cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7439276
title: MatSim Dataset and benchmark for one-shot visual materials and textures recognition

description: <strong>The MatSim Dataset and benchmark</strong> Synthetic dataset and real images benchmark for visual similarity recognition of materials and textures. MatSim: a synthetic dataset, a benchmark, and a method for computer vision-based recognition of similarities and transitions between materials and textures focusing on identifying any material under any conditions using one or a few examples (one-shot learning). Based on the paper: One-shot recognition of any material anywhere using contrastive learning with physics-based rendering <strong>Benchmark_MATSIM.zip: </strong>contain the benchmark made of real-world images as described in the paper <strong>Dataset Generation Scripts.zip: </strong>Contain the Blender (3.1) Python scripts used for generating the dataset<br> <br> <strong>MatSim_object_train_split_1,2,3.zip:</strong> Contain a subset of the synthetics dataset for images of CGI images materials on random objects as described in the paper. <strong>MatSim_Vessels_Train_1,2,3.zip </strong><strong>:</strong> Contain a subset of the synthetics dataset for images of CGI images materials inside transparent containers as described in the paper.<br> <br> <strong>*Note: these are subsets of the dataset; the full dataset can be found at:</strong><br> https://e1.pcloud.link/publink/show?code=kZIiSQZCYU5M4HOvnQykql9jxF4h0KiC5MX or<br> https://icedrive.net/s/A13FWzZ8V2aP9T4ufGQ1N3fBZxDF
DOI: 10.5281/zenodo.10104859
title: Effectiveness of Nutritional Web Application on Nutrition Status Improvement among Patients with Colorectal Cancer
DOI: 10.5281/zenodo.10104858
title: Effectiveness of Nutritional Web Application on Nutrition Status Improvement among Patients with Colorectal Cancer
DOI: 10.5281/zenodo.10056138
title: PyVIPER: A fast and scalable Python package for rank-based enrichment analysis of single-cell RNASeq data

description: Data Repository for VIPER analysis in Python. These data are used in Tutorials of the PyVIPER package. All files were generated by post-processing publicly available data from pancreatic ductal adenocarcinoma (PDAC) patients by Peng et al., 2019.
The files included are:

B-cell-net.tsv: ARACNe3-inferred gene regulatory network for B cells in PDAC
ductal-2-net.tsv: ARACNe3-inferred gene regulatory network for malignant ductal cell type 2 in PDAC
fibroblast-net.tsv: ARACNe3-inferred gene regulatory network for fibroblasts in PDAC
stellate-net.tsv: ARACNe3-inferred gene regulatory network for stellate cells in PDAC
Tutorial_1_gExpr_fibroblast_5802.tsv.gz: gene expression signature calculated for 5802 cells (fibroblasts) used in Tutorial 1
Tutorial_2_counts_mixed_4632.tsv.gz: UMI matrix for 4632 cells from different cellular populations used in Tutorial 2
Tutorial_2_metadata_mixed_4632.tsv.gz: metadata for 4632 cells from different cellular populations used in Tutorial 2
 

Files in .pkl format are the ARACNe3-inferred gene regulatory networks for the specific cell population in PDAC in .pkl format
DOI: 10.5281/zenodo.10056139
title: Processed Peng dataset

description: Data Repository for VIPER analysis in Python. These data are used in Tutorials of the VIPERPY package. All files were generated by post-processing publicly available data from pancreatic ductal adenocarcinoma (PDAC) patients by Peng et al., 2019.
The files included are:

B-cell-net.tsv: ARACNe3-inferred gene regulatory network for B cells in PDAC
ductal-2-net.tsv: ARACNe3-inferred gene regulatory network for malignant ductal cell type 2 in PDAC
fibroblast-net.tsv: ARACNe3-inferred gene regulatory network for fibroblasts in PDAC
stellate-net.tsv: ARACNe3-inferred gene regulatory network for stellate cells in PDAC
Tutorial_1_gExpr_fibroblast_5802.tsv.gz: gene expression signature calculated for 5802 cells (fibroblasts) used in Tutorial 1
Tutorial_2_counts_mixed_4632.tsv.gz: UMI matrix for 4632 cells from different cellular populations used in Tutorial 2
Tutorial_2_metadata_mixed4632.tsv.gz: metadata for 4632 cells from different cellular populations used in Tutorial 2
DOI: 10.5281/zenodo.10104870
title: pseudotriton_montanus_decontaminated_FSCR.fasta.gz

description: pseudotriton_montanus_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.10104871
title: pseudotriton_montanus_decontaminated_FSCR.fasta.gz

description: pseudotriton_montanus_decontaminated_FSCR.fasta.gz
DOI: 10.7284/131500
title: ADCP (Acoustic Doppler Current Profiler) data collected during research cruise AE1806 using a Hawaii UHDAS instrument system onboard the platform RV Atlantic Explorer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/131831
title: TSG (Thermosalinograph) data collected during research cruise AE1806 using a SeaBird SBE-21 instrument system onboard the platform RV Atlantic Explorer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/131827
title: Winch data collected during research cruise AE1806 using a Markey DUSH-5 instrument system onboard the platform RV Atlantic Explorer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.5255/ukda-sn-856638
title: Flexible Dialogue Control for Intuitive Spatial Communication: Doll Dialogue Data, 2007-2007

description: This data collection addresses the impact of dialogue strategies and functional features of spatial arrangements on communicative success. To examine the sharing of cognition between two minds in order to achieve a joint goal, we collected a corpus of 24 extended German-language dialogues in a referential communication task that involved furnishing a dolls’ house. Results show how successful communication, as evidenced by correct placement of furniture items, is affected by: (a) functionality of the furniture arrangement; (b) previous task experience; and (c) dialogue features such as description length and orientation information. This downloadable dialogue corpus allows these issues to be addressed, designed with the following core features that together set it apart from previous data: a. Naturalistic action related dialogue. Two ‘naïve’ speakers (no confederate) are confronted with a task involving action, to be solved based on verbal interaction. b. Asymmetric knowledge. Each dialogue involves a ‘director’ and a ‘matcher’. The ‘director’ knows about the specific task goals (here: the target configuration), and informs the ‘matcher’ accordingly. c. Everyday spatial concepts in a multi-object arrangement context. The task involves placing dolls’ house furniture in a target arrangement, similar to a range of common tasks in everyday life through evoking relevant action scripts and schemata that guide expectations about object location. d. Experimental variation. Furniture arrangements differ with respect to the functional relationship between objects and therefore in their congruence with context frames, and hence common ground (prior to communication). e. Operationalized success. Correct object placement informs about communicative success. f. Task experience. After swapping roles, dialogue partners can build on previous task experience. This resource includes transcripts (total: 126,846 words) and pictures showing the furnished dolls’ houses, allowing to evaluate task success.
DOI: 10.5281/zenodo.10104943
title: МАЙДАЛОВЧИ БАРАБАНГА ЎРНАТИЛГАН ПИЧОҚ КЎНДАЛАНГ КЕСИМИ ШАКЛИНИНГ ЎҒИТНИНГ МАЙДАЛАНИШИГА ТАЪСИРИ
DOI: 10.5281/zenodo.10104944
title: МАЙДАЛОВЧИ БАРАБАНГА ЎРНАТИЛГАН ПИЧОҚ КЎНДАЛАНГ КЕСИМИ ШАКЛИНИНГ ЎҒИТНИНГ МАЙДАЛАНИШИГА ТАЪСИРИ
DOI: 10.5281/zenodo.10059791
title: PyVIPER: A fast and scalable Python package for rank-based enrichment analysis of single-cell RNASeq data

description: Data Repository for VIPER analysis in Python. These data are used in Tutorials of the PyVIPER package. All files were generated by post-processing publicly available data from pancreatic ductal adenocarcinoma (PDAC) patients by Peng et al., 2019.
The files included are:

B-cell-net.tsv: ARACNe3-inferred gene regulatory network for B cells in PDAC
ductal-2-net.tsv: ARACNe3-inferred gene regulatory network for malignant ductal cell type 2 in PDAC
fibroblast-net.tsv: ARACNe3-inferred gene regulatory network for fibroblasts in PDAC
stellate-net.tsv: ARACNe3-inferred gene regulatory network for stellate cells in PDAC
Tutorial_1_gExpr_fibroblast_5802.tsv.gz: gene expression signature calculated for 5802 cells (fibroblasts) used in Tutorial 1
Tutorial_2_counts_mixed_4632.tsv.gz: UMI matrix for 4632 cells from different cellular populations used in Tutorial 2
Tutorial_2_metadata_mixed_4632.tsv.gz: metadata for 4632 cells from different cellular populations used in Tutorial 2
 

Files in .pkl format are the ARACNe3-inferred gene regulatory networks for the specific cell population in PDAC in .pkl format
DOI: 10.7284/131502
title: ADCP (Acoustic Doppler Current Profiler) data collected during research cruise AE1807 using a Hawaii UHDAS instrument system onboard the platform RV Atlantic Explorer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/131833
title: TSG (Thermosalinograph) data collected during research cruise AE1807 using a SeaBird SBE-21 instrument system onboard the platform RV Atlantic Explorer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/131832
title: Winch data collected during research cruise AE1807 using a Markey DUSH-5 instrument system onboard the platform RV Atlantic Explorer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.24535135
title: Characterisation of volatile and particulate emissions from home 3D printers

description: This dataset was obtained by characterising volatile and particulate emissions from home 3D printers. <br>
DOI: 10.21227/m4vb-p620
title: Abnormal High Density Crowds

description: Data Preprocessing:All video footage in this dataset has undergone comprehensive preprocessing techniques, including frame extraction, anomaly identification, cropping of frames to emphasize crowded scenes, and compression to reduce storage space.Overview:This dataset encompasses four distinct scene incidents, each meticulously detailed below: 1. Times Square Chaos:Video Source: Times Square Chaos from Three Angles Viewpoints: Three different perspectives.View_1: Angled shot, abnormal behavior starts at 00:00:12. Dimensions: 1280x720.View_2: Closeup eye-level shot, abnormal behavior starts at 00:00:53. Dimensions: 1280x720.View_3: Angled shot with cropped dimensions (580x720), abnormal behavior starts at 00:01:39.2. Las Vegas Mass Shooting:Video Source: Las Vegas Mass Shooting CCTV Video Training & Testing Frames: Divided into normal and abnormal instances at different angles and time frames.Train: Wide angled shot, normal behavior between 00:11:17 and 00:16:05. Dimensions: 992x468.Test_1: Same angle as training footage, abnormal behavior between 00:16:06 and 00:17:17. Dimensions: 992x468.Test_2, Test_3, Test_4: Varied angles and grayscale for abnormal behavior instances. Dimensions: 1280x720.3. Love Parade Disaster:Video Source: Love Parade Disaster Training & Testing Frames: Split into normal and abnormal sequences at a specific angle and time frame.Train_1, Train_2: Angled shots with normal behavior within defined time frames. Dimensions: 1280x720.Test: Abnormal behavior between 00:10:48 and 00:11:03. Dimensions: 1280x720. Frame-level labeling stored in XML format.4. Juventus Fan Panic in Italy:Video Sources: Video 1, Video 2 Viewpoints: Divided into two distinct viewpoints comprising abnormal behaviour instances.View_1: Wide, close, eye-level shot with abnormal behavior between 00:00:00 and 00:00:28. Dimensions: 1280x720.View_2: Wide angled shot, abnormal behavior between 00:00:00 and 00:00:28. Dimensions: 880x720.This dataset provides a comprehensive collection of annotated abnormal behaviors captured in different real-life scenarios, crucial for anomaly detection research and analysis.
DOI: 10.13012/b2idb-4326514_v1
title: Code and Data for "Quantifying Uncertainties in Greenhouse Gas Savings and Mitigation Costs with Cellulosic Biofuels"

description: Use of cellulosic biofuels from non-feedstocks are modeled using the BEPAM (Biofuel and Environmental Policy Analysis Model) model to quantifying the uncertainties about induced land use change effects, net greenhouse gas saving potential, and economic costs. The code is in GAMS, general algebraic modeling language. NOTE: Column 3 is titled "BAU" in "merged_BAU.gdx", "merged_RFS.gdx", and "merged_CEM.gdx", but contains "RFS" data in "merged_RFS.gdx" and "CEM" data in "merged_CEM.gdx".
DOI: 10.5281/zenodo.10104971
title: Sub-5 min RP-UHPLC-TIMS for high-throughput untargeted lipidomics and its application to multiple matrices
DOI: 10.5281/zenodo.10104972
title: Sub-5 min RP-UHPLC-TIMS for high-throughput untargeted lipidomics and its application to multiple matrices
DOI: 10.5281/zenodo.10105046
title: BOSHLANG'ICH SINF O'QUVCHILARIGA IJTIMOIY ME'YORLAR ASOSIDA NUTQIY SAVODXONLIKNI O'RGATISH
DOI: 10.5281/zenodo.10105047
title: BOSHLANG'ICH SINF O'QUVCHILARIGA IJTIMOIY ME'YORLAR ASOSIDA NUTQIY SAVODXONLIKNI O'RGATISH
DOI: 10.15151/esrf-es-1372726757
title: Preparation for CH6699: Single Layer FeS2 and NiS2 - New 2D materials, Part 1

description: First part of preparation for proposal no. CH6699 will be carried out. This will involve cleaning the gold (111) single crystal with ion bombardment, checking the cleanliness using Auger electron spectroscopy and calibration of the parameters for evaporating Iron and Nickel by e-beam evaporation. X-ray beam is not necessary.
DOI: 10.25934/pr00008848
title: Develop a mathematical model for Alzheimer's disease virtual clinical trial

description: Personalized medicine is an innovative approach that aims to tailor healthcare to individual patients based on their unique characteristics, such as genetic makeup, lifestyle factors, and environmental exposures. Alzheimer's disease is a prevalent neurodegenerative disorder that affects millions of people worldwide, causing cognitive decline and memory loss. The disease is estimated to affect more than 50 million people worldwide, and this number is expected to triple by 2050 due to the aging population. The development of personalized treatments for Alzheimer's disease is critical due to the complexity of the disease and the significant variability in how it manifests in different individuals.

In this project, we have developed a mathematical model that integrates cognitive, cerebrospinal fluid (CSF) biomarkers (this refers to abnormal clumps of proteins known as amyloid and tau, which are present in the brain; changes in the levels of these proteins are reflected in the CSF), positron emission tomopgraphy (PET) and magnetic resonance imaging (MRI) biomarkers of Alzheimer's disease. The "mathematical model 2019" refers to the Alzheimer's Disease Biomarker Cascade (ADBC) model that was published in 2019 [1]. The term "validated mathematical model" indicates that the ADBC model's parameters were trained using clinical data, enabling the model to accurately reflect clinical reality. The ADBC model incorporates four clinical biomarkers: Abeta, tau, neuron degeneration, and cognitive decline. "Individualized risk profiles" in this context refers to the personalized risk assessment for each biomarker in each patient. To "parameterize" the model means to train the mathematical model using optimization algorithms to ensure a good fit to the clinical data.

Regarding the relationship to dementia, CSF biomarkers provide valuable insights into the development and progression of dementia. Abnormal levels of amyloid and tau proteins in the CSF have been associated with Alzheimer's disease and other forms of dementia. Analyzing these biomarkers can help in the diagnosis, monitoring, and understanding of the underlying pathological processes in dementia.
It is worth noting that the term "imaging" mentioned in the context of the ADBC model refers to brain imaging techniques such as positron emission tomography (PET) and magnetic resonance imaging (MRI). These imaging modalities provide visual representations of the brain and can be used to complement the biomarker data in Alzheimer's disease research.

Our model is intended to help healthcare professionals personalize treatment plans for Alzheimer's patients by providing them with individualized risk profiles and potential prevention and treatment strategies. To validate and refine our model further, we plan to use clinical trial datasets to parameterize it. Ultimately, we aim to use this validated mathematical model to conduct research on potential treatments for Alzheimer's disease, potentially benefiting millions of people affected by this devastating condition.
DOI: 10.5281/zenodo.10103988
title: BACCHUS Long-Term Dataset

description: Agricultural multi-month dataset with overlapping paths for mapping and localisation algorithms for autonomous robots
In March 2022, we started conducting the long-term data acquisition campaign at Ktima Gerovassiliou vineyard. This vineyard extends for more than 100ha located on the outskirts of Epanomi, Greece.
As shown in the image below (taken in March and June), agricultural environments present seasonal changes, repetitive structures, uneven terrain and different weather conditions, which make achieving long-term autonomy for robots a challenging problem.
Motivated by these challenging conditions and the lack of an agricultural dataset in the literature, we present the BLT dataset. Its primary objective is to push developments and evaluations of different mapping and localisation algorithms for long-term autonomous robots operating in agricultural fields. However, we believe that thanks to its temporal aspect, the dataset can also be used for phenotyping and crop mapping tasks.
Available from https://lcas.lincoln.ac.uk/wp/research/data-sets-software/blt/
DOI: 10.5281/zenodo.10103987
title: BACCHUS Long-Term Dataset

description: Agricultural multi-month dataset with overlapping paths for mapping and localisation algorithms for autonomous robots
In March 2022, we started conducting the long-term data acquisition campaign at Ktima Gerovassiliou vineyard. This vineyard extends for more than 100ha located on the outskirts of Epanomi, Greece.
As shown in the image below (taken in March and June), agricultural environments present seasonal changes, repetitive structures, uneven terrain and different weather conditions, which make achieving long-term autonomy for robots a challenging problem.
Motivated by these challenging conditions and the lack of an agricultural dataset in the literature, we present the BLT dataset. Its primary objective is to push developments and evaluations of different mapping and localisation algorithms for long-term autonomous robots operating in agricultural fields. However, we believe that thanks to its temporal aspect, the dataset can also be used for phenotyping and crop mapping tasks.
Available from https://lcas.lincoln.ac.uk/wp/research/data-sets-software/blt/
DOI: 10.15129/9cdd2ccc-6dfa-4bb4-9678-4c6f467afdd5
title: Data for: "Geometry effects on energy selective focusing of laser-driven protons with open and closed hemisphere-cone targets"

description: This data corresponds to the experimental and simulation results reported in the publication "Geometry effects on energy selective focusing of laser-driven protons with open and closed hemisphere-cone targets". Simulation data was generated by running EPOCH version 4.17.10 with the input.deck files included. Experimental data was obtained as described in the publication.
DOI: 10.5281/zenodo.10105096
title: HISTORY OF DEVELOPMENT AND PROSPECTS FOR THE USE OF URETERAL STENTS AS A RATIONAL SOLUTION TO PROBLEMS OF URETERAL PATENCY
DOI: 10.5281/zenodo.10105095
title: HISTORY OF DEVELOPMENT AND PROSPECTS FOR THE USE OF URETERAL STENTS AS A RATIONAL SOLUTION TO PROBLEMS OF URETERAL PATENCY
DOI: 10.25625/68fgz2/fmix6q
title: Supporting_info_Dunkl_et_al_Table_S8.xls

description: :unav
DOI: 10.25625/68fgz2/tv3uk3
title: Supporting_info_Dunkl_et_al.docx

description: :unav
DOI: 10.25625/68fgz2/zpr4x1
title: Supporting_info_Dunkl_et_al_Table_S7.xls

description: :unav
DOI: 10.25625/68fgz2/lcukti
title: Supporting_info_Dunkl_et_al_Table_S4.xls

description: :unav
DOI: 10.25625/68fgz2/2dj0hw
title: Supporting_info_Dunkl_et_al_Table_S6.xls

description: :unav
DOI: 10.25625/68fgz2/6zdkip
title: Supporting_info_Dunkl_et_al_Table_S2.xls

description: :unav
DOI: 10.5255/ukda-sn-856719
title: COVID-19 Lockdowns, Mental Health and Wellbeing in Undergraduate Students, 2020-2022

description: The COVID-19 pandemic has had a substantial impact on mental health; because students are particularly vulnerable to loneliness, isolation, stress and unhealthy lifestyle choices, their mental health and wellbeing may potentially be more severely impacted by lockdown measures than the general population. This study assessed the mental health and wellbeing of UK undergraduate students during and after the lockdowns associated with the COVID-19 pandemic. Data were collected via online questionnaire at 3 time points – during the latter part of the first wave of the pandemic (spring/summer 2020; n=46) while stringent lockdown measures were still in place but gradually being relaxed; during the second wave of the pandemic (winter 2020-21; n=86) while local lockdowns were in place across the UK; and during the winter of 2021-22 (n=77), when infection rates were high but no lockdown measures were in place. Stress was found to most strongly predict wellbeing and mental health measures during the two pandemic waves. Other substantial predictors were diet quality and intolerance of uncertainty. Positive wellbeing was the least well accounted for of our outcome variables. Conversely, we found that depression and anxiety were higher during winter 2021-22 (no lockdowns) than winter 2020-21 (under lockdown). This may be due to the high rates of infection over that period and the effects of COVID-19 infection itself on mental health. This suggests that, as significant as the effects of lockdowns were on the wellbeing of the nation, not implementing lockdown measures could potentially have been even more detrimental for mental health.
DOI: 10.7284/131504
title: ADCP (Acoustic Doppler Current Profiler) data collected during research cruise AE1808 using a Hawaii UHDAS instrument system onboard the platform RV Atlantic Explorer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/131835
title: TSG (Thermosalinograph) data collected during research cruise AE1808 using a SeaBird SBE-21 instrument system onboard the platform RV Atlantic Explorer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/131834
title: Winch data collected during research cruise AE1808 using a Markey DUSH-5 instrument system onboard the platform RV Atlantic Explorer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.5281/zenodo.10105197
title: BOLALARDA NUTQ JARAYONINING RIVOJLANISH BOSQICHLARI VA UNDAGI MUAMMOLAR
DOI: 10.5281/zenodo.10105198
title: BOLALARDA NUTQ JARAYONINING RIVOJLANISH BOSQICHLARI VA UNDAGI MUAMMOLAR
DOI: 10.6084/m9.figshare.24100362
title: TerraTools example data

description: Example data for the terratools package. See https://terratools.readthedocs.io/en/latest/
DOI: 10.6084/m9.figshare.24100362.v3
title: TerraTools example data

description: Example data for the terratools package. See https://terratools.readthedocs.io/en/latest/
DOI: 10.6084/m9.figshare.24100362.v2
title: TerraTools example data

description: Example data for the terratools package. See https://terratools.readthedocs.io/en/latest/
DOI: 10.7284/131505
title: ADCP (Acoustic Doppler Current Profiler) data collected during research cruise AE1810 using a Hawaii UHDAS instrument system onboard the platform RV Atlantic Explorer

description: Original (unprocessed) data collected during a Field_expedition