cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8297256
title: Synthetic Airborne Intruder Dataset: A dataset based on High-Resolution Inpainting for Safety Critical Detect and Avoid

description: Modern machine learning techniques have shown tremendous potential, especially for object detection on camera images. For this reason, they are also used to enable safety-critical automated processes such as autonomous drone flights. We present a study on object detection for Detect and Avoid, a safety critical function for drones that detects air traffic during automated flights for safety reasons. An ill-posed problem is the generation of good and especially large data sets, since detection itself is the corner case. Most models suffer from limited ground truth in raw data, e.g. recorded air traffic or frontal flight with a small aircraft. It often leads to poor and critical detection rates. We overcome this problem by using inpainting methods to bootstrap the dataset such that it explicitly contains the corner cases of the raw data. We provide an overview of inpainting methods and generative models and present an example pipeline given a small annotated dataset. We validate our method by generating a high-resolution dataset and present it to an independent object detector that was fully trained on real data. This dataset is represented in the following repository. The dataset is structured as follows: # Synthetic Airborne Intruder Dataset This dataset was syntheticaly generated using an adapted [Pix2Pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) with different background images and object segementations. Each image contains one object instance. The annotations are in the COCO annotation format. ## Data Structure<br> Synthetic Dataset Root:<br> --train<br> |--images<br> |--instances.json<br> --val<br> |--images<br> |--instances.json<br> --test<br> |--images<br> |--instances.json<br> --Background_Sources<br> |--sources_train.csv<br> |--sources_val.csv<br> |--sources_test.cs<br> --README.md ## Categories | Id | Name | Instances over all splits |<br> | ---| --- | --- |<br> | 0 | large airplane | 1695 | <br> | 1 | small airplane | 1255 | <br> | 2 | very small airplane | 46 | <br> | 3 | helicopter | 2201 | <br> | 4 | drone | 961 |<br> | 5 | hot air balloon | 315 |<br> | 6 | paraglider | 565 |<br> | 7 | airship | 42 |<br> | 8 | UFO | 0 | ### Note:<br> UFO is a placeholder for future expansion of the dataset. ## Splits<br> The dataset consists of 3 splits: train 5900 images, val 590 images, test 590 images.<br> The Number of instances per class and per split can be seen in the table below: Class | train | val | test<br> -------|-------|-----|--------<br> large airplane | 1416 | 142 | 137<br> small airplane | 1046 | 96 | 113<br> very small airplane | 38 | 2 | 6<br> helicopter | 1812 | 206 | 183<br> drone | 800 | 86 | 75<br> hot air balloon | 268 | 21 | 26<br> paragliders | 492 | 32 | 41<br> airship | 28 | 5 | 9<br> UFO | 0 | 0 | 0 ## Sources<br> The sources of the background images can be found in the files [here](./Background_Sources/).
DOI: 10.5281/zenodo.8301120
title: Synthetic Airborne Intruder Dataset: A dataset based on High-Resolution Inpainting for Safety Critical Detect and Avoid

description: Modern machine learning techniques have shown tremendous potential, especially for object detection on camera images. For this reason, they are also used to enable safety-critical automated processes such as autonomous drone flights. We present a study on object detection for Detect and Avoid, a safety critical function for drones that detects air traffic during automated flights for safety reasons. An ill-posed problem is the generation of good and especially large data sets, since detection itself is the corner case. Most models suffer from limited ground truth in raw data, e.g. recorded air traffic or frontal flight with a small aircraft. It often leads to poor and critical detection rates. We overcome this problem by using inpainting methods to bootstrap the dataset such that it explicitly contains the corner cases of the raw data. We provide an overview of inpainting methods and generative models and present an example pipeline given a small annotated dataset. We validate our method by generating a high-resolution dataset and present it to an independent object detector that was fully trained on real data. This dataset is represented in the following repository. The dataset is structured as follows: # Synthetic Airborne Intruder Dataset This dataset was syntheticaly generated using an adapted [Pix2Pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) with different background images and object segementations. Each image contains one object instance. The annotations are in the COCO annotation format. ## Data Structure<br> Synthetic Dataset Root:<br> --train<br> |--images<br> |--instances.json<br> --val<br> |--images<br> |--instances.json<br> --test<br> |--images<br> |--instances.json<br> --Background_Sources<br> |--sources_train.csv<br> |--sources_val.csv<br> |--sources_test.cs<br> --README.md ## Categories | Id | Name | Instances over all splits |<br> | ---| --- | --- |<br> | 0 | large airplane | 1695 | <br> | 1 | small airplane | 1255 | <br> | 2 | very small airplane | 46 | <br> | 3 | helicopter | 2201 | <br> | 4 | drone | 961 |<br> | 5 | hot air balloon | 315 |<br> | 6 | paraglider | 565 |<br> | 7 | airship | 42 |<br> | 8 | UFO | 0 | ### Note:<br> UFO is a placeholder for future expansion of the dataset. ## Splits<br> The dataset consists of 3 splits: train 5900 images, val 590 images, test 590 images.<br> The Number of instances per class and per split can be seen in the table below: Class | train | val | test<br> -------|-------|-----|--------<br> large airplane | 1416 | 142 | 137<br> small airplane | 1046 | 96 | 113<br> very small airplane | 38 | 2 | 6<br> helicopter | 1812 | 206 | 183<br> drone | 800 | 86 | 75<br> hot air balloon | 268 | 21 | 26<br> paragliders | 492 | 32 | 41<br> airship | 28 | 5 | 9<br> UFO | 0 | 0 | 0 ## Sources<br> The sources of the background images can be found in the files [here](./Background_Sources/).
DOI: 10.25345/c5n29ph6d
title: MassIVE MSV000092788 - Identification of antioxidant metabolites from Five Plants of the Polynesian Pharmacopoeia and Cosmetopoeia for skin care
title: Oxidative stress contributes to impairment of skin health and aspects in the wound healing process for pathologies such as acne, psoriasis or skin cancer. Five Polynesian medicinal plants, among the most traditionally used for skin care (cosmetopoeia and pharmacopoeia) are studied herein for their antioxidant properties: Calophyllum inophyllum, Gardenia taitensis, Curcuma longa, Cordia subcordata and Ficus prolixa. For this purpose, plant extracts were submitted to in vitro bioassays related to antioxidant properties and their bioactive constituents were identified by metabolomic analytical approach. UHPLC-MS/MS analysis was performed leading to characterize 61 metabolites. Compounds annotated for F. prolixa and C. subcordata were reported for the first time in those indigenous trees. Antioxidant properties were evaluated by TPC, DPPH and FRAP assays. F. prolixa extract was the most active one within antioxidant properties similar to ascorbic acid and showed antioxidant intracellular activity on HaCaT model by AOP1 assay. On-Line-HPLC DPPH allowed the identification of phenolic bioactive compounds. These results highlight the potential of F. prolixa aerial roots as a source of antioxidant for skin care topical applications.
DOI: 10.5281/zenodo.7263336
title: PeopleAndWriting - método de análisis paleográfico

description: Resumen de los aspectos clave de carácter paleográfico a analizar dentro del marco del proyecto europeo de investigación "The Secret Life of Writing: People, Script and Ideas in the Iberian Peninsula (c. 900-1200)". [This project has received funding from the European Union’s EU Framework Programme for Research and Innovation Horizon 2020 under Grant Agreement No. 850604. The European Research Agency is not responsible for any use that may be made of the information it contains.]
DOI: 10.5281/zenodo.8301156
title: PeopleAndWriting - método de análisis paleográfico

description: Resumen de los aspectos clave de carácter paleográfico a analizar dentro del marco del proyecto europeo de investigación "The Secret Life of Writing: People, Script and Ideas in the Iberian Peninsula (c. 900-1200)". [This project has received funding from the European Union’s EU Framework Programme for Research and Innovation Horizon 2020 under Grant Agreement No. 850604. The European Research Agency is not responsible for any use that may be made of the information it contains.]
DOI: 10.34740/kaggle/dsv/6388920
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.34740/kaggle/dsv/6388930
title: UNET Lung Segmentation Weights for Chest X Rays
DOI: 10.20375/0000-0010-05d3-a
title: Documentation of Data Collection.pdf
DOI: 10.20375/0000-0010-05d4-9
title: terms_en_1999-2023.csv.zip
DOI: 10.20375/0000-0010-05d5-8
title: terms_ru_1999-2023.csv.zip
DOI: 10.20375/0000-0010-05d6-7
title: transcripts_en_2023-08-30.json.zip
DOI: 10.20375/0000-0010-05d7-6
title: transcripts_ru_2023-08-30.json.zip
DOI: 10.48320/86673554-7c09-4569-91cf-c69119a5a8cb
title: The President’s Words: A Term Frequency Analysis of Putin’s and Medvedev’s Statements in Official Kremlin Transcripts
DOI: 10.6084/m9.figshare.24057558
title: Principal component and linear discriminant analyses for the classification of hominoid primate specimens based on bone shape data

description: Supplementary material
DOI: 10.6084/m9.figshare.24057558.v1
title: Principal component and linear discriminant analyses for the classification of hominoid primate specimens based on bone shape data

description: Supplementary material
DOI: 10.6084/m9.figshare.23668494
title: data-solow & institutional quality data

description: working-age population (ages 15-64)growth rate was extracted from the World Bank Open Data databases .he real GDP (Y) ,the working-age population (L) and the depreciation rate δ were extracted from the Penn World Table 9.1 . The growth rate of technological progress g is assumed to be constant and equal to 1%.the average share of real investment (inclusive of government investment) was calculated based on PWT 9.1 data. The Regulator Quality Index, Corruption Index, Voice and Accountability Index, Political Stabili-ty/No Violence Index, Government Effectiveness Index, and Rule of Law Index were obtained from the Worldwide Governance Indicators (WGI) dataset.This dataset include 4 different samples of countries, with different economic and institutional environment.
DOI: 10.5517/ccdc.csd.cc2d0pmr
title: CCDC 2205204: Experimental Crystal Structure Determination

description: Related Article: Michael Ricca, Shaolei Yao, Tommy Le, Jonathan M. White, Paul S. Donnelly, Mark A. Rizzacasa|2023|Org.Biomol.Chem.|21|6789|doi:10.1039/D3OB01085F
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2ftmzw
title: CCDC 2258969: Experimental Crystal Structure Determination

description: Related Article: Dipendu Mandal, Zheng-Wang Qu, Stefan Grimme, Douglas W. Stephan|2023|Chem.Commun.|59|10508|doi:10.1039/D3CC02684A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fwb7x
title: CCDC 2260589: Experimental Crystal Structure Determination

description: Related Article: Franz A. Mautner, Roland C. Fischer, Ana Torvisco, Saskia Speed, Ramon Vicente, Nahed M.H. Salem, Zahra Haghighijoo, Eric A. Austin, Febee R. Louka, Salah S. Massoud|2023|Polyhedron|243|116510|doi:10.1016/j.poly.2023.116510
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fwb8y
title: CCDC 2260590: Experimental Crystal Structure Determination

description: Related Article: Franz A. Mautner, Roland C. Fischer, Ana Torvisco, Saskia Speed, Ramon Vicente, Nahed M.H. Salem, Zahra Haghighijoo, Eric A. Austin, Febee R. Louka, Salah S. Massoud|2023|Polyhedron|243|116510|doi:10.1016/j.poly.2023.116510
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fwb9z
title: CCDC 2260591: Experimental Crystal Structure Determination

description: Related Article: Franz A. Mautner, Roland C. Fischer, Ana Torvisco, Saskia Speed, Ramon Vicente, Nahed M.H. Salem, Zahra Haghighijoo, Eric A. Austin, Febee R. Louka, Salah S. Massoud|2023|Polyhedron|243|116510|doi:10.1016/j.poly.2023.116510
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fwsjn
title: CCDC 2261032: Experimental Crystal Structure Determination

description: Related Article: Pengfei Fu, Tao Liu, Yang Shen, Xin Lei, Tianjie Xiao, Peng Chen, Dongsheng Qiu, Zhen Wang, Yandong Zhang|2023|J.Am.Chem.Soc.|145|18642|doi:10.1021/jacs.3c06442
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fwskp
title: CCDC 2261033: Experimental Crystal Structure Determination

description: Related Article: Pengfei Fu, Tao Liu, Yang Shen, Xin Lei, Tianjie Xiao, Peng Chen, Dongsheng Qiu, Zhen Wang, Yandong Zhang|2023|J.Am.Chem.Soc.|145|18642|doi:10.1021/jacs.3c06442
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fwslq
title: CCDC 2261034: Experimental Crystal Structure Determination

description: Related Article: Pengfei Fu, Tao Liu, Yang Shen, Xin Lei, Tianjie Xiao, Peng Chen, Dongsheng Qiu, Zhen Wang, Yandong Zhang|2023|J.Am.Chem.Soc.|145|18642|doi:10.1021/jacs.3c06442
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fwsmr
title: CCDC 2261035: Experimental Crystal Structure Determination

description: Related Article: Pengfei Fu, Tao Liu, Yang Shen, Xin Lei, Tianjie Xiao, Peng Chen, Dongsheng Qiu, Zhen Wang, Yandong Zhang|2023|J.Am.Chem.Soc.|145|18642|doi:10.1021/jacs.3c06442
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.23623014.v1
title: Annotated draft genome of Theodoxus fluviatilis

description: In total, 14 Gb of PacBio reads with a raw read length of about 5.7 Kb were generated on 4 SMRT cells. The de novo assembly consists of 35,695 contigs, with an N50 = 73.828 bp and moderate BUSCO scores (BUSCO C: 76.9 % [S: 74.3% , D: 2.6% ], F:10.6% , M:12.5%). <br> md5sums 2ef607cb523c3f625bb4d0e6744b7115 joingenespy_output.aa 6d28cae3bc8f92d4a5b9f2a106d71efe joingenespy_output.codingseq 2e06ee3b3a72881b5ba6e861a1f784c1 P_RNA_scaffold.Ppolish_6ab_onlyS7.fasta.gz
DOI: 10.25625/cdby3d/g1nzaf
title: Info_static_asymmetries.txt

description: :unav
DOI: 10.25625/cdby3d/iaqx4u
title: Ni_2022_07_21_M02_M47_600mW_I1I2_normed_to_Sum_KK_45.9_to_43.95_error_beta_t.tab

description: :unav
DOI: 10.25625/cdby3d/7ve03w
title: Ni_2022_07_22_M009_p_pol_600mW_at_sample_64eV_at_same_side_as66eV_asymmetry_curves.tab

description: :unav
DOI: 10.25625/cdby3d/vw3ahf
title: 1_exy_vs_energy_iter_comp_static_all_angles2angles45.9_to_44.0.png

description: :unav
DOI: 10.25625/cdby3d/xhglo0
title: 0_Fig1_Asyms_3angles_64eV.png

description: :unav
DOI: 10.5281/zenodo.10257184
title: Sample based prevalence data complementing the European Union One Health 2022 Zoonoses Report - Finland

description: This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.
description: EU; FI; csv; zoonoses_support:efsa.europa.eu
DOI: 10.5281/zenodo.10257183
title: Sample based prevalence data complementing the European Union One Health 2022 Zoonoses Report - Finland

description: This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.
description: EU; FI; csv; zoonoses_support:efsa.europa.eu
DOI: 10.25625/cdby3d/4m4skn
title: Ni_2022_07_22_M009_p_pol_600mW_at_sample_64eV_at_same_side_as66eV_I1_normed_to_sum.tab

description: :unav
DOI: 10.25625/cdby3d/ynt5hm
title: Ni_2022_07_21_M02_M47_600mW_I1I2_normed_to_Sum_KK_45.9_to_43.95_error_imag_t.tab

description: :unav
DOI: 10.25625/cdby3d/owmgxv
title: Ni23nm_Si3N4Si_2022_07_21_different_angles_energies_neg_asym.tab

description: :unav
DOI: 10.25625/cdby3d/8wxb7t
title: Ni_2022_07_21_M047_p_pol_600mW_at_sample_64eV_big_at_same_side_as66eV_time.tab

description: :unav
DOI: 10.25625/cdby3d/kdbyjh
title: Ni_2022_07_22_M009_p_pol_600mW_at_sample_64eV_at_same_side_as66eV_time.tab

description: :unav
DOI: 10.25625/cdby3d/gnjmkr
title: info.txt

description: :unav
DOI: 10.25625/cdby3d/xiiwul
title: 0_Fig16_fit_results_more_energies_new.png

description: :unav
DOI: 10.25625/cdby3d/gzci0g
title: Ni_2022_07_21_M047_p_pol_600mW_at_sample_64eV_at_same_side_as66eV_I2_normed_to_sum.tab

description: :unav
DOI: 10.25625/cdby3d/paqcnl
title: Ni_2022_07_23_M002_p_pol_600mW_at_sample_64eV_at_other_side_as66eV_I2_normed_to_sum.tab

description: :unav
DOI: 10.25625/cdby3d/4lds24
title: 0_Ni_static_all_angles_exy_TDDFT_shifted_by_66eV_new.png

description: :unav
DOI: 10.25625/cdby3d/5ksl0y
title: Ni_2022_07_21_M02_M47_600mW_I1I2_normed_to_Sum_KK_45.9_to_43.95_real.tab

description: :unav
DOI: 10.25625/cdby3d/xznk2d
title: Ni_2022_07_21_M02_M47_600mW_I1I2_normed_to_Sum_KK_45.9_to_43.95_imag.tab

description: :unav
DOI: 10.25625/cdby3d/4rlilp
title: Ni23nm_Si3N4Si_2022_07_21_different_angles_asym_neg_asym.tab

description: :unav
DOI: 10.25625/cdby3d/qm6vcs
title: 0_Fig7_exy_beta_change_with_errors.png

description: :unav
DOI: 10.25625/cdby3d/rukl09
title: Ni_2022_07_21_M02_M47_600mW_I1I2_normed_to_Sum_KK_45.9_to_43.95_delta_t.tab

description: :unav
DOI: 10.25625/cdby3d/tutc5q
title: 1_I1I2_fit_exp_NiO2nm_Ni22nm17__iter_5.png

description: :unav
DOI: 10.25625/cdby3d/aclafj
title: Middle_TDDFT_DOS_Ni_1_2eV_pump_47fs_Gauss_intensity_min_ni.tab

description: :unav
DOI: 10.25625/cdby3d/0qkfax
title: 1_beta_normed_tot0_iter_2.png

description: :unav
DOI: 10.5281/zenodo.10257161
title: Sample based prevalence data complementing the European Union One Health 2022 Zoonoses Report - Croatia

description: This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.
description: EU; csv; zoonoses_support:efsa.europa.eu
DOI: 10.5281/zenodo.10257162
title: Sample based prevalence data complementing the European Union One Health 2022 Zoonoses Report - Croatia

description: This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.
description: EU; csv; zoonoses_support:efsa.europa.eu
DOI: 10.25625/cdby3d/lbmdin
title: Ni_2022_07_21_M047_p_pol_600mW_at_sample_64eV_at_same_side_as66eV_I1_normed_to_sum.tab

description: :unav
DOI: 10.25625/cdby3d/zjc5hh
title: 1_I1_I2_time_step00angles45.9_to_44.0.png

description: :unav
DOI: 10.25625/cdby3d/er1lob
title: 1_beta_normed_tot0_iter_1.png

description: :unav
DOI: 10.25625/cdby3d/atis3r
title: Ni_2022_07_23_M002_p_pol_600mW_at_sample_64eV_at_other_side_as66eV_asymmetry_curves.tab

description: :unav
DOI: 10.25625/cdby3d/ikmich
title: 1_delta_normed_tot0_iter_5.png

description: :unav
DOI: 10.25625/cdby3d/aqczxe
title: Ni_2022_07_23_M002_p_pol_600mW_at_sample_64eV_at_other_side_as66eV_I1I2_time.tab

description: :unav
DOI: 10.25625/cdby3d/2dlvq5
title: TDDFT_Ni_1_2eV_pump_47fs_Gauss_intensity__exx_imag.tab

description: :unav
DOI: 10.25625/cdby3d/aes5xz
title: 1_delta_normed_tot0_iter_3.png

description: :unav
DOI: 10.25625/cdby3d/317wrk
title: Ni_2022_07_22_M009_p_pol_600mW_at_sample_64eV_at_same_side_as66eV_I1I2_time.tab

description: :unav