cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc24lky8
title: CCDC 1984060: Experimental Crystal Structure Determination

description: Related Article: Biming Mao, Honglei Liu, Zhengyang Yan, Yi Xu, Jiaqing Xu, Wei Wang, Yongjun Wu, Hongchao Guo|2020|Angew.Chem.,Int.Ed.|59|11316|doi:10.1002/anie.202002765
DOI: 10.5517/ccdc.csd.cc24ll0c
title: CCDC 1984062: Experimental Crystal Structure Determination

description: Related Article: Biming Mao, Honglei Liu, Zhengyang Yan, Yi Xu, Jiaqing Xu, Wei Wang, Yongjun Wu, Hongchao Guo|2020|Angew.Chem.,Int.Ed.|59|11316|doi:10.1002/anie.202002765
DOI: 10.5517/ccdc.csd.cc24ll2f
title: CCDC 1984064: Experimental Crystal Structure Determination

description: Related Article: Biming Mao, Honglei Liu, Zhengyang Yan, Yi Xu, Jiaqing Xu, Wei Wang, Yongjun Wu, Hongchao Guo|2020|Angew.Chem.,Int.Ed.|59|11316|doi:10.1002/anie.202002765
DOI: 10.6084/m9.figshare.14914605
title: Chloroplast short-read alignment of 50 broomcorn millet accessions

description: Chloroplast short-read alignment of 50 broomcorn millet accession against the reference sequence NC_029732.1 (NC_029732.1 is identical with KU343177.1)
DOI: 10.6084/m9.figshare.14922807.v1
title: HIST random test bw

description: testing histone
DOI: 10.6084/m9.figshare.14922807
title: HIST random test bw

description: testing histone
DOI: 10.5281/zenodo.5078030
title: Aquatic Mollusca (Gastropoda and Bivalvia) from the Malaysian Borneo: The inconsistency records

description: This data set contains the available taxonomic inconsistencies of aquatic Mollusca (Gastropoda and Bivalvia) reported from Malaysian Borneo (East Malaysia) comprised of two provinces namely Sarawak and Sabah along with the federal territory of Labuan.
description: {"references": ["Al-Asif et al. (2020). Zooplankton species composition and diversity in the seagrass habitat of Lawas, Sarawak, Malaysia"]}
DOI: 10.5281/zenodo.5040076
title: Figure data for the manuscript "SI-traceable frequency dissemination at 1572.06 nm in a stabilized fiber network with ring topology"

description: This file contains the data shown in Fig. 1, Fig 3, Fig. 4, Fig. 5 and Fig. 6 of the manuscript "SI-traceable frequency dissemination at 1572.06 nm in a stabilized fiber network with ring topology". Additional information on the data and processing procedure are available from the author upon reasonable request.
DOI: 10.5281/zenodo.5040075
title: Figure data for the manuscript "SI-traceable frequency dissemination at 1572.06 nm in a stabilized fiber network with ring topology"

description: This file contains the data shown in Fig. 1, Fig 3, Fig. 4, Fig. 5 and Fig. 6 of the manuscript "SI-traceable frequency dissemination at 1572.06 nm in a stabilized fiber network with ring topology". Additional information on the data and processing procedure are available from the author upon reasonable request.
DOI: 10.6084/m9.figshare.14922768.v1
title: Nominal categorisation performed for the analysed dimensions

description: Nominal categorisation performed for the analysed dimensions
DOI: 10.6084/m9.figshare.14922768
title: Nominal categorisation performed for the analysed dimensions

description: Nominal categorisation performed for the analysed dimensions
DOI: 10.5281/zenodo.5075448
title: Semantic Sea-Ice Classification for Belgica Bank in Greenland

description: Each Sentinel-1 image is tiled into patches of 256x256 pixels. The size of the images is different and we reduced to the smallest one. In total for each image are 6,400 patches [1-2, 4]. See the excel file for the 24 Sentinel-1 ids. The semantic classes are: <em>Black border</em> <em>Old ice</em> <em>First-Year ice </em> <em>Glaciers </em> <em>Icebergs</em> <em>Mountains </em> <em>Young ice</em> <em>Water group</em> The last class combines the <em>Floating ice, Water body, Water ice current and melted snow</em> defined in [3] because they have very similar physical properties. <strong>References:</strong> 1. C.O. Dumitru, G. Schwarz, C. Karmakar, and M. Datcu, “Machine Learning-Based Paradigm for Boosting the Semantic Annotation of EO Images”, IGARSS, Belgium, July 2021, pp. 1-4. 2. C. Karmakar, C.O. Dumitru, and M. Datcu, “Explainable AI for SAR Image Time Series: Knowledge Extraction for Polar Areas”, MDPI Remote Sensing Journal, 2021, pp. 1-21 (under review). 3. C.O. Dumitru, V. Andrei, G. Schwarz, and M. Datcu, “Machine Learning for Sea Ice Monitoring from Satellites”, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W16, pp. 83-89, 2019. 4. C. Karmakar, C.O. Dumitru, G. Schwarz, and M. Datcu, “<em>Feature-Free Explainable Data Mining in SAR Images Using Latent Dirichlet Allocation</em>”, IEEE JSTARS, vol. 14, pp. 676-689, 2021.
DOI: 10.5281/zenodo.5075447
title: Semantic Sea-Ice Classification for Belgica Bank in Greenland

description: Each Sentinel-1 image is tiled into patches of 256x256 pixels. The size of the images is different and we reduced to the smallest one. In total for each image are 6,400 patches [1-2, 4]. See the excel file for the 24 Sentinel-1 ids. The semantic classes are: <em>Black border</em> <em>Old ice</em> <em>First-Year ice </em> <em>Glaciers </em> <em>Icebergs</em> <em>Mountains </em> <em>Young ice</em> <em>Water group</em> The last class combines the <em>Floating ice, Water body, Water ice current and melted snow</em> defined in [3] because they have very similar physical properties. <strong>References:</strong> 1. C.O. Dumitru, G. Schwarz, C. Karmakar, and M. Datcu, “Machine Learning-Based Paradigm for Boosting the Semantic Annotation of EO Images”, IGARSS, Belgium, July 2021, pp. 1-4. 2. C. Karmakar, C.O. Dumitru, and M. Datcu, “Explainable AI for SAR Image Time Series: Knowledge Extraction for Polar Areas”, MDPI Remote Sensing Journal, 2021, pp. 1-21 (under review). 3. C.O. Dumitru, V. Andrei, G. Schwarz, and M. Datcu, “Machine Learning for Sea Ice Monitoring from Satellites”, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W16, pp. 83-89, 2019. 4. C. Karmakar, C.O. Dumitru, G. Schwarz, and M. Datcu, “<em>Feature-Free Explainable Data Mining in SAR Images Using Latent Dirichlet Allocation</em>”, IEEE JSTARS, vol. 14, pp. 676-689, 2021.
DOI: 10.5281/zenodo.5046268
title: Characterizing ferromagnetic domains in ring structures using in-situ magnetic Fresnel imaging

description: This deposit contains supplementary datasets and data processing scripts used in a Specialization Project by Rajith Aravinth at the Norwegian University of Science and Technology (NTNU). <strong>Dataset</strong>: 2021_03_26_FA721_A6.5_in_situ_stack.hspy Sample FA721, window W1, ring A6.5um, objective lens 0768.<br> Tilting range [-2.0, 2.0] deg. in X and [-2.0, 2.0] deg. in Y, step size 1.0 deg. <strong>Python files:</strong><br> processing.py utils.py p001_make_hyperspy.py Running processing.py produces the domains and areas as numpy files, that can be used for visualisation and quantifications.<br> Looping through the whole dataset takes quite some time, hence the results are also to be found in the .npy files. # Numpy files<br> areas.npy domains.npy <strong>Notebook:</strong> Jupyter_notebook.ipynb<br> More detail overlook of the algorithm, with visualizations and result
DOI: 10.5281/zenodo.5031898
title: Characterizing ferromagnetic domains in ring structures using in-situ magnetic Fresnel imaging

description: This deposit contains supplementary datasets and data processing scripts used in a Specialization Project by Rajith Aravinth at the Norwegian University of Science and Technology (NTNU). <strong>Dataset</strong>: 2021_03_26_FA721_A6.5_in_situ_stack.hspy Sample FA721, window W1, ring A6.5um, objective lens 0768.<br> Tilting range [-2.0, 2.0] deg. in X and [-2.0, 2.0] deg. in Y, step size 1.0 deg. <strong>Python files:</strong><br> processing.py utils.py p001_make_hyperspy.py Running processing.py produces the domains and areas as numpy files, that can be used for visualisation and quantifications.<br> Looping through the whole dataset takes quite some time, hence the results are also to be found in the .npy files. # Numpy files<br> areas.npy domains.npy <strong>Notebook:</strong> Jupyter_notebook.ipynb<br> More detail overlook of the algorithm, with visualizations and result
DOI: 10.5281/zenodo.5078039
title: The Relation Between Crystal Structure and the Occurrence of Quantum-Rotor Induced Polarization

description: NMR raw data as TopSpin (Bruker Corporation) files and XRD data as Origin files for the article titled "The Relation Between Crystal Structure and the Occurrence of Quantum-Rotor Induced Polarization".
DOI: 10.6084/m9.figshare.14554836
title: TXWRI-dataset

description: Results of white beam X-ray interference measurements on semiconductor wafers. <br>
DOI: 10.6084/m9.figshare.14554836.v1
title: TXWRI-dataset

description: Results of white beam X-ray interference measurements on semiconductor wafers. <br>
DOI: 10.6084/m9.figshare.14176664
title: The Code and Data

description: This zipped folder contains the code and data used to generate the figures in our manuscript "Quantitative analysis of non-equilibrium systems from short-time experimental data"<br>
DOI: 10.6084/m9.figshare.14176664.v1
title: The Code and Data

description: This zipped folder contains the code and data used to generate the figures in our manuscript "Quantitative analysis of non-equilibrium systems from short-time experimental data"<br>
DOI: 10.5281/zenodo.5078055
title: In-house sequence database of S.pn recombinant protein vaccine target-related genes

description: In-house sequence database of S.pn recombinant protein vaccine target-related genes.
DOI: 10.5281/zenodo.5078054
title: In-house sequence database of S.pn recombinant protein vaccine target-related genes

description: In-house sequence database of S.pn recombinant protein vaccine target-related genes.
DOI: 10.5281/zenodo.4088446
title: Manual Topic Annotation of German Novels and Parlament Protocols by multiple Annotators

description: This dataset was created in the research project hermA and contains topic annotations for 960 sentences, half of which were taken from transcripts of the German Bundestag and half from recent German novels. For each sentence, 30 different annotators evaluated whether illness is addressed, how central the topic is, if so, and how certain they are in the annotation. The dataset contains the following columns: <strong>item_id </strong>(for each sentence) <strong>worker_id </strong>(for each annotator) <strong>worker_group </strong>(either "crowdworker" or "student") <strong>corpus </strong>(either "protocol_corpus"=transcript corpus or "novel_corpus"=fiction corpus) <strong>text_source</strong> <strong>previous_sentences </strong>(in the text_source) <strong>target_sentence</strong> <strong>following_sentences </strong>(in the text_source) <strong>semantic_field_token </strong>(if existing) <strong>semantic_field_status </strong>(either "True" or "False") <strong>1_wird_im_fett_gedruckten_satz_krankheit_thematisiert </strong>(topic annotations: either "ja" or "nein") <strong>1b_wie_zentral_ist_das_thema_krankheit_im_fettgedruckten_satz </strong>(topic centrality: "NaN", "krankheit_kommt_eher_am_rande_des_satzes_vor" or "krankheit_ist_das_zentrale_thema_des_satzes") <strong>2_wie_sicher_bist_du_dir_bei_der_antwort_zu_frage_1_ </strong>(annotation certainty: "sehr_sicher", "eher_sicher", "eher_unsicher" or "sehr_unsicher") We use the annotations to model ambiguity in: Andresen, Melanie; Vauth, Michael & Zinsmeister, Heike. 2020. Modeling Ambiguity with Many Annotators and Self-Assessments of Annotator Certainty. <em>Proceedings of 14th Linguistic Annotation Workshop</em>.
description: {"references": ["Andresen, Melanie; Vauth, Michael & Zinsmeister, Heike. 2020. Modeling Ambiguity with Many Annotators and Self-Assessments of Annotator Certainty. Proceedings of 14th Linguistic Annotation Workshop. 2020."]}
DOI: 10.5281/zenodo.4088445
title: Manual Topic Annotation of German Novels and Parlament Protocols by multiple Annotators

description: This dataset was created in the research project hermA and contains topic annotations for 960 sentences, half of which were taken from transcripts of the German Bundestag and half from recent German novels. For each sentence, 30 different annotators evaluated whether illness is addressed, how central the topic is, if so, and how certain they are in the annotation. The dataset contains the following columns: <strong>item_id </strong>(for each sentence) <strong>worker_id </strong>(for each annotator) <strong>worker_group </strong>(either "crowdworker" or "student") <strong>corpus </strong>(either "protocol_corpus"=transcript corpus or "novel_corpus"=fiction corpus) <strong>text_source</strong> <strong>previous_sentences </strong>(in the text_source) <strong>target_sentence</strong> <strong>following_sentences </strong>(in the text_source) <strong>semantic_field_token </strong>(if existing) <strong>semantic_field_status </strong>(either "True" or "False") <strong>1_wird_im_fett_gedruckten_satz_krankheit_thematisiert </strong>(topic annotations: either "ja" or "nein") <strong>1b_wie_zentral_ist_das_thema_krankheit_im_fettgedruckten_satz </strong>(topic centrality: "NaN", "krankheit_kommt_eher_am_rande_des_satzes_vor" or "krankheit_ist_das_zentrale_thema_des_satzes") <strong>2_wie_sicher_bist_du_dir_bei_der_antwort_zu_frage_1_ </strong>(annotation certainty: "sehr_sicher", "eher_sicher", "eher_unsicher" or "sehr_unsicher") We use the annotations to model ambiguity in: Andresen, Melanie; Vauth, Michael & Zinsmeister, Heike. 2020. Modeling Ambiguity with Many Annotators and Self-Assessments of Annotator Certainty. <em>Proceedings of 14th Linguistic Annotation Workshop</em>.
description: {"references": ["Andresen, Melanie; Vauth, Michael & Zinsmeister, Heike. 2020. Modeling Ambiguity with Many Annotators and Self-Assessments of Annotator Certainty. Proceedings of 14th Linguistic Annotation Workshop. 2020."]}
DOI: 10.5281/zenodo.5078107
title: Social Accounting Matrix for Lithuania, 2017 (with the shift to bioplastics in packing industry)

description: The dataset is based on doi: 10.5281/zenodo.5077893 but includes bioplastics as the main input for the production of plastic sacks and bags.
description: This project has received funding from the Research Council of Lithuania (LMTLT), agreement No S-MIP-20-53.
DOI: 10.5281/zenodo.5078106
title: Social Accounting Matrix for Lithuania, 2017 (with the shift to bioplastics in packing industry)

description: The dataset is based on doi: 10.5281/zenodo.5077893 but includes bioplastics as the main input for the production of plastic sacks and bags.
description: This project has received funding from the Research Council of Lithuania (LMTLT), agreement No S-MIP-20-53.
DOI: 10.5281/zenodo.5078108
title: Aquatic Mollusca (Gastropoda and Bivalvia) from the Malaysian Borneo: A bibliographic records

description: This data set contains the available aquatic Mollusca reported from Malaysian Borneo (East Malaysia) comprised of two provinces namely Sarawak and Sabah along with the federal territory of Labuan.
description: {"references": ["Al-Asif et al. (2020). Zooplankton species composition and diversity in the seagrass habitat of Lawas, Sarawak, Malaysia"]}
DOI: 10.6084/m9.figshare.14223030.v1
title: The Aegean Archipelago as cradle: divergence of the glaphyrid genus <i>Pygopleurus</i> and phylogeography of <i>P. foina</i>

description: The genus <i>Pygopleurus</i> includes some of the most important beetle pollinators of the Eastern Mediterranean islands, showing a particular affinity to red-guild flowers. Focussing on the Aegean Archipelago, 75 individuals belonging to five <i>Pygopleurus</i> species were sampled and screened for two mtDNA loci (COI and 16S). The phylogenetic analysis implemented both by Bayesian and Maximum Likelihood approaches, showed a deep and robust structure for three species, mostly reflecting the palaeogeographic history of the islands. Interspecific divergence among them appears to have emerged at the beginning of the Messinian Salinity Crisis (5.99-5.33 Mya), with differentiation dating back to the eustatic sea level changes of the Pleistocene (2.56-0.0117 Mya). For <i>P. foina</i>, the most abundant species in the Aegean Archipelago, a finer sampling (121 individuals from 8 islands) and analysis of COI locus showed a distinct separation between northern and southern islands of East Aegean that disconnected non-simultaneously from the continent during the Pleistocene. A strong geographic structure was also evident at smaller taxonomic scales, with island populations exhibiting unique haplotypes. Thus, we concluded that the major palaeogeographic events of the region are reflected in the observed patterns of inter- and intraspecific divergence among and within <i>Pygopleurus</i> species in the Aegean Archipelago, something that is facilitated by this genus’ low dispersal ability.
DOI: 10.6084/m9.figshare.14223030
title: The Aegean Archipelago as cradle: divergence of the glaphyrid genus <i>Pygopleurus</i> and phylogeography of <i>P. foina</i>

description: The genus <i>Pygopleurus</i> includes some of the most important beetle pollinators of the Eastern Mediterranean islands, showing a particular affinity to red-guild flowers. Focussing on the Aegean Archipelago, 75 individuals belonging to five <i>Pygopleurus</i> species were sampled and screened for two mtDNA loci (COI and 16S). The phylogenetic analysis implemented both by Bayesian and Maximum Likelihood approaches, showed a deep and robust structure for three species, mostly reflecting the palaeogeographic history of the islands. Interspecific divergence among them appears to have emerged at the beginning of the Messinian Salinity Crisis (5.99-5.33 Mya), with differentiation dating back to the eustatic sea level changes of the Pleistocene (2.56-0.0117 Mya). For <i>P. foina</i>, the most abundant species in the Aegean Archipelago, a finer sampling (121 individuals from 8 islands) and analysis of COI locus showed a distinct separation between northern and southern islands of East Aegean that disconnected non-simultaneously from the continent during the Pleistocene. A strong geographic structure was also evident at smaller taxonomic scales, with island populations exhibiting unique haplotypes. Thus, we concluded that the major palaeogeographic events of the region are reflected in the observed patterns of inter- and intraspecific divergence among and within <i>Pygopleurus</i> species in the Aegean Archipelago, something that is facilitated by this genus’ low dispersal ability.
DOI: 10.6084/m9.figshare.14922771.v1
title: Supplementary Material for: PD-L1 Testing in Cytological Non-Small Cell Lung Cancer Specimens: A Comparison with Biopsies and Review of the Literature

description: <b><i>Introduction:</i></b> Programmed death-ligand 1 (PD-L1) expression is used for treatment prediction in non-small cell lung cancer (NSCLC). While cytology may be the only available material in the routine clinical setting, testing in clinical trials has mainly been based on biopsies. <b><i>Methods:</i></b> We included 2 retrospective cohorts of paired, concurrently sampled, cytological specimens and biopsies. Also, the literature on PD-L1 in paired cytological/histological samples was reviewed. Focus was on the cutoff levels ≥1 and ≥50% positive tumor cells. <b><i>Results:</i></b> Using a 3-tier scale, PD-L1 was concordant in 40/47 (85%) and 66/97 (68%) of the paired NSCLC cases in the 2 cohorts, with kappa 0.77 and 0.49, respectively. In the former cohort, all discordant cases had lower score in cytology. In both cohorts, concordance was lower in samples from different sites (e.g., biopsy from primary tumor and cytology from pleural effusion). Based on 25 published studies including about 1,700 paired cytology/histology cases, the median (range) concordance was 81–85% (62–100%) at cutoff 1% for a positive PD-L1 staining and 89% (67–100%) at cutoff 50%. <b><i>Conclusions:</i></b> The overall concordance of PD-L1 between cytology and biopsies is rather good but with significant variation between laboratories, which calls for local quality assurance.
DOI: 10.6084/m9.figshare.14922771
title: Supplementary Material for: PD-L1 Testing in Cytological Non-Small Cell Lung Cancer Specimens: A Comparison with Biopsies and Review of the Literature

description: <b><i>Introduction:</i></b> Programmed death-ligand 1 (PD-L1) expression is used for treatment prediction in non-small cell lung cancer (NSCLC). While cytology may be the only available material in the routine clinical setting, testing in clinical trials has mainly been based on biopsies. <b><i>Methods:</i></b> We included 2 retrospective cohorts of paired, concurrently sampled, cytological specimens and biopsies. Also, the literature on PD-L1 in paired cytological/histological samples was reviewed. Focus was on the cutoff levels ≥1 and ≥50% positive tumor cells. <b><i>Results:</i></b> Using a 3-tier scale, PD-L1 was concordant in 40/47 (85%) and 66/97 (68%) of the paired NSCLC cases in the 2 cohorts, with kappa 0.77 and 0.49, respectively. In the former cohort, all discordant cases had lower score in cytology. In both cohorts, concordance was lower in samples from different sites (e.g., biopsy from primary tumor and cytology from pleural effusion). Based on 25 published studies including about 1,700 paired cytology/histology cases, the median (range) concordance was 81–85% (62–100%) at cutoff 1% for a positive PD-L1 staining and 89% (67–100%) at cutoff 50%. <b><i>Conclusions:</i></b> The overall concordance of PD-L1 between cytology and biopsies is rather good but with significant variation between laboratories, which calls for local quality assurance.
DOI: 10.21227/ghrq-xh55
title: Neural Audio Fingerprint Dataset (mini)

description: Most of existing audio fingerprinting systems have limitations to be used for high-specific audio retrieval at scale. In this work, we generate a low-dimensional representation from a short unit segment of audio, and couple this fingerprint with a fast maximum inner-product search. To this end, we present a contrastive learning framework that derives from the segment-level search objective. Each update in training uses a batch consisting of a set of pseudo labels, randomly selected original samples, and their augmented replicas. These replicas can simulate the degrading effects on original audio signals by applying small time offsets and various types of distortions, such as background noise and room/microphone impulse responses. In the segment-level search task, where the conventional audio fingerprinting systems used to fail, our system using 10x smaller storage has shown promising results. Our code and dataset are available at https://mimbres.github.io/neural-audio-fp/.
DOI: 10.25838/d5p-20
title: TopProperty dataset

description: Transmembrane proteins (TMPs) are critical components of cellular life. However, due to experimental challenges, the number of experimentally resolved TMP structures is severely underrepresented in databases compared to their cellular abundance. Prediction of (per-residue) features such as transmembrane topology, membrane exposure, secondary structure, and solvent accessibility can be a useful starting point for experimental design or protein structure prediction, but often requires different computational tools for different features or types of proteins. We present TopProperty, a meta-predictor that predicts all of these features for TMPs or globular proteins. TopProperty predictions are robust, especially for proteins with few sequence homologs, and significantly better than the evaluated state-of-the-art primary predictors on all quality metrics. TopProperty eliminates the need for protein type- or feature-tailored tools, specifically for TMPs. TopProperty is freely available as web server and standalone at https://cpclab.uni-duesseldorf.de/topsuite/.
DOI: 10.17894/ucph.c589422c-64fd-4585-af31-4571497bcbe5
title: FOWD-CDIP v0.5.2