cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.20196941
title: Raw dataset

description: Raw dataset 1.Datasets data link (GSE6919/ GSE46602/GSE70768)： https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi <br> 2. Datasets data link (TCGA-PRAD)： https://portal.gdc.cancer.gov/exploration?filters={"content":[{"content":{"field":"cases.primary_site","value":["pancreas"]},"op":"in"},{"content":{"field":"cases.diagnoses.tissue_or_organ_of_origin","value":["body of pancreas","head of pancreas","islets of langerhans","other specified parts of pancreas","overlapping lesion of pancreas","pancreas, nos","pancreatic duct","tail of pancreas"]},"op":"in"}],"op":"and"} <br> <br> <br> <br> <br> <br> <br> <br> <br> <br>
DOI: 10.5281/zenodo.6782179
title: The role of energy storage technologies in the context of the Swiss energy transition (SwissStore)

description: The role of energy storage is subject to intense debates at the national and international level, reflecting a lack of consensus about the techno-economic potential and respective merits of the various energy storage technologies. This is largely due to the wide spectrum of possible applications, evolving technology and unclear market conditions. In the context of the SwissStore project we studied energy storage in different contexts and from different perspectives, i.e.<br> • in households, in combination with photovoltaic panels (coupled with batteries and thermal storage), taking the perspective of the private investor<br> • in all buildings (all sectors, also with PV), with a focus on increased short-term flexibility, thereby also considering other strategies next to storage (e.g. demand-side response) and taking the perspective of the social planner.<br> In both cases, optimisation frameworks were developed for Switzerland and different scenarios were studied for the next decades.
description: Project Funded by the Swiss National Science Foundation (SNF), grant number 205121 172703/1
DOI: 10.5281/zenodo.6772168
title: Dataset_publication

description: Dataset analyzed for publication
DOI: 10.5281/zenodo.6782178
title: The role of energy storage technologies in the context of the Swiss energy transition (SwissStore)

description: The role of energy storage is subject to intense debates at the national and international level, reflecting a lack of consensus about the techno-economic potential and respective merits of the various energy storage technologies. This is largely due to the wide spectrum of possible applications, evolving technology and unclear market conditions. In the context of the SwissStore project we studied energy storage in different contexts and from different perspectives, i.e.<br> • in households, in combination with photovoltaic panels (coupled with batteries and thermal storage), taking the perspective of the private investor<br> • in all buildings (all sectors, also with PV), with a focus on increased short-term flexibility, thereby also considering other strategies next to storage (e.g. demand-side response) and taking the perspective of the social planner.<br> In both cases, optimisation frameworks were developed for Switzerland and different scenarios were studied for the next decades.
description: Project Funded by the Swiss National Science Foundation (SNF), grant number 205121 172703/1
DOI: 10.5258/soton/d2269
title: Dataset supporting the Southampton Doctoral thesis titled: 'It Needs to Be, "We've Given Every Child with ADHD...Every Opportunity to Succeed".'

description: This dataset contains: Meta-analysis.zip These are Microsoft Excel files with the data extracted from all the papers identified for inclusion in the meta-analysis titled: The Effects of ADHD Teacher Training Programs on Teachers and Pupils: A Systematic Review and Meta-Analysis. The data contains largely means, standard deviations, sample numbers and there is also a t-test result for a paper which did not report sufficient means and standard deviations. The data was extracted independently by Rebecca Ward and Sarah Bristow and then compared to ensure any disputes were resolved. This data was used in the software Comprehensive Meta Analysis although it could be used in multiple forms of software to calculate meta-analyses. School staff perspectives interview transcriptions These are Microsoft Word files with anonymised transcripts of audio files from interviews with school staff used in the qualitative study titled: School staff perspectives on ADHD and training: understanding the needs and views of UK primary staff. The data was transcribed and anonymised by Rebecca Ward and a research team including undergraduate voluntary research assistants at the University of Southampton. The data was then uploaded into NVivo software for analysis although the data can be analysed using a variety of programs. Related projects/Funders: University of Southampton Jubilee Scholarship from the School of Psychology
DOI: 10.5281/zenodo.6568778
title: ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches

description: Adversarial patches are optimized contiguous pixel blocks in an input image that cause a machine-learning model to misclassify it. However, their optimization is computationally demanding and requires careful hyperparameter tuning. To overcome these issues, we propose ImageNet-Patch, a dataset to benchmark machine-learning models against adversarial patches. It consists of a set of patches optimized to generalize across different models and applied to ImageNet data after preprocessing them with affine transformations. This process enables an approximate yet faster robustness evaluation, leveraging the transferability of adversarial perturbations. We release our dataset as a set of folders indicating the patch target label (e.g., `banana`), each containing 1000 subfolders as the ImageNet output classes. An example showing how to use the dataset is shown below. <pre><code class="language-python"># code for testing robustness of a model import os.path from torchvision import datasets, transforms, models import torch.utils.data class ImageFolderWithEmptyDirs(datasets.ImageFolder): """ This is required for handling empty folders from the ImageFolder Class. """ def find_classes(self, directory): classes = sorted(entry.name for entry in os.scandir(directory) if entry.is_dir()) if not classes: raise FileNotFoundError(f"Couldn't find any class folder in {directory}.") class_to_idx = {cls_name: i for i, cls_name in enumerate(classes) if len(os.listdir(os.path.join(directory, cls_name))) &gt; 0} return classes, class_to_idx # extract and unzip the dataset, then write top folder here dataset_folder = 'data/ImageNet-Patch' available_labels = { 487: 'cellular telephone', 513: 'cornet', 546: 'electric guitar', 585: 'hair spray', 804: 'soap dispenser', 806: 'sock', 878: 'typewriter keyboard', 923: 'plate', 954: 'banana', 968: 'cup' } # select folder with specific target target_label = 954 dataset_folder = os.path.join(dataset_folder, str(target_label)) normalizer = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) transforms = transforms.Compose([ transforms.ToTensor(), normalizer ]) dataset = ImageFolderWithEmptyDirs(dataset_folder, transform=transforms) model = models.resnet50(pretrained=True) loader = torch.utils.data.DataLoader(dataset, shuffle=True, batch_size=5) model.eval() batches = 10 correct, attack_success, total = 0, 0, 0 for batch_idx, (images, labels) in enumerate(loader): if batch_idx == batches: break pred = model(images).argmax(dim=1) correct += (pred == labels).sum() attack_success += sum(pred == target_label) total += pred.shape[0] accuracy = correct / total attack_sr = attack_success / total print("Robust Accuracy: ", accuracy) print("Attack Success: ", attack_sr) </code></pre>
DOI: 10.5281/zenodo.6568777
title: ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches

description: Adversarial patches are optimized contiguous pixel blocks in an input image that cause a machine-learning model to misclassify it. However, their optimization is computationally demanding and requires careful hyperparameter tuning. To overcome these issues, we propose ImageNet-Patch, a dataset to benchmark machine-learning models against adversarial patches. It consists of a set of patches optimized to generalize across different models and applied to ImageNet data after preprocessing them with affine transformations. This process enables an approximate yet faster robustness evaluation, leveraging the transferability of adversarial perturbations. We release our dataset as a set of folders indicating the patch target label (e.g., `banana`), each containing 1000 subfolders as the ImageNet output classes. An example showing how to use the dataset is shown below. <pre><code class="language-python"># code for testing robustness of a model import os.path from torchvision import datasets, transforms, models import torch.utils.data class ImageFolderWithEmptyDirs(datasets.ImageFolder): """ This is required for handling empty folders from the ImageFolder Class. """ def find_classes(self, directory): classes = sorted(entry.name for entry in os.scandir(directory) if entry.is_dir()) if not classes: raise FileNotFoundError(f"Couldn't find any class folder in {directory}.") class_to_idx = {cls_name: i for i, cls_name in enumerate(classes) if len(os.listdir(os.path.join(directory, cls_name))) &gt; 0} return classes, class_to_idx # extract and unzip the dataset, then write top folder here dataset_folder = 'data/ImageNet-Patch' available_labels = { 487: 'cellular telephone', 513: 'cornet', 546: 'electric guitar', 585: 'hair spray', 804: 'soap dispenser', 806: 'sock', 878: 'typewriter keyboard', 923: 'plate', 954: 'banana', 968: 'cup' } # select folder with specific target target_label = 954 dataset_folder = os.path.join(dataset_folder, str(target_label)) normalizer = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) transforms = transforms.Compose([ transforms.ToTensor(), normalizer ]) dataset = ImageFolderWithEmptyDirs(dataset_folder, transform=transforms) model = models.resnet50(pretrained=True) loader = torch.utils.data.DataLoader(dataset, shuffle=True, batch_size=5) model.eval() batches = 10 correct, attack_success, total = 0, 0, 0 for batch_idx, (images, labels) in enumerate(loader): if batch_idx == batches: break pred = model(images).argmax(dim=1) correct += (pred == labels).sum() attack_success += sum(pred == target_label) total += pred.shape[0] accuracy = correct / total attack_sr = attack_success / total print("Robust Accuracy: ", accuracy) print("Attack Success: ", attack_sr) </code></pre>
DOI: 10.5517/ccdc.csd.cc28xlc5
title: CCDC 2112847: Experimental Crystal Structure Determination

description: Related Article: Qiaoyan Xing, Fuhong Xiao, Guojiang Mao, Guo-Jun Deng|2022|Org.Lett.|24|4377|doi:10.1021/acs.orglett.2c01599
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.cc22fg33
title: CCDC 1919554: Experimental Crystal Structure Determination

description: Related Article: M. Ramos Silva, M. Susano|2022|Solid State Commun.|353|114854|doi:10.1016/j.ssc.2022.114854
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.cc29cs3l
title: CCDC 2126479: Experimental Crystal Structure Determination

description: Related Article: Meiling Li, Yan Gao, Weiting Yang, Chaowei Zhang, Yu Fang, Cong Wang, Shuyan Song, Qinhe Pan|2022|Inorg.Chem.|61|9801|doi:10.1021/acs.inorgchem.2c01332
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: 10.5255/ukda-sn-855808
title: The Lived Experience of Waiting and Delay, 2018-2019

description: Waiting is a pervasive feature of life in organisations, yet little prior research has focused on the lived experience of waiting from the perspective of the employee. The present study aims to rectify this omission by tracking in depth the varied waiting experiences of 33 diverse individuals in three occupational settings via a multi-method approach involving participant-led photography and photo-elicitation interviews. The research provides insights into the 'qualities and textures' of waiting, employee agency during waiting experiences, and the spatial and material contours of the waits. This may include ‘micro-waiting’, short-term, long-term, or indeterminate waiting. This study contributes to knowledge of the temporal experience of work, and sheds light on a previously hidden aspect of employee attitudes and experiences. The dataset comprises 33 semi-structured interviews with ceramic artists, office workers and hairdressers, and 371 photographs taken by participants.
DOI: 10.5281/zenodo.6782932
title: Buddhist Chinese Word Embeddings

description: Buddhist Chinese word embeddings trained with FastText on the Buddhist texts present in the Kanseki repository. There are four models present here (and the full binary output for one), each differing in how the Kanseki repository was segmented into tokens. The "chinese_model" was segmented into 1-grams (individual characters). The "chinese_model_word" was segmented into words using a dictionary of buddhist terms and phrases not found were segmented with the classical Chinese word segmenter distributed with the stanza python library (https://stanfordnlp.github.io/stanza/available_models.html). The "chinese_model_hybrid_char_term" was segmented with a glossary of Buddhist terms and phrases not found were divided into 1-grams. "chinese_model_hybrid_char_term_2" uses a more extensive glossary of Buddhist terms and missing sections are divided into 1-grams. All models are default 100 dimensional FastText models created for this pilot study: Felbur, Rafal, Marieke Meelen & Paul Vierthaler (2022), 'Crosslinguistic Semantic Textual Similarity of Buddhist Chinese and Classical Tibetan' in <em>Journal of Open Humanities Data</em>. This research was done with generous funding from the Open Philology project. This project (running 2018–2022) is funded by the European Research Council (ERC) under the Horizon 2020 program (Advanced Grant agreement No 741884). It is based at the Leiden University Institute for Area Studies.
DOI: 10.5281/zenodo.6782931
title: Buddhist Chinese Word Embeddings

description: Buddhist Chinese word embeddings trained with FastText on the Buddhist texts present in the Kanseki repository. There are four models present here (and the full binary output for one), each differing in how the Kanseki repository was segmented into tokens. The "chinese_model" was segmented into 1-grams (individual characters). The "chinese_model_word" was segmented into words using a dictionary of buddhist terms and phrases not found were segmented with the classical Chinese word segmenter distributed with the stanza python library (https://stanfordnlp.github.io/stanza/available_models.html). The "chinese_model_hybrid_char_term" was segmented with a glossary of Buddhist terms and phrases not found were divided into 1-grams. "chinese_model_hybrid_char_term_2" uses a more extensive glossary of Buddhist terms and missing sections are divided into 1-grams. All models are default 100 dimensional FastText models created for this pilot study: Felbur, Rafal, Marieke Meelen & Paul Vierthaler (2022), 'Crosslinguistic Semantic Textual Similarity of Buddhist Chinese and Classical Tibetan' in <em>Journal of Open Humanities Data</em>. This research was done with generous funding from the Open Philology project. This project (running 2018–2022) is funded by the European Research Council (ERC) under the Horizon 2020 program (Advanced Grant agreement No 741884). It is based at the Leiden University Institute for Area Studies.
DOI: 10.5255/ukda-sn-855667
title: Sonic Enhancement of Virtual Exhibits Data, 2020-2021

description: We conducted an online experiment to explore how sound influences the interest level, emotional response, and engagement of individuals who view objects within a virtual exhibit. As part of this experiment, we designed a set of different soundscapes, which we presented to participants who viewed museum objects virtually. We then asked participants to report their felt affect and level of engagement with the exhibits. This dataset contains the data we recorded in these experiments and used in the analysis presented in our paper entitled 'Sonic Enhancement of Virtual Exhibits'.
DOI: 10.25402/pme.20198339
title: Supplementary Tables – The pharmacogenetics of mycophenolate mofetil in Tunisian renal transplant patients

description: <strong>Table 1: </strong>Primers, restriction enzymes and fragments lengths <strong>Table 2</strong>: Association of genotypes with tacrolimus and cyclosporine trough concentrations measured at the 3rd month post-RT <strong>Table 3:</strong> Association of genotypes with tacrolimus and cyclosporine trough concentrations measured at the 6th month post-RT
DOI: 10.25402/pme.20198339.v1
title: Supplementary Tables – The pharmacogenetics of mycophenolate mofetil in Tunisian renal transplant patients

description: <strong>Table 1: </strong>Primers, restriction enzymes and fragments lengths <strong>Table 2</strong>: Association of genotypes with tacrolimus and cyclosporine trough concentrations measured at the 3rd month post-RT <strong>Table 3:</strong> Association of genotypes with tacrolimus and cyclosporine trough concentrations measured at the 6th month post-RT
DOI: 10.5255/ukda-sn-855824
title: Evaluating The Association of Knowledge and Serum Levels in Vitamin D Consumers: A Cross-Sectional Survey During the COVID-19 Pandemic, 2020-2021

description: Vitamin D deficiency is more prevalent in Iran, making the rational use of it more critical. To this end, assessing people’s knowledge about the rational use of vitamin D and its safety is worthwhile to pursue. Objectives: This study aims to evaluate the level of consumers’ knowledge about vitamin D and its relationship with serum levels. Methods: This is a cross-sectional survey designed to assess consumers’ knowledge and its association with demographic characteristics and their serum 25-hydroxy vitamin D3 (25(OH)D) levels in community pharmacies. The association between demographic parameters, lifestyle, habits, and serum levels was reported. Results: 384 individuals participated in this study, among whom 345 (89.8%) consumers requested large doses of vitamin D3. The average knowledge score was 23.67 ± 7.76 out of 50. The average serum level of 25(OH)D was reported as 49.01 ± 32.01 ng/ml, resulting in 117 (30.5%) subjects were in the "insufficient" levels, 223 (58.1%) were in the "sufficient”, and 44 (11.5%) were in the "hypervitaminosis" levels. There is a significant inverse association between knowledge and age, weight, body mass index, education levels, serum level, and monthly dose. Also, a significant inverse association was detected between knowledge in terms of toxicity and vitamin D level. Further, participants’ knowledge of "hypervitaminosis" was lower than those with "sufficient" levels. Conclusion: Although almost all consumers had a sufficient level of 25(OH)D, the lack of knowledge about vitamin D was evident. It could lead the people being exposed to hypervitaminosis, as were seen in 11.5% of the participants.
DOI: 10.5281/zenodo.6782974
title: Dataset liver

description: Dataset liver
DOI: 10.5281/zenodo.6782973
title: Dataset liver

description: Dataset liver
DOI: 10.57865/k867-ah67
title: Czech GGS COVID Pilot – a Follow-up study
DOI: 10.5281/zenodo.6782955
title: Supporting information for " Modeling Injection-Induced Fracture Propagation in Crystalline Rocks by a Fluid-Solid Coupling Grain-Based Model "

description: This Excel files contain the experimental and simulation data for " Modeling Injection-Induced Fracture Propagation in Crystalline Rocks by a Fluid-Solid Coupling Grain-Based Model " (JGR: Solid Earth). The paper is authored by Song Wang, Jian Zhou, Luqing Zhang, Thomas Nagel, Zhenhua Han, and Yanlong Kong.
DOI: 10.5281/zenodo.6782954
title: Supporting information for " Modeling Injection-Induced Fracture Propagation in Crystalline Rocks by a Fluid-Solid Coupling Grain-Based Model "

description: This Excel files contain the experimental and simulation data for " Modeling Injection-Induced Fracture Propagation in Crystalline Rocks by a Fluid-Solid Coupling Grain-Based Model " (JGR: Solid Earth). The paper is authored by Song Wang, Jian Zhou, Luqing Zhang, Thomas Nagel, Zhenhua Han, and Yanlong Kong.
DOI: 10.5281/zenodo.6697271
title: Viral Culture in Early Nineteenth-Century Europe newspaper dataset

description: Dataset produced during the project Viral Culture in Early Nineteenth-Century Europe. The project traced text reuse by analysing large OCR'd newspaper collections using a BLAST based algorithm. This algorithm produces text clusters. This dataset contains two produced cluster datasets based on two different data collections. For the first dataset, the Austrian ANNO newspaper collection, this dataset contains metadata describing the used newspapers. For the second dataset, German-language newspapers in the Europeana collection, this dataset contains project produced metadata describing the newspapers used by the project, as well as the OCR's content for these newspaper issues. The OCR is produced with Tesseract OCR from digital page images downloaded from the Europeana services.
DOI: 10.5281/zenodo.6697270
title: Viral Culture in Early Nineteenth-Century Europe newspaper dataset

description: Dataset produced during the project Viral Culture in Early Nineteenth-Century Europe. The project traced text reuse by analysing large OCR'd newspaper collections using a BLAST based algorithm. This algorithm produces text clusters. This dataset contains two produced cluster datasets based on two different data collections. For the first dataset, the Austrian ANNO newspaper collection, this dataset contains metadata describing the used newspapers. For the second dataset, German-language newspapers in the Europeana collection, this dataset contains project produced metadata describing the newspapers used by the project, as well as the OCR's content for these newspaper issues. The OCR is produced with Tesseract OCR from digital page images downloaded from the Europeana services.
DOI: 10.6084/m9.figshare.19333181
title: Annotation files for the marmalade hoverfly (Episyrphus balteatus) genome

description: Gene annotation file in gtf format and repeats annotation file in bed format. Gene annotation file includes gene_id; transcript_id; gene_name (Drosophila=Human BLAST hit) and gene_description. Protein-coding gene models from Augustus were named EBAG0xxxxxx, non-coding RNA gene models EBAG1xxxxxx and Stringtie RNAseq based ones EBAG2xxxxxx. For details see: Genome-wide transcriptomic changes reveal the genetic pathways involved in insect migration” by Doyle et al. The sequence datasets associated with this annotation have been deposited at DDBJ/ENA/GenBank and are available via Bioproject PRJNA720810. The genome assembly described in this paper has been deposited under the accession JAGTYB000000000. The version described in the above paper is version JAGTYB010000000. <br> Doyle et al (2022) Genome-wide transcriptomic changes reveal the genetic pathways involved in insect migration. <em>Molecular Ecology</em>.