Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
724
datasets available to search
ShareScore release 0.9.0
Dataset results
724 results for “german”
Questionnaires_English_German_Italian
<p>The questionnaires guided the semi-structured interviews with married couples</p>
Sentence-aligned student translations of Crito (Ancient Greek, English, German, Persian)
<p>This dataset is a corpus of five student's translation of Plato's Crito aligned at sentence-level with the original Ancient Greek text, one German translation, and two English translations. The Ancient Greek text is the Burnet edition, made available by Perseus Digital Library. For more information, see: <br> https://www.perseus.tufts.edu/hopper/text?doc=Perseus%3Atext%3A1999.01.0169%3Atext%3DCrito%3Asection%3D43a<br> </p> <p>The details of the eight translations (five Persian, two English, and one German translations) are as follows:</p> <ul> <li>German Translation: The German translation of Schleiermmacher available on Project Gutenberg has been aligned at the sentence level.<br> For more information, see: Plato, F. Schleiermacher, Platons Werke, In der Realschulbuchhandlung, 1809<br> Link to the text on Project Gutenberg:<br> https://www.projekt-gutenberg.org/platon/platowr1/kriton.html<br> </li> <li>English Translations: Two different English translations of "Crito", one by Benjamin Jowett and the other by Harold North Fowler are included in the dataset.<br> For more information on Jowett's translation, see:<br> Plato, H. N. Fowler, W. Lamb, Plato in Twelve Volumes, Vol. 1 translated by Harold North<br> Fowler; Introduction by W.R.M. Lamb, volume 1, Harvard University Press and Wiliam<br> Heinemann Ltd., Cambridge, MA and London, 1966.<br> Fowler's translation on Perseus Digital Library:<br> https://www.perseus.tufts.edu/hopper/text?doc=plat.+crito+43a<br> For more information on Jowett's translation, see:<br> Plato, B. Jowett, Crito, The Internet Classics Archive, Massachusetts Institute of Technology,<br> http://classics.mit.edu/Plato/crito.html.<br> </li> <li>Persian Translations: The dataset consists of five Persian translations by students who have already completed a 30-hour Homeric Greek course. Each translator has translated the text into Persian using treebanks, commentaries, lexicon entries, and English and German translations. The translators themselves aligned the Persian translations to the Greek text at word-level using Ugarit. The alignments are available in their Ugarit profile:<br> Shouresh Assimi: https://ugarit.ialigner.com/userProfile.php?userid=50956<br> Aylar Mahmoudzadeh Sarabi: https://ugarit.ialigner.com/userProfile.php?userid=63464&tgid=9576<br> Nima Mohammadi: https://ugarit.ialigner.com/userProfile.php?userid=52434&tgid=9362<br> Kimia Nikpour: https://ugarit.ialigner.com/userProfile.php?userid=52378<br> Farshid Rahimi: https://ugarit.ialigner.com/userProfile.php?userid=50932&tgid=9727</li> </ul> <p>The group's initial goal was to produce one finalized translation of Crito to Persian, but due to the intriguing variations in the translations and the text's intricacy, it was decided to provide three finalized translations rather than one. The finalized translations will be available in Beyond Translation as part of the Perseus Digital Library under a Creative Commons license. For more information on our final versions of Crito, see: http://beyond-translation.perseus.org</p>
Berlin State Library (2023). Named Entity Disambiguation German Database for the Named Entity Linking System of the Berlin State Library (SBB)
<p>This database is a part of a BERT-based entity recognition and three-stage entity linking (EL) system. Its components consist of three models as well as three related databases, one of which is published here.</p>
H2020 Platone German Demonstrator Use Case 1, 2, 3 and 4 Measurement Data
<p>This dataset belongs to the German demonstrator of the H2020 Platone project (WP5). This dataset contains measurement data and processed data relevant for the evaluation of UseCases (UCs) applied in the field test side.</p> <p><strong>Background - Field Test Setup</strong></p> <p>The field test setup consists of a Low Voltage (LV) community with 450 kW installed generation capacity. The power exchange between the LV grid and Medium Voltage (MV) grid takes place along a single Point of Common Coupling (PCC). i.e., a secondary substation that includes a transformer with sensors on the LV busbar to measure the net power exchange. The community consists of 89 households, 450kW of installed PV generation capacity, a Community Battery Energy Storage (CBES) connected to the LV busbar with 300 kW and 850 kWh capacity. </p> <p><strong>Description of data set:</strong></p> <p>p_tei - arithmetic mean of measured power exchange at PCC (Total residual power exchange Export/Import) measured in 1-minute intervals devided by number of samples available for computing within 15 minutes (p_tei_count)</p> <p>p_tcb - arithmetic mean of measured charging/discharging power of CBES in 1-minute intervals devided by number of samples available for computing within 15 minutes (p_tei_count)</p> <p>p_tcb_set – triggered charging/discharging power of CBES</p> <p>p_tei_c - Computed power exchange at PPC. That value indicates the value p_tei if no UC would have been applied (baseline).</p> <p>e_im –cumulated measured energy import (from MV grid into LV grid)</p> <p>e_ex - cumulated measured energy export (from LV grid into MV grid)</p> <p>soc – State Of Charge of CBES</p> <p>soc_max – maximum permissible SOC of CBES</p> <p>soe - State Of Energy of CBES</p> <p>soc_min – minimum permissible SOC of CBES</p> <p>id – ID of UC that is active at point of time</p> <p>setpoint - Charging/discharging power for CBES triggered by EMS (ALF-C) during active an UC</p> <p>subtype - 0 - Rule-Based Operation Mode with 15-minutes control cycles of battery (CBES in the field) ;1 - Day-ahead forecast-based control; 2.0 - Schedule-based operation mode with optimization applied to a day-ahead forecast (optimization target: minimization of power exchanges at MV/LV PCC within 24h period ; 21 - Schedule-based operation mode with optimization applied to a day-ahead forecast (optimization target: minimization of power exchanges at MV/LV PCC and achieving a requested State of Charge (of CBES) at the end of UC_End;</p> <p>type – Triggered Type of UC (1 - "Virtual Islanding of LV community" (UC 1); 2 - "Coordination of Flex Request" (UC 2); 3 - "Energy Import in Bulk" (UC 3); 4 - "Bulk-based Energy Export" (UC 4)</p> <p>bulk – (yes/no) – indicates whether bulk energy import or export is active. Only relevant for UC 3 and 4.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 864300.</p>
German Gaming Magazines Sales Data 1980-2000
<p>A data set that collects sales and distribution data of seventeen German gaming magazines 1980-2000, based on the print run lists ("Auflagelisten") published by the <i>Informationsgemeinschaft zur Feststellung der Verbreitung von Werbeträgern</i> (<a href="https://en.wikipedia.org/wiki/Informationsgemeinschaft_zur_Feststellung_der_Verbreitung_von_Werbetr%C3%A4gern"><i>Information Community for the Assessment of the Circulation of Media</i></a> – IVW). More information on the dataset and its structure can be found here: <a href="https://chludens.hypotheses.org/1228">https://chludens.hypotheses.org/1228</a></p>
Dataset SeBluCo study: SARS-CoV-2-antibodies among German blood donors 2020 – 2022, a repetitive cross-sectional study
<p>The dataset is the result of a repetitive cross-sectional study in 28 regions in Germany on SARS-CoV-2 antibodies in residual samples of blood donors from April 2020 to April 2021, September 2021 and April/May 2022. These data were used to aide in monitoring the pandemic in Germany. Data were completely anonymised at the site of sample collection. Serological test results are accompanied by demographic data including sex, age and area of residence (assigned a level two Nomenclature des Unités Territoriales Statistiques (NUTS2)). </p><p>The file contains data (sheet "data") as well as the description of variable content and coding (sheet "variables").</p>
Data and code from: Functional rarity of plants in German hay meadows - patterns on the species level and mismatches with community species richness
Open the record for dataset details and reuse information.
Patient-reported outcomes predict return to work and health-related quality of life 6 months after cardiac rehabilitation: Results from a German multi-centre registry (OutCaRe): dataset
<p>This is the resulting data set from the prospective observational study (available cases at the time of follow up monitoring) OutCaRe, that is the basis of all results presented in the article "Patient-reported outcomes predict return to work and health-related quality of life 6 months after cardiac rehabilitation: Results from a German multi-centre registry (OutCaRe)".</p> <p>OutCaRe is a registered study (https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00011418).</p>
Video recordings for the female German Matrix Sentence Test (OLSA)
<p>This dataset provides the video recordings for the female German Matrix Sentence Test (OLSA), a speech intelligibility test. The audio recordings are not provided in the dataset, but research licenses are available from Hörzentrum Oldenburg gGmbH. Please refer to Hörzentrum Oldenburg gGmbH for the audio material (<a href="https://www.hz-ol.de/en/matrix.html">https://www.hz-ol.de/en/matrix.html</a>). An example can be seen in "exampleWithAudio.mp4". For testing audiovisual speech perception, the video recordings should be played together with the original speech OLSAf mentioned above. One second of silence at the beginning of each audio file should be added for synchronous playback.</p> <p>Additionally, a web demo is provided to see how the test functions without sound (try it here with Chrome: <a href="http://www.staff.uni-oldenburg.de/gerard.llorach.to/AVOLSA/">http://www.staff.uni-oldenburg.de/gerard.llorach.to/AVOLSA/</a>). In order to make the web demo work, the folders "Video.zip" and "webData.zip" should be unzipped and placed where "A_demo.html" is. The web demo permits to test visual-only, audiovisual, and audio-only modalities. For testing with audio (if acquired), please drag and drop the audio files ("01248.wav", "02064.wav"...) in the web interface. For further information about the web demo, please read the introduction inside the file "A_demo.html". For further information about the material and how it was recorded please refer to the references.</p> <p>Special thanks to Jutta Birkigt, the talker of the audio and video recordings of the female German Matrix Sentence Test.</p> <p>Reference:</p> <p>Llorach, G., Kirschner, F., Grimm, G., Zokoll, M.A., Wagener, K.C. and Hohmann, V., 2021. Development and evaluation of video recordings for the OLSA matrix sentence test. <em>International Journal of Audiology</em>, pp.1-11.</p>
SEAS5/System4-LARSIM_ME Seasonal Streamflow Forecasts for German Waterways
<p>The datasets provided were produced as part of the IMPREX project for work package 4, task 1 “<em>Development of the regional and European scale reforecast dataset of hydrological extremes</em> “, work package 4, task 2 “<em>Analysis of the impact of changes in precipitation attributes from short to medium and climatic ranges</em>” and work package 9, task 3 “<em>Case studies</em>”. Analysis of the datasets are published in Meißner et al. 2017, in Deliverable 4.2 „ <em>The sensitivity of sub-seasonal to seasonal streamflow forecasts to meteorological forcing quality, modelled hydrology and the initial hydrological conditions</em> “ (Arnal et al. 2017) and in Deliverable 4.3 “<em>Forecast skill developments</em>” (Weerts et al. 2019). The aim was to evaluate the potential skill of seasonal streamflow forecasting for the German waterways Rhine, Elbe and Danube.</p> <p>As seasonal meteorological forecast data the reforecast dataset from ECMWF’s Seasonal Forecast System 4 (S4 hereafter) (Molteni et al. 2011) as well as from the fifth generation of ECMWF’s Seasonal forecasting system SEAS5 (ECMWF 2017, Johnson et al. 2018, Owens & Hewson 2018) of the period 1981 – 2016 were used.</p> <p>The horizontal resolution of System 4 is approximately 80 km. In operational mode the ensemble consists of 51 members generated by using an ensemble of initial conditions and by the use of stochastic physics. Re-forecasts starting on the 1st of each month for the years 1981-2016 are generated with the same model as used for the operational forecast. For the period 1981 – 2011 the ensemble size varies between 15 members (initialization months January, March, April, June, July, September, October, December) and 51 members (for the remaining months). Since 2012, the ensemble size is 51 members all over the year (operational forecasts).</p> <p>The fifth generation of ECMWF seasonal forecasting system SEAS5 replaced System 4 in November 2017. The horizontal resolution of the model is 0.4°x0.4° (approx. 36 km). The ensemble consists of 51-members created using a combination of Sea Surface Temperature SST and atmospheric initial condition perturbations and the activation of stochastic physics (ECMWF 2017). Re-Forecasts with the ensemble size of 25 members starting on the 1st of each month for the years 1981-2016 are generated with the same model as used for the operational forecast.</p> <p>The hydrological model applied is called LARSIM-ME (ME – MittelEuropa = Central Europe) and is based in the model software LARSIM (Large Area Runoff SImulation Model) originally developed by Ludwig & Bremicker (2006). LARSIM-ME covers the catchments of the rivers Rhine, Elbe, Weser/Ems, Odra and Upper Danube. The total catchment size simulated by the model is approximately 800,000 km². The spatial resolution is 5 km x 5 km and the computational time-step is daily. For more details about the model see Meißner et al. (2017).</p> <p>The precipitation and temperature data, used to force the hydrological model in simulation mode up to the initialization of the particular forecast, is taken from the E-OBS dataset, version 18 (Haylock et al. 2008). The downward surface solar radiation is extracted from the ERA-Interim reanalysis (Dee et al. 2011) for the period 1979-2018. For further details on data processing see Meißner et al. (2017).</p> <p>As meteorological seasonal forecasts tend to drift towards the model climate with increasing lead-time, the outputs daily total precipitation and air temperature from S4, interpolated to a 50 km x 50 km grid (multiple of the 5 km x 5 km model grid) and from SEAS5, interpolated to a 25 km x 25 km grid, respectively, were drift-corrected with the meteorological observation dataset used for the baseline simulation. As drift correction method the quantile-quantile method (Piani et al. 2010) was used. We corrected daily values of the different variables on a monthly basis, which means each daily value of the same month is corrected by the same scaling. Separate drift correction factors were estimated for each forecast initialization date (calendar month) and monthly lead time (month 1 to month 7) based on the reforecast datasets. In the final step the corrected precipitation and temperature were downscaled to the 5 km by 5 km model grid and used as forcing to create the streamflow re-forecast dataset with LARSIM-ME.</p> <p><strong>Dataset Q_OBS_DE.nc:</strong></p> <p>Mean daily observed flow of the gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe for the period 1951–2017 stored as variable <strong><em>q_obs(time=24472, stations=8</em>)</strong>.</p> <p>Data originate from the database of gauge measurements of the Federal Waterways and Shipping Administration (WSV). These data were quality checked and published by the gauge-operating WSV offices. Nevertheless, data errors and inconsistencies cannot be ruled out completely, so that neither the WSV nor the BfG do accept any liability for the correctness and completeness of the data. Data source: "German Federal Waterways and Shipping Administration (WSV)", provided by the German Federal Institute of Hydrology (BfG)</p> <p><a href="https://zenodo.org/record/3696446">https://zenodo.org/record/3696446</a></p> <p><strong>Dataset Q_EOBS_LME.nc:</strong></p> <p>Mean daily simulated flow of the hydrological model LARSIM-ME forced by observed meteorology from the EOBS dataset and ERA-Interim stored as variable <em><strong>q_sim (time=13880, stations=8)</strong></em>. Period 1979-2016, Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.</p> <pre><code>float q_sim(time=13880, stations=8); :units = "m3/s"; :_FillValue = -9999.0f; // float :long_name = "simulated streamflow"; :coordinates = "lat lon";</code></pre> <p><strong>Dataset Q_System4_LME.nc:</strong></p> <p>Mean daily forecasted flow of the hydrological model LARSIM-ME forced by air temperature and precipitation of ECMWF’s Seasonal Forecast System 4 re-forecasts initialized 1st of each month for the years 1981-2016 with a lead time of 7 months. Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.</p> <p>Forecast values are stored as variable <em><strong>q_fcast_ens(time=432, lead_time=215, realization=51, stations=8)</strong></em>, first dimension forecast dates, second dimension lead time, third dimension realization, fourth dimension stations.</p> <pre><code>loat q_fcast_ens(time=432, lead_time=215, realization=51, stations=8); :_FillValue = -9999.0f; // float :long_name = "forecast streamflow ensemble"; :units = "m3/s"; :coordinates = "lat lon";</code></pre> <p><strong>Dataset Q_SEAS5_LME.nc:</strong></p> <p>Mean daily forecasted flow of the hydrological model LARSIM-ME forced by air temperature and precipitation of ECMWF’s Seasonal Forecast System SEAS5 re-forecasts initialized 1st of each month for the years 1981-2016 with a lead time of 7 months. Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.</p> <p>Forecast values are stored in the variable <em><strong>q_fcast_ens(time=432, lead_time=215, realization=25, stations=8)</strong></em>, first dimension forecast dates, second dimension lead time, third dimension ensemble member, fourth dimension stations.</p> <pre><code>float q_fcast_ens(time=432, lead_time=215, realization=25, stations=8); :_FillValue = -9999.0f; // float :long_name = "forecast streamflow ensemble"; :units = "m3/s"; :coordinates = "lat lon";</code></pre> <p><strong>Literature</strong></p> <p>Arnal, L., H. Cloke, L. Magnusson, B. Klein, D. Meissner, A. de Tomas, J. Hunink, I. Pechlivanidis, L. Crochemore, S. Suarez, A. Solera, J. Andreu, J. Knight, F. Liggins, A. Weerts, M. H. Ramos & G. Thirel (2017): The sensitivity of sub-seasonal to seasonal streamflow forecasts to meteorological forcing quality, modelled hydrology and the initial hydrological conditions. Deliverable 4.2, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811, <a href="http://www.imprex.eu/system/files/generated/files/resource/d4-2-imprex-v1-0.pdf">http://www.imprex.eu/system/files/generated/files/resource/d4-2-imprex-v1-0.pdf</a></p> <p>Dee, D. P., S. M. Uppala, A. J. Simmons, P. Berrisford, P. Poli, S. Kobayashi, U. Andrae, M. A. Balmaseda, G. Balsamo, P. Bauer, P. Bechtold, A. C. M. Beljaars, L. van de Berg, J. Bidlot, N. Bormann, C. Delsol, R. Dragani, M. Fuentes, A. J. Geer, L. Haimberger, S. B. Healy, H. Hersbach, E. V. Holm, L. Isaksen, P. Kallberg, M. Kohler, M. Matricardi, A. P. McNally, B. M. Monge-Sanz, J. J. Morcrette, B. K. Park, C. Peubey, P. de Rosnay, C. Tavolato, J. N. Thepaut & F. Vitart (2011): The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society 137(656), 553-597</p> <p>ECMWF (2017): SEAS5 user guide - Version 1.1. ECMWF, Reading, UK</p> <p>Haylock, M. R., N. Hofstra, A. M. G. Klein Tank, E. J. Klok, P. D. Jones & M. New (2008): A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. Journal of Geophysical Research: Atmospheres 113(D20), D20119</p> <p>Johnson, S. J., T. N. Stockdale, L. Ferranti, M. A. Balmaseda, F. Molteni, L. Magnusson, S. Tietsche, D. Decremer, A. Weisheimer, G. Balsamo, S. Keeley, K. Mogensen, H. Zuo & B. Monge-Sanz (2018): SEAS5: The new ECMWF seasonal forecast system. Geosci. Model Dev. Discuss. 2018, 1-44</p> <p>Ludwig, K. & M. Bremicker (2006): The Water Balance Model LARSIM –Design, Content and Applications. 22. C. Leibundgut, S. Demuth and J. Lange (Eds), Freiburger Schriften zur Hydrologie, Institut für Hydrologie, Universität Freiburg im Breisgau, Freiburg, 141 pp.</p> <p>Meißner, D., B. Klein & M. Ionita (2017): Development of a monthly to seasonal forecast framework tailored to inland waterway transport in central Europe. Hydrol. Earth Syst. Sci. 21(12), 6401-6423</p> <p>Molteni, F., T. Stockdale, M. Balmaseda, G. Balsamo, R. Buizza, L. Ferranti, L. Magnusson, K. Mogensen, T. Palmer & F. Vitart (2011): The new ECMWF seasonal forecast system (System 4). ECMWF Research Department Technical Memorandum n. 656, Shinfield Park, Reading</p> <p>Owens, R. & T. R. E. Hewson (2018): ECMWF Forecast User Guide. ECMWF, Reading, doi: 10.21957/m1cs7h</p> <p>Piani, C., J. O. Haerter & E. Coppola (2010): Statistical bias correction for daily precipitation in regional climate models over Europe. Theoretical and Applied Climatology 99(1-2), 187-192</p> <p>Weerts, A., F. Silvestro, L. Magnusson, B. Klein, I. Pechlivanidis, F. Wetterhall, D. Lavers, E. Gascon, J. Day, S. Hagelin, M. Lindskog & B. van Osnabrugge (2019): Forecast skill developments. Deliverable 4.3, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811</p>
RCP8.5-ECEARTH-RACMO-LARSIM_ME Climate Flow Projection Data for German Waterways
<p>The datasets provided here were produced as part of the IMPREX project for work package 4, task 4 „<em>Improving prediction on the climate scale</em>“ and work package 9, task 3 “<em>Case studies</em>”. Analysis of the datasets are published in Deliverable 4.4 „<em>Estimation of hazards based on improved representation of highly vulnerable water resources of strategic importance on the climate scale</em>“ (Falloon et al 2019). The aim was to study the impact of internal climate model variability and bias correction method on the climate change signal of relevant flow indicators for the German waterways Rhine, Elbe and Danube.</p> <p>To assess the impact of internal variability of the global climate model on future changes of flow, precipitation, temperature and global radiation of the 16-member ensemble generated with the RCM KNMI-RACMO2 driven by the GCM EC-EARTH 2.3 provided by WP3 of IMPREX were used. EC-EARTH was run 16 times from 1850 to 2100, each member starting from a slightly different initial state, under forcing of historical emissions until 2005 and the RCP8.5 greenhouse gas concentration pathway from 2006 onwards. Each of the EC-EARTH members was subsequently dynamically downscaled using KNMI-RACMO2 on a 0.11° (~12 km) resolved domain (Aalbers et al. 2018).</p> <p>To correct the systematic model biases of climate models different bias correction methods were applied: (1) no bias correction, (2) linear scaling (Lenderink et al. 2007) and (3) quantile-quantile mapping (Piani et al. 2010). Bias correction relationships were derived for five-day periods (for each variable and location, in total 73 bias correction relationships were derived) including 13 days before and after the considered five-day period (total window size was 31 days) from the observations and values of the regional climate simulations. The period used to estimate the bias correction relationships was 1971-2000.</p> <p>The hydrological model applied is called LARSIM-ME (ME – MittelEuropa = Central Europe) and is based in the model software LARSIM (Large Area Runoff SImulation Model) originally developed by Ludwig & Bremicker (2006). LARSIM-ME covers the catchments of the rivers Rhine, Elbe, Weser/Ems, Odra and Upper Danube. The total catchment size simulated by the model is approximately 800,000 km². The spatial resolution is 5 km x 5 km and the computational time-step is daily. As observed meteorological forcings, precipitation, air temperature and global radiation from the HYRAS data set (Rauthe et al. 2013) available for the 5 km x 5 km model grid and the period 1951-2015 were used. The hydrological model was calibrated using the automatic calibration scheme Shuffled Complex Evolution SCE-UA algorithm (Duan et al. 1994). For more details about the model see Meißner et al. (2017).</p> <p>The meteorological variables air temperature, precipitation and global radiation produced by the KNMI RACMO-EC-EARTH 16 member ensemble (period 1951-2100) were interpolated to a 25 km x 25 km grid and afterwards bias corrected with respect to the observation data (HYRAS) used for calibration of the hydrological model LARSIM. From this 25 km x 25 km grid the bias corrected variables were downscaled to the 5 km x 5 km model grid of LARSIM using monthly background climatology fields on the 5 km x 5 km target grid of the HYRAS dataset. The bias-corrected and downscaled data was then used as meteorological forcing of LARSIM to calculate flow projections for the rivers Rhine, Elbe and Upper Danube (up to the German/Austrian border).</p> <p><strong>Dataset Q_OBS_DE.nc:</strong></p> <p>Mean daily observed flow of the gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe for the period 1951–2017 stored as variable <em><strong>q_obs(time=24472, stations=8)</strong></em>.</p> <p>Data originate from the database of gauge measurements of the Federal Waterways and Shipping Administration (WSV). These data were quality checked and published by the gauge-operating WSV offices. Nevertheless, data errors and inconsistencies cannot be ruled out completely, so that neither the WSV nor the BfG do accept any liability for the correctness and completeness of the data. Data source: "German Federal Waterways and Shipping Administration (WSV)", provided by the German Federal Institute of Hydrology (BfG)</p> <pre><code>float q_obs(time=24472, stations=8); :units = "m3/s"; :_FillValue = -9999.0f; // float :long_name = "observed streamflow"; :coordinates = "lat lon";</code></pre> <p><strong>Dataset Q_HYRAS_LME.nc:</strong></p> <p>Mean daily simulated flow of the hydrological model LARSIM-ME forced by observed meteorology from the HYRAS dataset stored as variable <em><strong>q_sim (time=23741, stations=8)</strong></em>. Period 1951-2015, Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.</p> <pre><code>float q_sim(time=23741, stations=8); :units = "m3/s"; :_FillValue = -9999.0f; // float :long_name = "simulated streamflow"; :coordinates = "lat lon";</code></pre> <p><strong>Q_RCP85_ECEARTH_RACMO_[bc]_LME.nc:</strong></p> <p>Mean daily projected flow of the hydrological model LARSIM-ME forced by 16 realizations of RCP8.5-ECEARTH-RACMO stored as variable <em><strong>q_sim(time=54787, realization=16, stations=8)</strong></em>, first dimension time, second dimension realization and third dimension stations. Bias correction of meteorological forcings [bc]: NOBC: no bias correction, LS: linear scaling, QQMAP Quantile-Quantile Mapping. Period 1951-2100, Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.</p> <pre><code>float q_sim(time=54787, realization=16, stations=8); :units = "m3/s"; :_FillValue = -9999.0f; // float :long_name = "projected streamflow"; :coordinates = "lat lon";</code></pre> <p><strong>Literature</strong></p> <p>Aalbers, E. E., G. Lenderink, E. van Meijgaard & B. J. J. M. van den Hurk (2018): Local-scale changes in mean and heavy precipitation in Western Europe, climate change or internal variability? Climate Dynamics 50(11), 4745-4766</p> <p>Duan, Q., S. Sorooshian & V. K. Gupta (1994): Optimal use of the SCE-UA global optimization method for calibrating watershed models. Journal of Hydrology 158(3–4), 265-284</p> <p>Falloon, P., K. Williams, J. Andreu, A. Solera, S. Suárez-Almiñana, B. Klein, D. Meissner, J. Hunink, J. Eekhout & J. de Vente (2019): Estimation of hazards based on improved representation of highly vulnerable water resources of strategic importance on the climate scale. Deliverable 4.4, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811, <a href="https://imprex.eu/system/files/generated/files/resource/imprex-deliverablereport-d4-4-final-1.pdf">https://imprex.eu/system/files/generated/files/resource/imprex-deliverablereport-d4-4-final-1.pdf</a></p> <p>Lenderink, G., A. Buishand & W. van Deursen (2007): Estimates of future discharges of the river Rhine using two scenario methodologies: direct versus delta approach. Hydrology and Earth System Sciences 11(3), 1143-1159</p> <p>Ludwig, K. & M. Bremicker (2006): The Water Balance Model LARSIM –Design, Content and Applications. 22. C. Leibundgut, S. Demuth and J. Lange (Eds), Freiburger Schriften zur Hydrologie, Institut für Hydrologie, Universität Freiburg im Breisgau, Freiburg, 141 pp.</p> <p>Meißner, D., B. Klein & M. Ionita (2017): Development of a monthly to seasonal forecast framework tailored to inland waterway transport in central Europe. Hydrol. Earth Syst. Sci. 21(12), 6401</p> <p>Piani, C., J. O. Haerter & E. Coppola (2010): Statistical bias correction for daily precipitation in regional climate models over Europe. Theoretical and Applied Climatology 99(1-2), 187-192</p> <p>Rauthe, M., H. Steiner, U. Riediger, A. Mazurkiewicz & A. Gratzki (2013): A Central European precipitation climatology - Part I: Generation and validation of a high-resolution gridded daily data set (HYRAS). Meteorologische Zeitschrift 22(3), 235-256</p>
ENS-LARSIM_ME Monthly Streamflow Forecasts for German Waterways
<p>The datasets provided here were produced as part of the IMPREX project for work package 4, task 1 “<em>Development of the regional and European scale reforecast dataset of hydrological extremes</em>“ and work package 9, task 3 “<em>Case studies</em>”. Analysis of the datasets are published in Deliverable 9.4 “<em>Semi-operational forecasting system for Rhine, Danube and Elbe to support improved transport cost planning</em>“ (Klein & Meißner 2019). The aim was to evaluate the potential skill of monthly streamflow forecasting for the German waterways Rhine, Elbe and Danube.</p> <p>As meteorological forcing data to calculate monthly flow forecasts the extended-range forecasts from ECMWF-ENS are applied. Twice a week (Monday and Thursday), the ENS model is extended to a lead time up to 46 days by ECMWF. The horizontal resolution for the first 15 days is 0.2°x0.2° (approx. 18 km) and from day 15 to day 46 it is 0.4°x0.4° (approx. 36 km). The ensemble consists of 1 control forecasts and 50 perturbed members, made from slightly different initial atmospheric and oceanic conditions (Owens & Hewson 2018). Re-Forecasts are generated with the same model as used for the operational forecasts for the past 20 years, starting on the same day and month as each real time forecast. The ensemble size is 11-member, which means that in total 20 years x 11 members = 220 forecasts are available for each real time forecast date. The re-forecasts are also created twice a week (Mondays and Thursdays) and are available a week in advance.</p> <p>The hydrological model applied is called LARSIM-ME (ME – MittelEuropa = Central Europe) and is based in the model software LARSIM (Large Area Runoff SImulation Model) originally developed by Ludwig & Bremicker (2006). LARSIM-ME covers the catchments of the rivers Rhine, Elbe, Weser/Ems, Odra and Upper Danube. The total catchment size simulated by the model is approximately 800,000 km². The spatial resolution is 5 km x 5 km and the computational time-step is daily. For more details about the model see Meißner et al. (2017).</p> <p>Real-time meteorological station data (precipitation, temperature and global radiation) was interpolated to the 5 km x 5 km model grid and used as meteorological forcing to initialize LARSIM-ME at the forecast date.</p> <p>Re-forecasts for the hindcast dates 10th March 2016 – 09th March 2017 generating re-forecasts of the last 20 years were used. As station density of real-time meteorological station data is limited before 2000, only reforecasts with a forecast date after 1999 were considered. Daily total precipitation, daily mean air temperature and global radiation of the reforecast dataset of ECMWF-ENS were interpolated to the 5kmx5km model grid and used as forcing to create the streamflow re-forecast dataset with LARSIM-ME.</p> <p><strong>Dataset Q_OBS.nc:</strong></p> <p>Mean daily observed flow of the gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe for the period 1951–2017 stored as variable <strong>q_obs(time=24472, stations=8)</strong>.</p> <p>Data originate from the database of gauge measurements of the Federal Waterways and Shipping Administration (WSV). These data were quality checked and published by the gauge-operating WSV offices. Nevertheless, data errors and inconsistencies cannot be ruled out completely, so that neither the WSV nor the BfG do accept any liability for the correctness and completeness of the data. Data source: "German Federal Waterways and Shipping Administration (WSV)", provided by the German Federal Institute of Hydrology (BfG).</p> <p><a href="https://zenodo.org/record/3696446">https://zenodo.org/record/3696446</a></p> <p><strong>Dataset Q_SYNOP_LME.nc:</strong></p> <p>Mean daily simulated flow of the hydrological model LARSIM-ME forced by observed meteorology from real-time meteorological station data stored as variable float <em><strong>q_sim(time=6210, stations=8)</strong></em>. Period 2000-2016, Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.</p> <pre><code>float q_sim(time=6210, stations=8); :units = "m3/s"; :_FillValue = -9999.0f; // float :long_name = "simulated streamflow"; :coordinates = "lat lon";</code></pre> <p><strong>Dataset Q_ENS_LME.nc:</strong></p> <p>Mean daily forecasted flow of the hydrological model LARSIM-ME forced by air temperature, precipitation and global radiation of the ECMWF ENS re-forecasts for the hindcast dates 10th March 2016 – 09th March 2017 with a lead time of 46 days. Forecast dates 2nd January 2000 to 9th March 2016, in total 1699 forecasts. Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.</p> <p>Forecast values are stored in the variable <em><strong>q_fcast_ens(time=1699, lead_time=46, realization=11, stations=8)</strong></em>, first dimension forecast dates, second dimension lead time, third dimension realization, fourth dimension station.</p> <pre><code>float q_fcast_ens(time=1699, lead_time=46, realization=11, stations=8); :_FillValue = -9999.0f; // float :long_name = "forecast streamflow ensemble"; :units = "m3/s"; :coordinates = "lat lon";</code></pre> <p><strong>Literature</strong></p> <p>Klein, B. & D. Meissner (2019): Semi-operational forecasting system for Rhine, Danube and Elbe to support improved transport cost planning. Deliverable 9.4, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811, <a href="https://imprex.eu/system/files/generated/files/resource/deliverable9-4-imprex-v1-0.pdf">https://imprex.eu/system/files/generated/files/resource/deliverable9-4-imprex-v1-0.pdf</a></p> <p>Ludwig, K. & M. Bremicker (2006): The Water Balance Model LARSIM –Design, Content and Applications. 22. C. Leibundgut, S. Demuth and J. Lange (Eds), Freiburger Schriften zur Hydrologie, Institut für Hydrologie, Universität Freiburg im Breisgau, Freiburg, 141 pp.</p> <p>Meißner, D., B. Klein & M. Ionita (2017): Development of a monthly to seasonal forecast framework tailored to inland waterway transport in central Europe. Hydrol. Earth Syst. Sci. 21(12), 6401</p> <p>Owens, R. & T. R. E. Hewson (2018): ECMWF Forecast User Guide. ECMWF, Reading, doi: 10.21957/m1cs7h</p>
ANGUSII Scenarios: Pathways to 100% Renewable German Energy System
<p>The data set contains an open source energy system model based on the Open Energy Modelling Framework (oemof) for Germany. Results are calculated for different scenarios for the year 2030, 2040 and 2050.</p> <p>A documentation file can be found on <a href="https://github.com/znes/angus-scenarios/blob/3d76ee93d075c3cd83e39ee60ccc34aa13da5732/documentation/scenario-description.pdf">Github.</a></p> <p> </p>
Analysis of performance indicators for German Library Statistics in 2019
<p>Analyse der Berechenbarkeit von Kennzahlen der ISO 11620:2014 auf Basis der Deutschen Bibliotheksstatistik des Berichtsjahres 2019.</p> <p>Analysis of the calculability of performance indicators of ISO 11620:2014 on the basis of the German Library Statistics of the reporting year 2019.</p>
German-Swiss survey on the use of (digital) language resources
<p>This survey on the use of (digital) language resources in German-speaking Switzerland complements the Europe-wide survey conducted by the European Network of e-Lexicography (ENeL) and was carried out in close cooperation with the Leibniz Institute for the German Language (IDS) in Mannheim. The aim was to obtain information on the use of language resources from the user's perspective.</p>
Two German literary magazines of the late 18th century - Die Horen and Athenaeum
<p>7<sup>th </sup>Project Presentation</p>
Historical German Children's Playbooks - 6 Digitized Books with Images, OCR-Fulltext, and Named Entity Recognition
<p>The dataset consists of 6 digitized books with 1750 images and OCR-fulltext.</p> <p>Additionally, named entity recognition has been carried out on basis of flair's de-ner model, see https://github.com/flairNLP for details.</p>
German Arming Sword
15th C German Oakeshott Collection Catalogue Number: OC007 An excellent example of a type XVIII blade, this weapon would have provided a perfect compromise between effectiveness against armored and unarmored opponents. This piece is particularly notable for the still intact original grip, composed of leather over wood. Originally part of the armory at Schloss Erbach, this weapon now resides in the Oakeshott collection. [Support us on Patreon](https://www.patreon.com/oakeshott) [Follow us on Facebook](https://www.facebook.com/oakeshottinstitute) Source: Objaverse 1.0 / Sketchfab
German WWII Bunker Regelbau M178
German World War II bunker modeled after the one in Ijmuiden, the Netherlands. The real one still exists and is covered with grafiti. This type of bunker is called Regelbau M178. Some info on this specific bunker: https://www.tracesofwar.nl/sights/107803/Atlantikwall---Regelbau-M178-MKB-Heerenduin.htm Source: Objaverse 1.0 / Sketchfab
German Goose Wing Broad Axe
"...it was a kind of plane or striking chisel that early Americans used for hewing round logs into square beams." -[Eric Sloane](https://en.wikipedia.org/wiki/Eric_Sloane), [*Museum of Early American Tools*](https://www.amazon.com/Museum-Early-American-Tools-Americana/dp/0486425606/ref=sr_1_1?crid=1MDB8783EQVPS&dchild=1&keywords=museum+of+early+american+tools&qid=1613590281&s=books&sprefix=museum+of+early%2Cstripbooks%2C170&sr=1-1) Modeled from Eric Sloane's [*Museum of Early American Tools*](https://www.amazon.com/Museum-Early-American-Tools-Americana/dp/0486425606/ref=sr_1_1?crid=1MDB8783EQVPS&dchild=1&keywords=museum+of+early+american+tools&qid=1613590281&s=books&sprefix=museum+of+early%2Cstripbooks%2C170&sr=1-1). Textured with [CC0 Textures](https://cc0textures.com) assets. Made with [Blender](https://blender.org). Source: Objaverse 1.0 / Sketchfab
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.