo.org/record/8010410">PaymentRegister</a>, and <a href="https://zenodo.org/record/8013071">TariffRegister </a>datasets.

Input and output data for the paper "Evaluating the German onshore wind auction programme: An analysis based on individual bids"

Batz Liñeiro, T., Müsgens, F., (2023). Energy Policy <a href="https://doi.org/10.1016/j.enpol.2022.113317">https://doi.org/10.1016/j.enpol.2022.113317</a> ABSTRACT Auctions are a highly demanded policy instrument for the promotion of renewable energy sources. Their flexible structure makes them adaptable to country-specific conditions and needs. However, their success depends greatly on how those needs are operationalised in the design elements. Disaggregating data from the German onshore wind auction programme into individual projects, we evaluated the contribution of auctions to the achievement of their primary (deployment at competitive prices) and secondary (diversity) objectives and have highlighted design elements that affect the policy's success or failure. We have shown that, in the German case, the auction scheme is unable to promote wind deployment at competitive prices, and that the design elements used to promote the secondary objectives not only fall short at achieving their intended goals but create incentives for large actors to game the system. Description The data package offered in this publication comprises input, processing, and output files, accompanied by the corresponding R-codes used for data processing at different stages. Among the various data outputs, the "Auctions" sheet within the file "3 Auction Realizations Onshore Wind" holds particular significance for users. Within this sheet, users can identify the realized projects, their respective IDs, and the reported individual bid values (BV). However, it is recommended to refer to the attached publication to gain a comprehensive understanding of the bid-value identification process. For users seeking to update the results, the input files can be easily updated by referring to partner publications that share the same file names. These partner publications include the <a href="https://zenodo.org/record/7945029">UnitRegister</a>, <a href="https://zenodo.org/record/8010410">PaymentRegister</a>, and <a href="https://zenodo.org/record/8013071">TariffRegister </a>datasets.

Evaluation Data for the Annotation of German and English New Testament Texts with Strong's Numbers

This repository provides a gold standard for parallel Bible texts and offers annotation of 20 New Testament verses with Strong's numbers referring to Greek words. We considered three German texts (Luther 2017, Schlachter, and Hoffnung für alle) and two English texts (New Revised Standard Version and World English Bible), See <a href="https://github.com/jd-s/gold-standard-parallel-bible">https://github.com/jd-s/gold-standard-parallel-bible</a> for more information.

opencc-zeroJun 2023View details →

dryad36/100

Data from: Parasites, depredators, and limited resources as potential drivers of winter mortality of feral honeybee colonies in German forests

Wild honeybees (Apis mellifera) are considered extinct in most parts of Europe. The likely causes of their decline include increased parasite burden, lack of high-quality nesting sites and associated depredation pressure, and food scarcity. In Germany, feral honeybees still colonize managed forests, but their survival rate is too low to maintain viable populations. Based on colony observations collected during a monitoring study, data on parasite prevalence, experiments on nest depredation, and analyses of land cover maps, we explored whether parasite pressure, depredation or expected landscape-level food availability explain feral colony winter mortality. Considering the colony-level occurrence of 18 microparasites in the previous summer, colonies that died did not have a higher parasite burden than colonies that survived. Camera traps installed at cavity trees revealed that four woodpecker species, great tits, and pine martens act as nest depredators. In a depredator exclusion experiment, the winter survival rate of colonies in cavities with protected entrances was 50% higher than that of colonies with unmanipulated entrances. Landscapes surrounding surviving colonies contained on average 6.4 percentage points more cropland than landscapes surrounding dying colonies, with cropland being known to disproportionately provide forage for bees in our study system. We conclude that the lack of spacious but well-protected nesting cavities and the shortage of food are currently more important than parasites in limiting populations of wild-living honeybees in German forests. Increasing the density and diversity of large tree cavities and promoting bee forage plants in forests will probably promote wild-living honeybees despite parasite pressure.

opencc-by-4.0Jul 2023View details →

Revisions. NS-Regime, WW2 and Holocaust in West-German Documentaries in the Early Federal Republic of Germany

This contribution presents an English summary of some of the important ideas of the project "Revisionen. Nationalsozialismus, Holocaust und Zweiter Weltkrieg im dokumentarischen Film der frühen Bundesrepublik Deutschland" (Revisions. National Socialism, Holocaust and World War II in Documentary Film in the Early Federal Republic of Germany) by the two authors Götz Lachwitz and Thomas Weber, which was published as a book. It deals with the hitherto little-noticed examination of the Nazi past, the Holocaust and the Second World War by the West German documentary films made up to 1961, which sought a new social approach to the past and thus sought to contribute to a resolution of the conflicts in their time. The project does not deal solely with well-known films such as Nuit et Brouillard by Alain Resnais or Mein Kampf by Erwin Leiser, but takes a look at a broader selection. Building on in-depth archival work, it also discusses films that have rarely or not at all been taken up in the specialized literature. Even if the discourses discernible in most of the films tie in with familiar interpretations, they still surprise us with a diversity of formats, distribution channels, and performance contexts, for example in documentary television series or political education work, which points to a more differentiated approach to the Nazi past in the early Federal Republic than has generally been assumed to date. Published in TraMeTraMi, 7-2023: https://trametrami.avinus.org/publikationen/7-2023  Further Informations: https://produkte.avinus.de/produkt/lachwitz-weber-revisionen

opencc-by-4.0Oct 2023View details →

Coordinates of Service Areas along the German Autobahn

This datasets contains the names, coordinates, and highway assignments of 408 service areas along the German Autobahn operated, inter alia, by Autobahn Tank & Rast Gruppe GmbH & Co. KG. This dataset can, for example, be useful for charging station and alternative refueling station location planning in research because existing service areas are often assumed to be candidate location for alternative infrastructure. The data in this publication was generated by using Google Maps (<a href="https://www.google.de/maps/">https://www.google.de/maps/</a>) and the location finder tool of the operator Tank & Rast (<a href="https://www.serways.de/standorte/">https://www.serways.de/standorte/</a>). This publication contains one CVS file. The coordinate reference system is EPSG:4326.

opencc-by-4.0Oct 2023View details →

Case study result data set for the submitted article "Implications of hydrogen import prices for the German energy system in a model-comparison experiment"

The data set contains result data for the German energy system in a long term scenario (scenario year 2045) as described in the publication "Implications of hydrogen import prices for the German energy system in a model-comparison experiment". The results have been generated with the models REMod of Fraunhofer Institute for Solar Energy Systems ISE, Enertile of Fraunhofer Institute for Systems and Innovation Research ISI, and SCOPE SD of Fraunhofer Institute for Energy Economics and Energy System Technology IEE.Abbreviations:<ul><li>BEV - Battery Electric Vehicles</li><li>CC - Combined Cycle</li><li>CCGT - Combined Cycle Gas Turbine</li><li>CHP - Combined heat and power</li><li>CO2 - Carbon dioxide</li><li>con - consumption</li><li>FC - Fuel Cell</li><li>FCEV - Fuel Cell Electric Vehicle</li><li>gen - generation</li><li>H2 - Hydrogen</li><li>HT - High temperature</li><li>ICE - Internal Combustion Engine</li><li>LDV - Light-Duty Vehicle</li><li>LT - Low temperature</li><li>med - medium</li><li>OC - Open Cycle</li><li>OCGT - Open Cycle Gas Turbine</li><li>PHEV - Plug-In Hybrid Vehicles</li><li>PS - Pumped Storage</li><li>PV - Photovoltaics</li><li>ST - Steam turbine</li><li>SynFuel - Synthetic fuel</li><li>w/ - with</li><li>w/o - without</li><li>yr - year</li></ul>

closedIPD-NOFeb 2026View details →

German Ablation Quality-Register

ClinicalTrials.gov study NCT01197638. IPD Sharing: NO. Countries: 1. Publications: 2.

restrictedIPD-UNDECIDEDFeb 2026View details →

German Corneal Cross Linking Register

ClinicalTrials.gov study NCT00560651. IPD Sharing: Not stated. Countries: 1. Publications: 4.

closedIPD-NOFeb 2026View details →

German Adaptation of REACH II

ClinicalTrials.gov study NCT01690117. IPD Sharing: NO. Countries: 1. Publications: 3.