{"status":"OK","data":{"q":"*","total_count":603,"start":0,"spelling_alternatives":{},"items":[{"name":"Replication Data for: Auf dem Weg zur Open Access-Transformation: Eine datenbasierte Analyse des DFG-Förderprogramms „Open Access Publizieren“","type":"dataset","url":"https://doi.org/10.26165/JUELICH-DATA/WLCJ4X","global_id":"doi:10.26165/JUELICH-DATA/WLCJ4X","description":"Datenset zum Artikel: Auf dem Weg zur Open Access-Transformation: Eine datenbasierte Analyse des DFG-Förderprogramms „Open Access Publizieren“, eingereicht bei \"Informationspraxis\". Seit 2010 stellt das DFG-Programm „Open Access Publizieren“ ein zentrales Instrument zur institutionellen Förderung von Open Access-Publikationen an deutschen Universitäten dar. Im Zuge einer DFG-Programmevaluation hat die Zentralbibliothek des Forschungszentrums Jülich eine Datenanalyse erstellt, die den Publikationsoutput der geförderten Universitäten in den Jahren 2011-2017 beleuchtet. Kernergebnisse der Studie sowie daraus abgeleitete Handlungsempfehlungen für zukünftige Monitoring-Verfahren werden in diesem Artikel präsentiert.","published_at":"2020-07-14T09:05:52Z","publisher":"Central Library","citationHtml":"Barbers, Irene; Rosenberger, Sonja; Mittermaier, Bernhard, 2020, \"Replication Data for: Auf dem Weg zur Open Access-Transformation: Eine datenbasierte Analyse des DFG-F&ouml;rderprogramms &bdquo;Open Access Publizieren&ldquo;\", <a href=\"https://doi.org/10.26165/JUELICH-DATA/WLCJ4X\" target=\"_blank\">https://doi.org/10.26165/JUELICH-DATA/WLCJ4X</a>, J&uuml;lich DATA, V4","identifier_of_dataverse":"zb","name_of_dataverse":"Central Library","citation":"Barbers, Irene; Rosenberger, Sonja; Mittermaier, Bernhard, 2020, \"Replication Data for: Auf dem Weg zur Open Access-Transformation: Eine datenbasierte Analyse des DFG-Förderprogramms „Open Access Publizieren“\", https://doi.org/10.26165/JUELICH-DATA/WLCJ4X, Jülich DATA, V4","storageIdentifier":"s3://10.26165/JUELICH-DATA/WLCJ4X","keywords":["Open Access","Open Access Monitoring","DFG","Publikationsfonds","Article Processing Charge"],"subjects":["Computer and Information Science"],"fileCount":11,"versionId":267,"versionState":"RELEASED","majorVersion":4,"minorVersion":2,"createdAt":"2020-04-30T13:15:32Z","updatedAt":"2022-03-16T15:09:40Z","contacts":[{"name":"Barbers, Irene","affiliation":"Forschungszentrum Jülich, Zentralbibliothek"}],"publications":[{"citation":"Heidler, Richard; Holzer, Angela; Weihberg, Roland (2020): Das DFG-Förderprogramm Open Access Publizieren. Bericht über die Förderung. Unter Mitarbeit von Michael Ploder, Jürgen Streicher, Angelika Sauer, Florian Holzinger, Michaela Dvorzak, Irene Barbers et al. Hg. v. Deutsche Forschungsgemeinschaft. Bonn.","url":"https://www.dfg.de/download/pdf/dfg_im_profil/zahlen_fakten/programm_evaluation/bericht_open_access_2020.pdf"},{"citation":"Das DFG-Förderprogramm Open Access Publizieren: Anhänge A-1 und A-2. Bericht über die Förderung (2020). In: Deutsche Forschungsgemeinschaft (Hg.): Das DFG-Förderprogramm Open Access Publizieren. Bericht über die Förderung. Unter Mitarbeit von Michael Ploder, Jürgen Streicher, Angelika Sauer, Florian Holzinger, Michaela Dvorzak, Irene Barbers et al. Bonn.","url":"https://www.dfg.de/download/pdf/dfg_im_profil/zahlen_fakten/programm_evaluation/bericht_open_access_2020_anhang.pdf"},{"citation":"Barbers, Irene; Rosenberger, Sonja; Mittermaier, Bernhard 2020. Auf dem Weg zur Open Access Transformation: Eine datenbasierte Analyse des DFG-Förderprogramms \"Open Acces Publizieren\". Informationspraxis 6(2), 73240","url":"https://doi.org/10.11588/ip.2020.2.73240"}],"authors":["Barbers, Irene","Rosenberger, Sonja","Mittermaier, Bernhard"]},{"name":"ELPVPower: A dataset for large scale PV power prediction using EL images of cells","type":"dataset","url":"https://doi.org/10.26165/JUELICH-DATA/TVWUUP","global_id":"doi:10.26165/JUELICH-DATA/TVWUUP","description":"Measurements are provided in the folder `data` as PNG-Images. The original measurements have been rescaled to the range [0, 255] globally, such that intensities between measurements are comparable. The dataset comes with a file `data.csv` that provides additional meta data associated with the measurements. This includes: peak_power: The measured maximum power of the module in [W] nominal_power: The given nominal power of the module in [W] pressure: The pressure that has been applied during mechanical load testing on a linear scale [0, 1] excitation_class: Indicates, whether this measurement has been taken at a `high` or `low` excitation current module_type: Module types as specified in the paper module_instance: Some of the module identities have been measured multiple times under varying conditions. Here, we specify the distinct instances power_group: Discretized `peak_power` used for stratified sampling fold_i_train: Indicates, whether this module is part of the i'th training fold of the crossvalidation","published_at":"2020-08-28T10:42:11Z","publisher":"EL images Dataverse","citationHtml":"Hoffmann, Mathis; Buerhop-Lutz, Claudia; Reeb, Luca; Pickel, Tobias; Winkler, Thilo; Doll, Bernd; W&uuml;rfl, Tobias; Peters, Ian Marius; Brabec, Christoph J.; Maier, Andreas; Christlein, Vincent, 2020, \"ELPVPower: A dataset for large scale PV power prediction using EL images of cells\", <a href=\"https://doi.org/10.26165/JUELICH-DATA/TVWUUP\" target=\"_blank\">https://doi.org/10.26165/JUELICH-DATA/TVWUUP</a>, J&uuml;lich DATA, V1","identifier_of_dataverse":"hiern_elimages","name_of_dataverse":"EL images Dataverse","citation":"Hoffmann, Mathis; Buerhop-Lutz, Claudia; Reeb, Luca; Pickel, Tobias; Winkler, Thilo; Doll, Bernd; Würfl, Tobias; Peters, Ian Marius; Brabec, Christoph J.; Maier, Andreas; Christlein, Vincent, 2020, \"ELPVPower: A dataset for large scale PV power prediction using EL images of cells\", https://doi.org/10.26165/JUELICH-DATA/TVWUUP, Jülich DATA, V1","storageIdentifier":"s3://10.26165/JUELICH-DATA/TVWUUP","subjects":["Computer and Information Science","Earth and Environmental Sciences","Engineering","Physics"],"fileCount":721,"versionId":31,"versionState":"RELEASED","majorVersion":1,"minorVersion":3,"createdAt":"2020-08-27T16:49:49Z","updatedAt":"2022-03-16T15:31:06Z","contacts":[{"name":"Buerhop-Lutz, Claudia","affiliation":"Forschungszentrum Jülich GmbH"}],"publications":[{"url":"https://arxiv.org/abs/2009.14712"}],"authors":["Hoffmann, Mathis","Buerhop-Lutz, Claudia","Reeb, Luca","Pickel, Tobias","Winkler, Thilo","Doll, Bernd","Würfl, Tobias","Peters, Ian Marius","Brabec, Christoph J.","Maier, Andreas","Christlein, Vincent"]},{"name":"Replication Data for: HOPS: high-performance library for (non-)uniform sampling of convex-constrained models","type":"dataset","url":"https://doi.org/10.26165/JUELICH-DATA/YXLFKJ","global_id":"doi:10.26165/JUELICH-DATA/YXLFKJ","description":"This collection contains showcased models and pre-processing results (determining an independent flux space and rounding) that are used as basis for benchmarking the uniform sampling performance of the CHHR implementations of the HOPS library [10.1093/bioinformatics/btaa872] (https://github.com/modsim/hops) and the COBRA toolbox [doi.org/10.1038/s41596-018-0098-2]. Here, the pre-processing was performed with the COBRA toolbox for all benchmarks to guarantee fair comparability. The models are split in two classes: 1) simplices with 64, 256, 512, 1024, and 2048 dimensions; 2) metabolic network models (e_coli_core, iAT_PLT_256, iJO1366, RECON1, Recon2, Recon3D_301) [http://bigg.ucsd.edu/], [doi.org/10.1038/nbt.2488] formulated in SBML format [doi.org/10.1093/bioinformatics/btg015]. For each of these models, the left hand side of the constraint system (called A), the right hand side (called b), a shift of the transformation from sampling space, meaning the null space, to parameter space, meaning the full space of the model (called p_shift), the linear transformation from sampling space to parameter space (called N) and the Chebyshev center (called start) are provided. These files come in rounded and unrounded types, indicating if the rounding algorithm has been applied. For convenience, sometimes the rounding transformation is also given (indicated by T). The rounding transformation is only given in the rounded form, because it is identity otherwise. Additionally, the dataset contains an E. coli WT model with carbon atom transitions and isotope labelling measurements [10.1038/nprot.2009.58] used in the Bayesian inference non-uniform sampling example. The E. coli WT model is contained in a single FluxML file [doi.org/10.3389/fmicb.2019.01022]. To reproduce the data generated with the E. coli WT model, the rounding was calculated using the HOPS library and independent fluxes were provided by the high-performance simulator 13CFLUX2 [doi.org/10.1093/bioinformatics/bts646].","published_at":"2020-12-18T21:32:41Z","publisher":"Metabolic Networks","citationHtml":"Johann F. Jadebeck; Axel Theorell; Samuel Leweke; Katharina N&ouml;h, 2020, \"Replication Data for: HOPS: high-performance library for (non-)uniform sampling of convex-constrained models\", <a href=\"https://doi.org/10.26165/JUELICH-DATA/YXLFKJ\" target=\"_blank\">https://doi.org/10.26165/JUELICH-DATA/YXLFKJ</a>, J&uuml;lich DATA, V1","identifier_of_dataverse":"metabolic_networks","name_of_dataverse":"Metabolic Networks","citation":"Johann F. Jadebeck; Axel Theorell; Samuel Leweke; Katharina Nöh, 2020, \"Replication Data for: HOPS: high-performance library for (non-)uniform sampling of convex-constrained models\", https://doi.org/10.26165/JUELICH-DATA/YXLFKJ, Jülich DATA, V1","storageIdentifier":"s3://10.26165/JUELICH-DATA/YXLFKJ","keywords":["Metabolic Network Model HOPS Sampling MCMC Rounding"],"subjects":["Medicine, Health and Life Sciences"],"fileCount":122,"versionId":290,"versionState":"RELEASED","majorVersion":1,"minorVersion":2,"createdAt":"2020-10-27T13:26:15Z","updatedAt":"2022-03-17T10:51:26Z","contacts":[{"name":"Johann F. Johann","affiliation":"Forschungszentrum Jülich GmbH"},{"name":"Katharina Nöh","affiliation":"Forschungszentrum Jülich GmbH"}],"publications":[{"citation":"Johann F Jadebeck, Axel Theorell, Samuel Leweke, Katharina Nöh, HOPS: high-performance library for (non-)uniform sampling of convex-constrained models, Bioinformatics, 2020, btaa872","url":"https://doi.org/10.1093/bioinformatics/btaa872"}],"authors":["Johann F. Jadebeck","Axel Theorell","Samuel Leweke","Katharina Nöh"]},{"name":"Data for: Stimulus transformation into motor action: dynamic graph analysis reveals a posterior-to-anterior shift in brain network communication of older subjects","type":"dataset","url":"https://doi.org/10.26165/JUELICH-DATA/T1PKNZ","global_id":"doi:10.26165/JUELICH-DATA/T1PKNZ","description":"Data description This dataset includes EEG recordings of 18 younger healthy subjects (18-35yrs) and 24 older healthy subjects (60+yrs) while they performed a visually cued finger tapping task. The task includes a visually-cued index finger tapping task and a vision-only control condition. Preprocessing: The data is preprocessed in the following manner: The raw data were first bandpass filtered from 0.5 to 48 Hz to increase the signal-to-noise ratio and to avoid a potential of 50 Hz as an electric current artifact and then downsampled from 2500 Hz to 200 Hz. Next, the continuous raw EEG data were visually inspected for paroxysmal and muscular artifacts not related to eye blinks. Noisy portions of the signal were excluded from further analysis. All trials in the Visually-cued condition with incorrect responses were excluded, as well as trials with response times (RT) greater than 1s. The data is epoched (-1.5 to 2.5 s) centered around stimulus onset. After segmenting the continuous EEG data, the obtained epochs were corrected for artifacts. First, epochs were rejected if the amplitude over the entire epoch was larger than 100 μV or showed an abnormal drift that exceeded 75 μV. Next, a semi-automated procedure based on independent component analysis (ICA) was used to identify epochs contaminated by artifacts such as blinks, eye movements, muscle activity, and infrequent single-channel noise. The independent component decomposition was performed using the Infomax ICA algorithm implemented in EEGLAB. The ADJUST algorithm (Mognon et al., 2011) was then used to identify and reject components containing blink/oculomotor or other artifacts that were distinguishable from the rest of the brain activity. Noisy channels were detected automatically by EEGLAB and interpolated using spherical spline interpolation. Finally, the artifact-free trials were average-referenced and baseline-corrected.","published_at":"2020-12-09T11:30:37Z","publisher":"Computational Neurology Dataverse","citationHtml":"Rosjat, Nils; Wang, Bin A.; Liu, Liqing; Fink, Gereon R.; Daun, Silvia, 2020, \"Data for: Stimulus transformation into motor action: dynamic graph analysis reveals a posterior-to-anterior shift in brain network communication of older subjects\", <a href=\"https://doi.org/10.26165/JUELICH-DATA/T1PKNZ\" target=\"_blank\">https://doi.org/10.26165/JUELICH-DATA/T1PKNZ</a>, J&uuml;lich DATA, V1","identifier_of_dataverse":"computational-neurology","name_of_dataverse":"Computational Neurology Dataverse","citation":"Rosjat, Nils; Wang, Bin A.; Liu, Liqing; Fink, Gereon R.; Daun, Silvia, 2020, \"Data for: Stimulus transformation into motor action: dynamic graph analysis reveals a posterior-to-anterior shift in brain network communication of older subjects\", https://doi.org/10.26165/JUELICH-DATA/T1PKNZ, Jülich DATA, V1","storageIdentifier":"s3://10.26165/JUELICH-DATA/T1PKNZ","keywords":["EEG","Aging","Externally triggerede movements"],"subjects":["Medicine, Health and Life Sciences"],"fileCount":137,"versionId":264,"versionState":"RELEASED","majorVersion":1,"minorVersion":1,"createdAt":"2020-09-10T11:14:57Z","updatedAt":"2022-03-16T15:08:13Z","contacts":[{"name":"Rosjat, Nils","affiliation":"Forschungszentrum Jülich"}],"publications":[{"citation":"Stimulus transformation into motor action: dynamic graph analysis reveals a posterior-to-anterior shift in brain network communication of older subjects Nils Rosjat, Bin A. Wang, Liqing Liu, Gereon R. Fink, Silvia Daun bioRxiv 2020.02.26.966325","url":"https://www.biorxiv.org/content/10.1101/2020.02.26.966325v3"}],"authors":["Rosjat, Nils","Wang, Bin A.","Liu, Liqing","Fink, Gereon R.","Daun, Silvia"]},{"name":"EL Dataset of PV modules","type":"dataset","url":"https://doi.org/10.26165/JUELICH-DATA/GCBNMA","global_id":"doi:10.26165/JUELICH-DATA/GCBNMA","description":"This repository provides a dataset of solar cell images extracted from high-resolution electroluminescence images of photovoltaic modules. The dataset contains 2,624 samples of 300x300 pixels 8-bit grayscale images of functional and defective solar cells with varying degree of degradations extracted from 44 different solar modules. The defects in the annotated images are either of intrinsic or extrinsic type and are known to reduce the power efficiency of solar modules. All images are normalized with respect to size and perspective. Additionally, any distortion induced by the camera lens used to capture the EL images was eliminated prior to solar cell extraction. Every image is annotated with a defect probability (a floating point value between 0 and 1) and the type of the solar module (either mono- or polycrystalline) the solar cell image was originally extracted from. The individual images are stored in the images directory and the corresponding annotations in labels.csv. --More explanations in the README file--","published_at":"2020-08-06T12:14:50Z","publisher":"EL images Dataverse","citationHtml":"Buerhop-Lutz, Claudia; Deitsch, Sergiu; Maier, Andreas; Gallwitz, Florian; Berger, Stephan; Doll, Bernd; Hauch, Jens; Camus, Christian; Brabec, Christoph J., 2020, \"EL Dataset of PV modules\", <a href=\"https://doi.org/10.26165/JUELICH-DATA/GCBNMA\" target=\"_blank\">https://doi.org/10.26165/JUELICH-DATA/GCBNMA</a>, J&uuml;lich DATA, V1","identifier_of_dataverse":"hiern_elimages","name_of_dataverse":"EL images Dataverse","citation":"Buerhop-Lutz, Claudia; Deitsch, Sergiu; Maier, Andreas; Gallwitz, Florian; Berger, Stephan; Doll, Bernd; Hauch, Jens; Camus, Christian; Brabec, Christoph J., 2020, \"EL Dataset of PV modules\", https://doi.org/10.26165/JUELICH-DATA/GCBNMA, Jülich DATA, V1","storageIdentifier":"s3://10.26165/JUELICH-DATA/GCBNMA","keywords":["EL-imaging, visual inspection, machine learning"],"subjects":["Computer and Information Science","Earth and Environmental Sciences","Engineering"],"fileCount":1,"versionId":263,"versionState":"RELEASED","majorVersion":1,"minorVersion":5,"createdAt":"2020-08-06T12:05:44Z","updatedAt":"2022-03-16T15:29:14Z","contacts":[{"name":"Buerhop-Lutz, Claudia","affiliation":"Forschungszentrum Jülich"}],"publications":[{"citation":"Buerhop-Lutz, C.; Deitsch, S.; Maier, A.; Gallwitz, F.; Berger, S.; Doll, B.; Hauch, J.; Camus, C. &amp; Brabec, C. J. A Benchmark for Visual Identification of Defective Solar Cells in Electroluminescence Imagery. European PV Solar Energy Conference and Exhibition (EU PVSEC), 2018."},{"citation":"Deitsch, S.; Buerhop-Lutz, C.; Maier, A. K.; Gallwitz, F. &amp; Riess, C. Segmentation of Photovoltaic Module Cells in Electroluminescence Images. CoRR, 2018, abs/1806.06530"},{"citation":"Deitsch, S.; Christlein, V.; Berger, S.; Buerhop-Lutz, C.; Maier, A.; Gallwitz, F. &amp; Riess, C. Automatic classification of defective photovoltaic module cells in electroluminescence images. Solar Energy, Elsevier BV, 2019, 185, 455-468."}],"authors":["Buerhop-Lutz, Claudia","Deitsch, Sergiu","Maier, Andreas","Gallwitz, Florian","Berger, Stephan","Doll, Bernd","Hauch, Jens","Camus, Christian","Brabec, Christoph J."]},{"name":"Replication Data for: Measurement of deuteron carbon vector analyzing powers in the kinetic energy range 170–380 MeV","type":"dataset","url":"https://doi.org/10.26165/JUELICH-DATA/RDWNFU","global_id":"doi:10.26165/JUELICH-DATA/RDWNFU","description":"This dataset contains the published vector analyzing powers as function of scattering angle and incident beam energy.","published_at":"2020-08-19T11:02:50Z","publisher":"Jülich Electric Dipole moment Investigations (JEDI)","citationHtml":"M&uuml;ller, Fabian, 2020, \"Replication Data for: Measurement of deuteron carbon vector analyzing powers in the kinetic energy range 170&ndash;380 MeV\", <a href=\"https://doi.org/10.26165/JUELICH-DATA/RDWNFU\" target=\"_blank\">https://doi.org/10.26165/JUELICH-DATA/RDWNFU</a>, J&uuml;lich DATA, V1","identifier_of_dataverse":"jedi","name_of_dataverse":"Jülich Electric Dipole moment Investigations (JEDI)","citation":"Müller, Fabian, 2020, \"Replication Data for: Measurement of deuteron carbon vector analyzing powers in the kinetic energy range 170–380 MeV\", https://doi.org/10.26165/JUELICH-DATA/RDWNFU, Jülich DATA, V1","storageIdentifier":"s3://10.26165/JUELICH-DATA/RDWNFU","keywords":["deuteron-carbon elastic scattering","polarized deuterons","vector analyzing power"],"subjects":["Physics"],"fileCount":1,"versionId":17,"versionState":"RELEASED","majorVersion":1,"minorVersion":1,"createdAt":"2020-08-19T09:20:08Z","updatedAt":"2020-09-02T09:02:27Z","contacts":[{"name":"Pretz, Jörg","affiliation":"Forschungszentrum Jülich"}],"publications":[{"citation":"Müller, F., Żurek, M., Bagdasarian, Z. et al. Measurement of deuteron carbon vector analyzing powers in the kinetic energy range 170–380 MeV. Eur. Phys. J. A 56, 211 (2020)."}],"authors":["Müller, Fabian"]},{"name":"Polar Stratospheric Cloud Simulations for CRISTA-NF","type":"dataset","url":"https://doi.org/10.26165/JUELICH-DATA/GGXJ5D","global_id":"doi:10.26165/JUELICH-DATA/GGXJ5D","description":"This data set contains simulated infrared limb emission spectra in the presence of polar stratospheric clouds (PSCs) for the air-borne CRyogenic Infrared Spectrometers and Telescopes for the Atmosphere - New Frontiers (CRISTA-NF). The atmospheric background conditions for the simulations represent the Arctic winter 2009/2010, in which PSCs were measured during the RECONCILE campaign. While the PSCs of 0.5, 1, 2, 4, and 8 km thickness are located between 13 and 30 km altitude, the spectra are available for altitudes between 10 km and the flight altitude of 18.4 km with 100 m vertical spacing. Two spectral ranges, from 785 to 840 cm-1 and 940 to 965 cm-1 are provided. The simulated PSC scenarios comprise pure ice, NAT, and STS scenarios, described by mono-modal log-normal particle size distributions, and two mixed scenarios STS/NAT and small NAT/large NAT, both described by bi-modal log-normal particle size distributions. For STS PSCs we followed the setup in Spang et al. (2012) and simulated three mixtures with wt% H2SO4/HNO of 2/48, 25/25, and 48/2 for five volume densities between 0.1 and 10 µm3/cm3 and four median radii between 0.1 and 1 µm with 0.3 µm in addition. For ice PSCs we extended the range to smaller volume densities, covering the range from 0.1 to 100 µm3/cm3 in 7 steps, and 6 median radii between 1 and 10 µm. Different to Spang et al. (2012) we derived the NAT particle size distribution from the HNO3 volume mixing ratio of 1 to 15 ppbv in 1 ppbv steps and median radii between 0.5 and 8 µm, where we added 6 and 8 µm and used a finer sampling of 0.5 µm between 0.5 and 4 µm. For the mixed STS/NAT scenario we combined STS with wt% H2SO4/HNO of 2/48, volume densities of 5 and 10 µm3/cm3, and median radii of 0.1, 0.3 ,1 µm with NAT volume mixing ratios of 5, 10, 15 ppbv, and median radii between 0.5 and 3.5 µm. For the bi-modal NAT simulation the data set contains simulations for 10 ppbv where the partitioning between the first and the second mode is 3/7, 5/5, and 7/3 ppbv. The median radii of the first mode were kept smaller (0.5 to 2.5 µm) than in the second mode (1 to 8 µm).","published_at":"2020-11-19T08:28:40Z","publisher":"Earth System Science","citationHtml":"Kalicinsky, Christoph; Grie&szlig;bach, Sabine; Spang, Reinhold, 2020, \"Polar Stratospheric Cloud Simulations for CRISTA-NF\", <a href=\"https://doi.org/10.26165/JUELICH-DATA/GGXJ5D\" target=\"_blank\">https://doi.org/10.26165/JUELICH-DATA/GGXJ5D</a>, J&uuml;lich DATA, V1","identifier_of_dataverse":"ess","name_of_dataverse":"Earth System Science","citation":"Kalicinsky, Christoph; Grießbach, Sabine; Spang, Reinhold, 2020, \"Polar Stratospheric Cloud Simulations for CRISTA-NF\", https://doi.org/10.26165/JUELICH-DATA/GGXJ5D, Jülich DATA, V1","storageIdentifier":"s3://10.26165/JUELICH-DATA/GGXJ5D","keywords":["IR","limb geometry","polar stratospheric clouds","nitric acid trihydrate"],"subjects":["Earth and Environmental Sciences"],"fileCount":0,"versionId":265,"versionState":"RELEASED","majorVersion":1,"minorVersion":2,"createdAt":"2020-11-18T13:53:41Z","updatedAt":"2022-03-16T12:29:39Z","contacts":[{"name":"Grießbach, Sabine","affiliation":"Forschungszentrum Jülich"}],"publications":[{"citation":"Kalicinsky, C., Griessbach, S., and Spang, R.: A new method to detect and classify polar stratospheric nitric acid trihydrate clouds derived from radiative transfer simulations and its first application to airborne infrared limb emission observations, Atmos. Meas. Tech., 14, 1893–1915, 2021.","url":"https://amt.copernicus.org/articles/14/1893/2021/"}],"authors":["Kalicinsky, Christoph","Grießbach, Sabine","Spang, Reinhold"]},{"name":"Replication Data for: Trajectory Data Set from the study on the 2017 BC wildfire detection","type":"dataset","url":"https://doi.org/10.26165/JUELICH-DATA/ATC1MZ","global_id":"doi:10.26165/JUELICH-DATA/ATC1MZ","description":"These are trajectory data calculated for the study by Hooghiem et al. (Atmos Chem. Phys. , 2020). In this study, wildfire-influenced airmasses were observed and the trajectories were used to trace back the origin.","published_at":"2020-11-20T07:50:39Z","publisher":"CLaMS Dataverse","citationHtml":"Groo&szlig;, Jens-Uwe, 2020, \"Replication Data for: Trajectory Data Set from the study on the 2017 BC wildfire detection\", <a href=\"https://doi.org/10.26165/JUELICH-DATA/ATC1MZ\" target=\"_blank\">https://doi.org/10.26165/JUELICH-DATA/ATC1MZ</a>, J&uuml;lich DATA, V1","identifier_of_dataverse":"clams","name_of_dataverse":"CLaMS Dataverse","citation":"Grooß, Jens-Uwe, 2020, \"Replication Data for: Trajectory Data Set from the study on the 2017 BC wildfire detection\", https://doi.org/10.26165/JUELICH-DATA/ATC1MZ, Jülich DATA, V1","storageIdentifier":"s3://10.26165/JUELICH-DATA/ATC1MZ","subjects":["Earth and Environmental Sciences"],"fileCount":4,"versionId":280,"versionState":"RELEASED","majorVersion":1,"minorVersion":1,"createdAt":"2020-11-19T15:30:25Z","updatedAt":"2022-03-16T17:29:26Z","contacts":[{"name":"Grooß, Jens-Uwe","affiliation":"Forschungszentrum Jülich"}],"publications":[{"citation":"Hooghiem, Joram J. D., Maria Elena Popa, Thomas Röckmann, Jens-Uwe Grooß, Ines Tritscher, Rolf Müller, Rigel Kivi, and Huilin Chen, Wildfire smoke in the lower stratosphere identified by in situ CO observations, Atmos. Chem. Phys., 20, 13985-14003, 2020.","url":"https://doi.org/10.5194/acp-20-13985-2020"}],"authors":["Grooß, Jens-Uwe"]},{"name":"Data for: Flux periodic oscillations and phase-coherent transport in GeTe nanowire-based devices","type":"dataset","url":"https://doi.org/10.26165/JUELICH-DATA/M1IQVG","global_id":"doi:10.26165/JUELICH-DATA/M1IQVG","description":"Flux periodic oscillations and phase-coherent transport in GeTe nanowire-based devices: Source data underlying Figs. 1-4 and Supplementary Figs. 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