cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/7k83-m346
title: Log file 

description: Abstract—Drones or Remote Piloted Aircrafts System (RPAS), have become incredibly popular in a variety of sectors. Critical uses of drones includes agriculture, delivery system and surveillance etc. As technology advances, the reliability of drone components becomes increasingly crucial. It is important to analyze the prime reasons of drone flight crashes as it can damage public property and it could also be harmful for human beings. Mechanical breakdowns, human error, battery failure, wildlife hazards could be one of the reasons for the flight crashes. In this study we are trying to make better understanding of the root causes of failures of drones for safer and more reliable drone technology. Sometimes the real reason related to the crash is not very clear, so flight log data analysis is required to pin point the causes. In this paper we analyzed data of one hexagonal drone crashed flight to identify the real cause of the failure of drone.
DOI: 10.5281/zenodo.6611925
title: International Virtual Permeability Benchmark 3D Image Dataset of the Fiber Tow Microscopic Sample

description: This microscopic 3D sample representing a part of the fiber tow of the woven composite reinforcement was used in the first phase of the international Virtual permeability benchmark. Its results are reported in [1-3]. While the permeability had been investigated before for various porous media, this benchmark exercise was the first contribution in the field of real fibrous microstructures.


The 3D image was extracted from a 3D X-ray microscope scan of a composite sample prepared at Leibniz-Institut für Verbundwerkstoffe GmbH (IVW, Kaiserslautern, Germany). The segmentation of the 3D image was performed at the Research Institute in Civil Engineering and Mechanics (GeM) of Ecole Centrale de Nantes (Nantes, France).
 
The segmented binary volume image data (.raw file) has the following characteristics:



	
dimensions of 1003 x 124 x 973 voxels;
	
0.5213 µm3 voxel size;
	
8-bit;
	
little-endian byte order.



The provided corresponding non-segmented volume image has the following characteristics:



	
dimensions of 1003 x 124 x 973 voxels;
	
0.5213 µm3 voxel size;
	
16-bit;
	
little-endian byte order.



The microscopic volume contains about 400 fibers of a single tow and covers the entire tow through-thickness dimension. Its average fiber volume fraction is 56.5%, though it varies locally along the fiber direction. The structure of the tow represents 3 yarns twisted together. The tow originates from the glass twill-weave fabric HexForce 01102 by HEXCEL.


 


[1]    Elena Syerko, Tim Schmidt, David May, Christophe Binetruy, Suresh G. Advani et al. Benchmark Exercise on Image-Based Permeability Determination of Engineering Textiles: Microscale Predictions // Composites Part A: Applied Science and Manufacturing – 2023: 107397.


[2]    Elena Syerko, Christophe Binetruy, Tim Schmidt, David May.  Micro-Scale Results from the Benchmark Exercise on the Image-Based Permeability Prediction of Composite Reinforcements // Proceedings of  IBSim-4i – Image-Based Simulation for Industry 2022, London, UK – 2022.


[3]    David May, Elena Syerko, Tim Schmidt, Christophe Binetruy, Luisa Rocha da Silva, Stepan Lomov, Suresh Advani. Benchmarking Virtual Permeability Predictions of Real Fibrous Microstructure // Proceedings of the American Society for Composites – 36th Technical Conference, Texas, USA – 2021.
DOI: 10.5281/zenodo.6611926
title: International Virtual Permeability Benchmark 3D Image Dataset of the Fiber Tow Microscopic Sample

description: This microscopic 3D sample representing a part of the fiber tow of the woven composite reinforcement was used in the first phase of the international Virtual permeability benchmark. Its results are reported in [1-3]. While the permeability had been investigated before for various porous media, this benchmark exercise was the first contribution in the field of real fibrous microstructures.


The 3D image was extracted from a 3D X-ray microscope scan of a composite sample prepared at Leibniz-Institut für Verbundwerkstoffe GmbH (IVW, Kaiserslautern, Germany). The segmentation of the 3D image was performed at the Research Institute in Civil Engineering and Mechanics (GeM) of Ecole Centrale de Nantes (Nantes, France).
 
The segmented binary volume image data (.raw file) has the following characteristics:



	
dimensions of 1003 x 124 x 973 voxels;
	
0.5213 µm3 voxel size;
	
8-bit;
	
little-endian byte order.



The provided corresponding non-segmented volume image has the following characteristics:



	
dimensions of 1003 x 124 x 973 voxels;
	
0.5213 µm3 voxel size;
	
16-bit;
	
little-endian byte order.



The microscopic volume contains about 400 fibers of a single tow and covers the entire tow through-thickness dimension. Its average fiber volume fraction is 56.5%, though it varies locally along the fiber direction. The structure of the tow represents 3 yarns twisted together. The tow originates from the glass twill-weave fabric HexForce 01102 by HEXCEL.


 


[1]    Elena Syerko, Tim Schmidt, David May, Christophe Binetruy, Suresh G. Advani et al. Benchmark Exercise on Image-Based Permeability Determination of Engineering Textiles: Microscale Predictions // Composites Part A: Applied Science and Manufacturing – 2023: 107397.


[2]    Elena Syerko, Christophe Binetruy, Tim Schmidt, David May.  Micro-Scale Results from the Benchmark Exercise on the Image-Based Permeability Prediction of Composite Reinforcements // Proceedings of  IBSim-4i – Image-Based Simulation for Industry 2022, London, UK – 2022.


[3]    David May, Elena Syerko, Tim Schmidt, Christophe Binetruy, Luisa Rocha da Silva, Stepan Lomov, Suresh Advani. Benchmarking Virtual Permeability Predictions of Real Fibrous Microstructure // Proceedings of the American Society for Composites – 36th Technical Conference, Texas, USA – 2021.
DOI: 10.25345/c51j97k1r
title: MassIVE MSV000093700 - Native metabolomics with E.coli CutA and Isosepiapterin
title: Native MS with E.coli CutA and Isosepiapterin to check for putative binding. Isosepiapterin was dissolved in 50% MeOH and run over a C18 column.
DOI: 10.5281/zenodo.10408883
title: Suomen kylät 1540-1940

description: Paikkatietoaineisto sisältää Suomen vanhat maakirjakylät voudintileistä 1900-luvulle.
DOI: 10.5281/zenodo.10408884
title: Suomen kylät 1540-1940

description: Paikkatietoaineisto sisältää Suomen vanhat maakirjakylät voudintileistä 1900-luvulle.
DOI: 10.57967/hf/1507
title: festcat_trimmed_denoised
DOI: 10.5281/zenodo.10227927
title: Reproducible network changes occur in a mouse model of temporal lobe epilepsy but do not correlate with disease severity

description: Dataset for the publication: 'Reproducible network changes occur in a mouse model of temporal lobe epilepsy but do not correlate with disease severity 'Rigoni et al. 2023, Neurobiology of Disease, doi: https://doi.org/10.1016/j.nbd.2023.106382

Dataset description

Data\data2publish\sub- : 50 epochs of raw epicranial EEG data (31 x 8001 x 50, channels x time x n_epochs, Fs=4k Hz). The epochs are available for 29 mice, on different sessions (ses-d0, ses-d28, ses-d29) depending on the animal 

Data\data2publish\EA_info.xlsx: number of epileptiform activities automatically detected for each animal at d28 and d29

Data\data2publish\derivatives\eeg_preprocessing: results of the script A_EEG_preprocessing.m, for each animal and session

Data\data2publish\derivatives\elec_layout: different layouts used to plot results. Mouse_layout_modif is the one used in Fig 4

Data\data2publish\derivatives\network_metrics_wpli: results of network analyses (script C_network_analyses.m)

Data\data2publish\derivatives\wpli: connectivity matrices (30 x 30) obtained with the script B_connectivity_wpli.m for each animal, in each session, for each frequency band wit

Code for analyses available here:  https://github.com/IsottaR/ir_mice_project_Zenodo 

Abbreviations:

EEG= electroencephalography
description: This work was supported by Swiss National Science Foundation (SNSF) grant no. 192749 CRSII5_170873 and CRSII5_209470to SV and grant no. 206720 to LS.
DOI: 10.5281/zenodo.10227928
title: Reproducible network changes occur in a mouse model of temporal lobe epilepsy but do not correlate with disease severity

description: Dataset for the publication: 'Reproducible network changes occur in a mouse model of temporal lobe epilepsy but do not correlate with disease severity 'Rigoni et al. 2023, Neurobiology of Disease, doi: https://doi.org/10.1016/j.nbd.2023.106382

Dataset description

Data\data2publish\sub- : 50 epochs of raw epicranial EEG data (31 x 8001 x 50, channels x time x n_epochs, Fs=4k Hz). The epochs are available for 29 mice, on different sessions (ses-d0, ses-d28, ses-d29) depending on the animal 

Data\data2publish\EA_info.xlsx: number of epileptiform activities automatically detected for each animal at d28 and d29

Data\data2publish\derivatives\eeg_preprocessing: results of the script A_EEG_preprocessing.m, for each animal and session

Data\data2publish\derivatives\elec_layout: different layouts used to plot results. Mouse_layout_modif is the one used in Fig 4

Data\data2publish\derivatives\network_metrics_wpli: results of network analyses (script C_network_analyses.m)

Data\data2publish\derivatives\wpli: connectivity matrices (30 x 30) obtained with the script B_connectivity_wpli.m for each animal, in each session, for each frequency band wit

Code for analyses available here:  https://github.com/IsottaR/ir_mice_project_Zenodo 

Abbreviations:

EEG= electroencephalography
description: This work was supported by Swiss National Science Foundation (SNSF) grant no. 192749 CRSII5_170873 and CRSII5_209470to SV and grant no. 206720 to LS.
DOI: 10.6084/m9.figshare.24874279
title: example_calls_per_individual.zip from Vocal complexity in a socially complex corvid: gradation, diversity and lack of common call repertoire in male rooks

description: Vocal communication is widespread in animals, with vocal repertoires of varying complexity. The social complexity hypothesis predicts that species may need high vocal complexity to deal with complex social organization (e.g. have a variety of different interindividual relations). We quantified the vocal complexity of two geographically distant captive colonies of rooks, a corvid species with complex social organization and cognitive performances, but understudied vocal abilities. We quantified the diversity and gradation of their repertoire, as well as the inter-individual similarity at the vocal unit level. We found that males produced call units with lower diversity and gradation than females, while song units did not differ between sexes. Surprisingly, while females produced highly similar call repertoires, even between colonies, each individual male produced almost completely different call repertoires from any other individual. These findings question the way male rooks communicate with their social partners. We suggest that each male may actively seek to remain vocally distinct, which could be an asset in their frequently changing social environment. We conclude that inter-individual similarity, an understudied aspect of vocal repertoires, should also be considered as a measure of vocal complexity.
DOI: 10.5281/zenodo.10409598
title: FIO (Zipfian) trace file for testing MiDAS code

description: This trace is an FIO (Zipfian) trace file, designed for testing MiDAS code. It consists of 54 million write operations on a 4GiB block device.

The format of the trace file is as follows: <Time Stamp (s)> <Request Type> <LBA (unit: 4KB)> <Request Size> <Stream Number>



Time Stamp (in seconds): This is the time when the request is made to a block device.

Request Type: This is categorized into four types: 0 for READ, 1 for WRITE, 2 for NOT USED, and 3 for TRIM.

LBA (Logical Block Address, unit: 4KB): This represents the logical block address on the block device, with each unit of LBA being 4KB.

Request Size: This indicates the size of the request being processed.

Stream Number: This is used for multi-stream SSDs, but is not utilized in MiDAS.
DOI: 10.5281/zenodo.10409599
title: FIO (Zipfian) trace file for testing MiDAS code

description: This trace is an FIO (Zipfian) trace file, designed for testing MiDAS code. It consists of 54 million write operations on a 4GiB block device.

The format of the trace file is as follows: <Time Stamp (s)> <Request Type> <LBA (unit: 4KB)> <Request Size> <Stream Number>



Time Stamp (in seconds): This is the time when the request is made to a block device.

Request Type: This is categorized into four types: 0 for READ, 1 for WRITE, 2 for NOT USED, and 3 for TRIM.

LBA (Logical Block Address, unit: 4KB): This represents the logical block address on the block device, with each unit of LBA being 4KB.

Request Size: This indicates the size of the request being processed.

Stream Number: This is used for multi-stream SSDs, but is not utilized in MiDAS.
DOI: 10.6084/m9.figshare.24316417
title: Wood decomposition and nutrient dynamics in burnt Mediterranean pine reforestations across an elevational gradient: a 15-year study.

description: The rate of wood decomposition after a forest fire was analyzed over a period of 15 years and six sampling periods (initial, and after 2, 4, 8, 10 and 15 years). 75 cm long pieces of logs were experimentally arranged along an elevation gradient (ca. 1400, 1700, 2000 and 2200 m a.s.l.) with contrasting climatic conditions. The concentration of C, N and P was also determined for four of the sampling periods (including the beginning and after 15 years) as well as the concentration of K, Ca, Mg, Na, Fe, Cu, Mn and Zn at the beginning and after 15 years. Nutrient analyses were carried out in 2006, 2008, 2010 and 2021. In 2006, the P content was determined by the molybdovanadate method and the rest of the nutrients (Ca, Mg, K, Na, Fe, Mn, Zn and Cu) by atomic absorption of the vegetal ash solution with a Perkin Elmer 5100 spectrometer (Waltham, MA, USA) (Marañón-Jiménez et al. 2013). In 2008 and 2010, the P content was determined by the molybdovanadate method with a Perkin Elmer 2400 spectrophotometer (Waltham, MA, USA) (Marañón-Jiménez and Castro 2013). In 2021, nutrient content (P, K, Na, Ca, Mg, Fe, Cu, Mn and Zn) was determined by inductively coupled plasma mass spectrometry (ICP-MS) with a Perkin Elmer Avio 500 spectrometer. For all sampling years, total C and N were determined with a CN Gas Analyzer by using an induction furnace and thermal conductivity (LECO TruSpec CN). Log volume was estimated by the conic-parabolic formula proposed by Fraver et al. (2007). Wood density was calculated by dividing wood dry weight by its volume. To check the accuracy of the 2021 data, nutrient analyses were repeated for 53 samples. The repeated samples showed consistency for all the nutrients except for Fe and Na, that gave random values without any correlation between the two trials of analysis. Therefore the values for these nutrients for 2021 were eliminated from the data set.
DOI: 10.6084/m9.figshare.24316417.v1
title: Wood decomposition and nutrient dynamics in burnt Mediterranean pine reforestations across an elevational gradient: a 15-year study.

description: The rate of wood decomposition after a forest fire was analyzed over a period of 15 years and six sampling periods (initial, and after 2, 4, 8, 10 and 15 years). 75 cm long pieces of logs were experimentally arranged along an elevation gradient (ca. 1400, 1700, 2000 and 2200 m a.s.l.) with contrasting climatic conditions. The concentration of C, N and P was also determined for four of the sampling periods (including the beginning and after 15 years) as well as the concentration of K, Ca, Mg, Na, Fe, Cu, Mn and Zn at the beginning and after 15 years. Nutrient analyses were carried out in 2006, 2008, 2010 and 2021. In 2006, the P content was determined by the molybdovanadate method and the rest of the nutrients (Ca, Mg, K, Na, Fe, Mn, Zn and Cu) by atomic absorption of the vegetal ash solution with a Perkin Elmer 5100 spectrometer (Waltham, MA, USA) (Marañón-Jiménez et al. 2013). In 2008 and 2010, the P content was determined by the molybdovanadate method with a Perkin Elmer 2400 spectrophotometer (Waltham, MA, USA) (Marañón-Jiménez and Castro 2013). In 2021, nutrient content (P, K, Na, Ca, Mg, Fe, Cu, Mn and Zn) was determined by inductively coupled plasma mass spectrometry (ICP-MS) with a Perkin Elmer Avio 500 spectrometer. For all sampling years, total C and N were determined with a CN Gas Analyzer by using an induction furnace and thermal conductivity (LECO TruSpec CN). Log volume was estimated by the conic-parabolic formula proposed by Fraver et al. (2007). Wood density was calculated by dividing wood dry weight by its volume. To check the accuracy of the 2021 data, nutrient analyses were repeated for 53 samples. The repeated samples showed consistency for all the nutrients except for Fe and Na, that gave random values without any correlation between the two trials of analysis. Therefore the values for these nutrients for 2021 were eliminated from the data set.
DOI: 10.5281/zenodo.10409117
title: High-quality teleseismic waveform records of nodal seismometers in the northern Sumatra, Indonesia

description: This dataset includes three-component teleseismic records (M≥5.5) used for receiver function calculation, waveform analysis, and imaging in Feng et al. (submitted to JGR: Solid Earth). The data are collected from the mobile seismic nodal array jointly deployed by Earth Observatory of Singapore, Singapore and Universitas Syiah Kuala, Indonesia during January 2020 to July 2021 (Lythgoe et al., 2022, https://doi.org/10.1029/2022eo220140). Please check out the readme document for detailed information.
DOI: 10.5281/zenodo.10409118
title: High-quality teleseismic waveform records of nodal seismometers in the northern Sumatra, Indonesia

description: This dataset includes three-component teleseismic records (M≥5.5) used for receiver function calculation, waveform analysis, and imaging in Feng et al. (submitted to JGR: Solid Earth). The data are collected from the mobile seismic nodal array jointly deployed by Earth Observatory of Singapore, Singapore and Universitas Syiah Kuala, Indonesia during January 2020 to July 2021 (Lythgoe et al., 2022, https://doi.org/10.1029/2022eo220140). Please check out the readme document for detailed information.
DOI: 10.5258/soton/d2882
title: Dataset in support of the Southampton doctoral thesis 'The role of matrix metalloproteinease 28 and gut microbiome in the regulation of obesity and metabolic disorders'

description: All animal managements and analysis.xlsx contains data presented in my thesis. It contains the different mouse groups used in this project and housed in different environments. Specific pathogen free environment (SPF) and conventional mouse room (CMR). The file also demonstrates mice age in weeks at the time of data collection along with their body weight and food intake measurements. It also shows their energy expenditure EE measurements in day and night in addition to tissue measurements done at the time of sampling which includes different white adipose tissue depots, liver and heart. There is also data on the fasting blood glucose measurements.
DOI: 10.6084/m9.figshare.24874302
title: adjoint-elastic-registration.zip from Organ registration from partial surface data in augmented surgery from an optimal control perspective

description: We address the problem of organ registration in augmented surgery, where the deformation of the patient’s organ is reconstructed in real-time from a partial observation of its surface. Physics-based registration methods rely on adding artificial forces to drive the registration, which may result in implausible displacement fields. In this paper, we look at this inverse problem through the lens of optimal control, in an attempt to reconstruct a physically consistent surface load. The resulting optimization problem features an elastic model, a least-squares data attachment term based on orthogonal projections, and an admissible set of surface loads defined prior to reconstruction in the mechanical model. After a discussion about the existence of solutions, we analyse the necessary optimality conditions and use them to derive a suitable optimization algorithm. We implement an adjoint method and we test our approach on multiple examples, including the so-called <i>Sparse Data Challenge</i>. We obtain very promising results, that illustrate the feasibility of our approach with linear and nonlinear models.
DOI: 10.6084/m9.figshare.24874302.v1
title: adjoint-elastic-registration.zip from Organ registration from partial surface data in augmented surgery from an optimal control perspective

description: We address the problem of organ registration in augmented surgery, where the deformation of the patient’s organ is reconstructed in real-time from a partial observation of its surface. Physics-based registration methods rely on adding artificial forces to drive the registration, which may result in implausible displacement fields. In this paper, we look at this inverse problem through the lens of optimal control, in an attempt to reconstruct a physically consistent surface load. The resulting optimization problem features an elastic model, a least-squares data attachment term based on orthogonal projections, and an admissible set of surface loads defined prior to reconstruction in the mechanical model. After a discussion about the existence of solutions, we analyse the necessary optimality conditions and use them to derive a suitable optimization algorithm. We implement an adjoint method and we test our approach on multiple examples, including the so-called <i>Sparse Data Challenge</i>. We obtain very promising results, that illustrate the feasibility of our approach with linear and nonlinear models.
DOI: 10.34740/kaggle/dsv/7246352
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.34740/kaggle/dsv/7245309
title: Google Recaptcha V2 Images
DOI: 10.25505/fiz.icsd.cc2351cq
title: CSD 1941262: Experimental Crystal Structure Determination

description: Related Article: Siméon Ponou, Sven Lidin, Anja V. Mudring|2022|ICSD Communication|||
description: Related Article: Siméon Ponou, Sven Lidin, Anja-Verena Mudring|2023|Inorg.Chem.|62|8519|doi:10.1021/acs.inorgchem.2c04312
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2d6j38
title: CSD 2210799: Experimental Crystal Structure Determination

description: Related Article: Yilin Niu, Yue Ding, Hongxin Sheng, Sai Sun, Chaoqin Chen, Jing Du, Hong-Ying Zang, Peng Yang|2022|Inorg.Chem.|61|21024|doi:10.1021/acs.inorgchem.2c03543
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2d6j49
title: CSD 2210800: Experimental Crystal Structure Determination

description: Related Article: Yilin Niu, Yue Ding, Hongxin Sheng, Sai Sun, Chaoqin Chen, Jing Du, Hong-Ying Zang, Peng Yang|2022|Inorg.Chem.|61|21024|doi:10.1021/acs.inorgchem.2c03543
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.