cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8065754
title: CL-MASR

description: <strong>CL-MASR Dataset</strong> This is the dataset used in the continual learning for multilingual ASR (CL-MASR) benchmark. It is composed of speech recordings from 20 languages selected from the Common Voice 13 dataset. For each language, it includes up to 10/1/1 hours for train/dev/test, respectively. The CL-MASR benchmark platform is available in the SpeechBrain toolkit (see recipes/CommonVoice):<br> https://github.com/speechbrain/speechbrain The original Common Voice 13 data are available at:<br> https://commonvoice.mozilla.org/en/datasets <strong>List of Languages</strong> - English (en)<br> - Chinese (zh-CN)<br> - German (de)<br> - Spanish (es)<br> - Russian (ru)<br> - French (fr)<br> - Portuguese (pt)<br> - Japanese (ja)<br> - Turkish (tr)<br> - Polish (pl)<br> - Kinyarwanda (rw)<br> - Esperanto (eo)<br> - Kabyle (kab)<br> - Luganda (lg)<br> - Meadow Mari (mhr)<br> - Central Kurdish (ckb)<br> - Abkhaz (ab)<br> - Kurmanji Kurdish (kmr)<br> - Frisian (fy-NL)<br> - Interlingua (ia)
description: {"references": ["M. Ravanelli, T. Parcollet, P. Plantinga, A. Rouhe, S. Cornell, L. Lugosch, C. Subakan, N. Dawalatabad, A. Heba, J. Zhong, J.-C. Chou, S.-L. Yeh, S.-W. Fu, C.-F. Liao, E. Rastorgueva, F. Grondin, W. Aris, H. Na, Y. Gao, R. D. Mori, and Y. Bengio. SpeechBrain: A general-purpose speech toolkit. arXiv preprint arXiv:2106.04624, 2021.", "R. Ardila, M. Branson, K. Davis, M. Kohler, J. Meyer, M. Henretty, R. Morais, L. Saunders, F. Tyers, and G. Weber. Common Voice: A massively-multilingual speech corpus. In Twelfth Language Resources and Evaluation Conference, pages 4218\u20134222, 2020."]}
DOI: 10.5281/zenodo.8065753
title: CL-MASR

description: <strong>CL-MASR Dataset</strong> This is the dataset used in the continual learning for multilingual ASR (CL-MASR) benchmark. It is composed of speech recordings from 20 languages selected from the Common Voice 13 dataset. For each language, it includes up to 10/1/1 hours for train/dev/test, respectively. The CL-MASR benchmark platform is available in the SpeechBrain toolkit (see recipes/CommonVoice):<br> https://github.com/speechbrain/speechbrain The original Common Voice 13 data are available at:<br> https://commonvoice.mozilla.org/en/datasets <strong>List of Languages</strong> - English (en)<br> - Chinese (zh-CN)<br> - German (de)<br> - Spanish (es)<br> - Russian (ru)<br> - French (fr)<br> - Portuguese (pt)<br> - Japanese (ja)<br> - Turkish (tr)<br> - Polish (pl)<br> - Kinyarwanda (rw)<br> - Esperanto (eo)<br> - Kabyle (kab)<br> - Luganda (lg)<br> - Meadow Mari (mhr)<br> - Central Kurdish (ckb)<br> - Abkhaz (ab)<br> - Kurmanji Kurdish (kmr)<br> - Frisian (fy-NL)<br> - Interlingua (ia)
description: {"references": ["M. Ravanelli, T. Parcollet, P. Plantinga, A. Rouhe, S. Cornell, L. Lugosch, C. Subakan, N. Dawalatabad, A. Heba, J. Zhong, J.-C. Chou, S.-L. Yeh, S.-W. Fu, C.-F. Liao, E. Rastorgueva, F. Grondin, W. Aris, H. Na, Y. Gao, R. D. Mori, and Y. Bengio. SpeechBrain: A general-purpose speech toolkit. arXiv preprint arXiv:2106.04624, 2021.", "R. Ardila, M. Branson, K. Davis, M. Kohler, J. Meyer, M. Henretty, R. Morais, L. Saunders, F. Tyers, and G. Weber. Common Voice: A massively-multilingual speech corpus. In Twelfth Language Resources and Evaluation Conference, pages 4218\u20134222, 2020."]}
DOI: 10.17028/rd.lboro.23592786
title: Supplemental information files for Fracture behaviour and toughening mechanisms of dry and wet collagen

description: Supplementary files for article Fracture behaviour and toughening mechanisms of dry and wet collagen The growing interest to the use of collagen films for biomedical applications motivates the analysis of their fracture behaviour in different environments. Studies revealed the decreased mechanical strength and stiffness as well as increased plasticity in water compared to collagen specimens tested in air. However, the fracture behaviour of pure collagen films in both air and water has not been reported so far. In this paper, the entire process of mode-I loading of single-edge notched tension (SENT) specimens was recorded and analysed. In case of in-air (dry) specimens, cracks propagated rapidly in a brittle fashion while large plastic deformations were observed in aqua prior to failure due to crack opening and a blunting mechanism in wet specimens. The fracture-toughness parameters for pure collagen in air and in aqua were estimated using linear-elastic (KI and GI) and elasto-plastic (JI) fracture-mechanics approaches, respectively, following the force-displacement response and deformational behaviour. GIC and JI were 1365 ± 112 J/m2 and 2500 ± 440 J/m2, respectively. Scanning electron microscopy was used to observe the structural changes linked to collagen fibrils in the crack-tip area and the fracture surface. For in-air specimens, the former mostly exhibited extrinsic toughening (usually at micro scale) acting behind the crack-tip, while in-aqua intrinsic toughening acting ahead of a crack tip was found. Fractography of in-air specimens showed no occurrence of voids while multiple micro-voids were found for in-aqua specimens. Statement of significance: The fracture toughness and crack propagation of both mineralised (bone, dentine) and non-mineralised (skin) tissues has been extensively investigated over the past decades. Though these tissues are rich in collagen, the fracture properties of pure collagen have not been quantified yet at macroscale. Considering the applications of collagen films in tissue regeneration, it is essential to perform investigations of their fracture behaviour in both dry and wet conditions. Determining the effect of environment on the fracture behaviour of collagen and understanding its toughening mechanism are essential for prevention of failures during application. Moreover, this would give an insight for fabrication of tougher collagen-based biomaterials for biomedical uses.
DOI: 10.17028/rd.lboro.23592786.v1
title: Supplemental information files for Fracture behaviour and toughening mechanisms of dry and wet collagen

description: Supplementary files for article Fracture behaviour and toughening mechanisms of dry and wet collagen The growing interest to the use of collagen films for biomedical applications motivates the analysis of their fracture behaviour in different environments. Studies revealed the decreased mechanical strength and stiffness as well as increased plasticity in water compared to collagen specimens tested in air. However, the fracture behaviour of pure collagen films in both air and water has not been reported so far. In this paper, the entire process of mode-I loading of single-edge notched tension (SENT) specimens was recorded and analysed. In case of in-air (dry) specimens, cracks propagated rapidly in a brittle fashion while large plastic deformations were observed in aqua prior to failure due to crack opening and a blunting mechanism in wet specimens. The fracture-toughness parameters for pure collagen in air and in aqua were estimated using linear-elastic (KI and GI) and elasto-plastic (JI) fracture-mechanics approaches, respectively, following the force-displacement response and deformational behaviour. GIC and JI were 1365 ± 112 J/m2 and 2500 ± 440 J/m2, respectively. Scanning electron microscopy was used to observe the structural changes linked to collagen fibrils in the crack-tip area and the fracture surface. For in-air specimens, the former mostly exhibited extrinsic toughening (usually at micro scale) acting behind the crack-tip, while in-aqua intrinsic toughening acting ahead of a crack tip was found. Fractography of in-air specimens showed no occurrence of voids while multiple micro-voids were found for in-aqua specimens. Statement of significance: The fracture toughness and crack propagation of both mineralised (bone, dentine) and non-mineralised (skin) tissues has been extensively investigated over the past decades. Though these tissues are rich in collagen, the fracture properties of pure collagen have not been quantified yet at macroscale. Considering the applications of collagen films in tissue regeneration, it is essential to perform investigations of their fracture behaviour in both dry and wet conditions. Determining the effect of environment on the fracture behaviour of collagen and understanding its toughening mechanism are essential for prevention of failures during application. Moreover, this would give an insight for fabrication of tougher collagen-based biomaterials for biomedical uses.
DOI: 10.5281/zenodo.7760561
title: Supporting data for "Reliable interpretability of biology-inspired deep neural networks"

description: <strong>Contents</strong> <em>data.tgz</em> contains all data necessary for reproducing the analysis in the manuscript. After cloning the GitHub repository, extract the contents of this file into folder <em>data</em>. The archive contains the following subfolders: <em>dtox</em><br> DTox results, one subfolder per seed <em>module_relevance.tsv</em>: contains node importance scores, with the following columns: (first, unnamed): compound identifier remaining columns: node identifiers (UniProt and Reactome IDs) <em>test_labels.csv</em>: predictions for the test set, with two columns: truth: true label (0 or 1) predicted: predicted label (decimal number between 0 and 1)<br> <em>mskimpact_[cancer type]_[experiment]</em><br> P-NET results using the MSK-IMPACT 2017 dataset, one subfolder per seed<br> [cancer type] is one of bc (breast cancer), cc (colorectal cancer), nsclc (non-small cell lung cancer), or pc (prostate cancer)<br> [experiment] is one of original (original setup) and shuffled (shuffled labels)<br> <em>pnet_[experiment]</em><br> P-NET results using the original (prostate cancer) dataset, one subfolder per seed<br> [experiment] is one of deterministic (deterministic input data), original (original setup), and shuffled (shuffled labels) <em>node_importance.csv</em>: contains node importance scores, with the following columns: (first, unnamed): node name coef: original node importance scores coef_graph: indegree plus outdegree of node coef_combined: adjusted node importance score (= coef / coef_graph if coef_graph &gt; mean(coef_graph) + 5 sd(coef_graph) in the respective layer) coef_combined_zscore: scaled coef_combined coef_combined2: z(z(coef_graph) - z(coef)) layer: layer of the node <em>predictions_test.csv</em>: predictions for the test set, with the following columns: (first, unnamed): sample name pred: predicted class (unfortunately, encoded by a double 1.0 or 0.0) pred_scores: probability of the predicted class y: true class (encoded as integer 1 or 0) <em>predictions_train.csv</em>: predictions for the training set (same columns as above) <em>link_weights_[layer].csv</em>: only in subfolder 234_20080808; matrices with edge weights <strong>Changelog</strong> <em>v1.1.0 – 2023-06-28</em> added DTox results added results of P-NET experiments with MSK-IMPACT 2017 dataset <em>v1.0.0 – 2023-03-22</em> initial release
DOI: 10.5281/zenodo.7760562
title: Supporting data for "Reliable interpretability of biology-inspired deep neural networks"

description: data.tgz contains all data necessary for reproducing the analysis in the manuscript.
DOI: 10.5281/zenodo.8091129
title: Supporting data for "Reliable interpretability of biology-inspired deep neural networks"

description: <strong>Contents</strong> <em>data.tgz</em> contains all data necessary for reproducing the analysis in the manuscript. After cloning the GitHub repository, extract the contents of this file into folder <em>data</em>. The archive contains the following subfolders: <em>dtox</em><br> DTox results, one subfolder per seed <em>module_relevance.tsv</em>: contains node importance scores, with the following columns: (first, unnamed): compound identifier remaining columns: node identifiers (UniProt and Reactome IDs) <em>test_labels.csv</em>: predictions for the test set, with two columns: truth: true label (0 or 1) predicted: predicted label (decimal number between 0 and 1)<br> <em>mskimpact_[cancer type]_[experiment]</em><br> P-NET results using the MSK-IMPACT 2017 dataset, one subfolder per seed<br> [cancer type] is one of bc (breast cancer), cc (colorectal cancer), nsclc (non-small cell lung cancer), or pc (prostate cancer)<br> [experiment] is one of original (original setup) and shuffled (shuffled labels)<br> <em>pnet_[experiment]</em><br> P-NET results using the original (prostate cancer) dataset, one subfolder per seed<br> [experiment] is one of deterministic (deterministic input data), original (original setup), and shuffled (shuffled labels) <em>node_importance.csv</em>: contains node importance scores, with the following columns: (first, unnamed): node name coef: original node importance scores coef_graph: indegree plus outdegree of node coef_combined: adjusted node importance score (= coef / coef_graph if coef_graph &gt; mean(coef_graph) + 5 sd(coef_graph) in the respective layer) coef_combined_zscore: scaled coef_combined coef_combined2: z(z(coef_graph) - z(coef)) layer: layer of the node <em>predictions_test.csv</em>: predictions for the test set, with the following columns: (first, unnamed): sample name pred: predicted class (unfortunately, encoded by a double 1.0 or 0.0) pred_scores: probability of the predicted class y: true class (encoded as integer 1 or 0) <em>predictions_train.csv</em>: predictions for the training set (same columns as above) <em>link_weights_[layer].csv</em>: only in subfolder 234_20080808; matrices with edge weights <strong>Changelog</strong> <em>v1.1.0 – 2023-06-28</em> added DTox results added results of P-NET experiments with MSK-IMPACT 2017 dataset <em>v1.0.0 – 2023-03-22</em> initial release
DOI: 10.5285/9c41e3aa67024bbdad796290a861e968
title: Large ensemble of global mean temperatures: 6-hourly HadAM4 model run data using the Climateprediction.net platform

description: Large ensembles of global temperature are provided for three climate scenarios: historical (2006-16), 1.5 C and 2.0 C above pre-industrial levels. Each scenario has 700 members (70 runs per year for 10-year periods) of 6-hourly mean temperatures at a resolution of 0.833 degrees x 0.556 degrees (longitude x latitude). The data was generated using the climateprediction.net (CPDN) climate simulation environment, to run the Met Office HadAM4 Atmosphere-only General Circulation Model (AGCM) from the UK Met Office Hadley Centre. Biases in simulated temperature were identified and corrected using quantile mapping with reference temperature data from ERA5 reanalysis. Data were generated using the Met Office HadAM4 model at 6-hourly temporal resolution and 0.833 degrees x 0.556 degrees (longitude x latitude) over global domain. The data from each scenario is divided into 4 batches. Historic scenario (2006-2016): December-March data in Batch 889, April-May data in Batch 920, June-September data in Batch 901, October-November data in Batch 923. 1.5C scenario: December-March data in Batch 891, April-May data in Batch 921, June-September data in Batch 902, October-November data in Batch 924. 2.0C scenario: December-March data in Batch 895, April-May data in Batch 922, June-September data in Batch 903, October-November data in Batch 925.
DOI: 10.6084/m9.figshare.23574249
title: 2023_Chigede_Binary data for diversity analysis.xlsx

description: Antimicrobial drug resistance from livestock products is a growing hazard for humans worldwide. Microbes are a rich source of structurally distinct bioactive compounds designed to protect the microbes and can offset antimicrobial resistance challenge. In that vein, microbes were screened for biosurfactant release from the broiler gastrointestinal tract, isolated and identified using 16S rRNA technique, annotated by similarity using BLASTn analysis against the NCBI nucleotide database. Phylogenetic analysis was performed on the BLASTn outcome to have an appreciation of the evolutionary genetic relationships.
DOI: 10.6084/m9.figshare.23574249.v1
title: 2023_Chigede_Binary data for diversity analysis.xlsx

description: Antimicrobial drug resistance from livestock products is a growing hazard for humans worldwide. Microbes are a rich source of structurally distinct bioactive compounds designed to protect the microbes and can offset antimicrobial resistance challenge. In that vein, microbes were screened for biosurfactant release from the broiler gastrointestinal tract, isolated and identified using 16S rRNA technique, annotated by similarity using BLASTn analysis against the NCBI nucleotide database. Phylogenetic analysis was performed on the BLASTn outcome to have an appreciation of the evolutionary genetic relationships.
DOI: 10.5281/zenodo.8059344
title: iSkylims demo dataset

description: iSkyLIMS is born with the aim of helping with the wet laboratory tasks, and implementing a workflow that guides genomics labs on their activities from library preparation to data production, reducing potential errors associated to high throughput technology, and facilitating the quality control of the sequencing. Also, iSkyLIMS connects the wet lab with dry lab facilitating data analysis by bioinformaticians. This dataset provides three run folders for NextSeq, NovaSeq y MiSeq Illumina machines with stats and log files that iskylims processes and uses for visualization.
DOI: 10.5281/zenodo.8059345
title: iSkylims demo dataset

description: iSkyLIMS is born with the aim of helping with the wet laboratory tasks, and implementing a workflow that guides genomics labs on their activities from library preparation to data production, reducing potential errors associated to high throughput technology, and facilitating the quality control of the sequencing. Also, iSkyLIMS connects the wet lab with dry lab facilitating data analysis by bioinformaticians. This dataset provides three run folders for NextSeq, NovaSeq y MiSeq Illumina machines with stats and log files that iskylims processes and uses for visualization.
DOI: 10.5281/zenodo.8091169
title: iSkylims demo dataset

description: iSkyLIMS is born with the aim of helping with the wet laboratory tasks, and implementing a workflow that guides genomics labs on their activities from library preparation to data production, reducing potential errors associated to high throughput technology, and facilitating the quality control of the sequencing. Also, iSkyLIMS connects the wet lab with dry lab facilitating data analysis by bioinformaticians. This dataset provides three run folders for NextSeq, NovaSeq y MiSeq Illumina machines with stats and log files that iskylims processes and uses for visualization.
DOI: 10.18112/openneuro.ds004505.v1.0.4
title: Real World Table Tennis
DOI: 10.5281/zenodo.8091254
title: chlorocystis_dangeardii_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.8091253
title: chlorocystis_dangeardii_spades_01.fasta.gz

description: .
DOI: 10.25835/0006102
title: KOeSL-Ergebnisse-Geodaten

description: This dataset contains results of a national mapping of cultural ecosystem services in Germany. They are an outcome of the research and development project ‘‘Valuing Cultural Ecosystem Services in Germany” and “Further developing nationwide indicators for cultural ecosystem services (KÖSL 2.0)”, supported by the Federal Agency for Nature Conservation (BfN) with funding from the Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety (BMUB), Grant ID: 3513 83 0300 and 3518 81 040B. __Dokumentation:__ * __Johannes Hermes, Christian Albert, Dirk Schmücker, Birte Bredemeier, Jan Barkmann, Christina von Haaren (2023):__ Erfassung und Bewertung kultureller Ökosystemleistungen in Deutschland. Die Qualität der Landschaft für Freiezeit- und Wochenenderholung in Deutschland: Potenzial, Dargebot, Präferenzen, Nutzung. BfN-Schrift 659, BfN - Bonn. https://doi.org/10.19217/skr659 * für Version 1 (2018) __Hermes, Johannes; Albert, Christian & Haaren, Christina v. (2018):__ Assessing the aesthetic quality of landscapes in Germany. In: Ecosystem Services 31, 296-307. https://doi.org/10.1016/j.ecoser.2018.02.015.
DOI: 10.17028/rd.lboro.23593014
title: Supplementary information files for Planet Patrolling: A citizen science brand audit of anthropogenic litter in the context of national legislation and international policy

description: Supplementary files for article Planet Patrolling: A citizen science brand audit of anthropogenic litter in the context of national legislation and international policy Anthropogenic Litter (AL) is ubiquitous in distribution and diverse in type and impact. Citizen science AL clean-ups engage citizens with the environment and have the potential to generate data that can inform policy. Here we present a detailed citizen science survey of AL across freshwater, terrestrial, and coastal environments of the United Kingdom (UK), coordinated by the not-for-profit Planet Patrol throughout 2020. Key materials, industries, brands, and parent companies associated with AL are identified. Plastic dominated AL (63%), followed by metal (14%), and composite materials (12%). The majority of AL (56%) had been used as beverage containers and non-beverage packaging, and 38.8% of AL was branded. Of the branded AL, 26% was associated with The Coca-Cola Company, Anheuser-Busch InBev, and PepsiCo. These three companies were associated with significantly more branded litter than any other. We place these data in the context of upcoming UK legislation and the Environmental Social Governance (ESG) statements of the companies associated with the majority of the recorded litter. Knowledge gaps and recommendations for AL surveying are made, and the focus of corporate and government actions are discussed.
DOI: 10.17028/rd.lboro.23593014.v1
title: Supplementary information files for Planet Patrolling: A citizen science brand audit of anthropogenic litter in the context of national legislation and international policy

description: Supplementary files for article Planet Patrolling: A citizen science brand audit of anthropogenic litter in the context of national legislation and international policy Anthropogenic Litter (AL) is ubiquitous in distribution and diverse in type and impact. Citizen science AL clean-ups engage citizens with the environment and have the potential to generate data that can inform policy. Here we present a detailed citizen science survey of AL across freshwater, terrestrial, and coastal environments of the United Kingdom (UK), coordinated by the not-for-profit Planet Patrol throughout 2020. Key materials, industries, brands, and parent companies associated with AL are identified. Plastic dominated AL (63%), followed by metal (14%), and composite materials (12%). The majority of AL (56%) had been used as beverage containers and non-beverage packaging, and 38.8% of AL was branded. Of the branded AL, 26% was associated with The Coca-Cola Company, Anheuser-Busch InBev, and PepsiCo. These three companies were associated with significantly more branded litter than any other. We place these data in the context of upcoming UK legislation and the Environmental Social Governance (ESG) statements of the companies associated with the majority of the recorded litter. Knowledge gaps and recommendations for AL surveying are made, and the focus of corporate and government actions are discussed.
DOI: 10.20387/bonares-8a20-rx11
title: Microbial biomass carbon in soil samples taken from cover crop root channels and bulk soil

description: The microbial biomass carbon of cover crop root channels and bulk soil was measured on samples taken 14 weeks after the cover crops were terminated with an herbicide prior to maize sowing. 

Research domain: Soil Sciences

Research question: How much microbial biomass carbon is in the root channels of cover crops, 14 weeks after the cover crops were terminated with a herbicide.