cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10091263
title: Evaluation of short alkyl chain modified [DBU][TFSI] based ionic liquids as supercapacitor electrolytes
title: Evaluation of short alkyl chain modified [DBU][TFSI] based ionic liquids as supercapacitor electrolytes (dataset)

description: This dataset contains the measurement data for figures (graphs) published in journal article:

Evaluation of short alkyl chain modified [DBU][TFSI] based ionic liquids as supercapacitor electrolytes (doi: 10.1016/j.electacta.2023.143659)

by Olli Pitkänen, Nemanja Vucetic, Helene Cabaud, Eva Bozo, Topias Järvinen, Jyri-Pekka Mikkola and Krisztian Kordas
DOI: 10.5281/zenodo.10091264
title: Evaluation of short alkyl chain modified [DBU][TFSI] based ionic liquids as supercapacitor electrolytes
title: Evaluation of short alkyl chain modified [DBU][TFSI] based ionic liquids as supercapacitor electrolytes (dataset)

description: This dataset contains the measurement data for figures (graphs) published in journal article:

Evaluation of short alkyl chain modified [DBU][TFSI] based ionic liquids as supercapacitor electrolytes (doi: 10.1016/j.electacta.2023.143659)

by Olli Pitkänen, Nemanja Vucetic, Helene Cabaud, Eva Bozo, Topias Järvinen, Jyri-Pekka Mikkola and Krisztian Kordas
DOI: 10.57967/hf/1488
title: eumetsat-rss
DOI: 10.6084/m9.figshare.23943231
title: A Study on the Candidate Gene Association and Interaction with Measures of UV Exposure in Pseudoexfoliation Patients from India

description: Environmental and genetic factors are associated with development of Pseudoexfoliation syndrome (XFS). Here we intended to elucidate the association of candidate genes in relevance to UV exposure in these patients. This is a case-control study of 309 subjects (<i>N</i> = 219 controls and 90 XFS cases) from India. PCR based direct sequencing was performed for candidate genes (<i>LOXL1, POMP</i> and <i>TMEM136</i>) followed by genotype and haplotype analysis. The promoter methylation status was assessed by Methylation specific PCR based direct sequencing of genomic DNA for all samples. The methylation status was compared with that of primary fibroblasts cultures established from patient’s Tenon’s tissue samples in subset of these patients. SNPs rs3825942, rs41435250, rs8818 (<i>LOXL1</i>) and rs3737528 (<i>POMP</i>) showed significant association with XFS. <i>LOXL1</i> gene haplotype GAGC (rs1048661- rs3825942- rs41435250–rs8818) was associated with lower risk for XFS with a p value 4.1961 × 10<sup>−6</sup> (OR =0; 95%CI, 0.000–0.003). <i>POMP</i> gene haplotypes for intronic SNPs (rs1340815- rs3737528- rs913797) TCC and TTC were associated with increased risk for the disease (OR &gt; 1.0). Significant correlation for SNPs rs3825942 of <i>LOXL1</i> (ρ= −0.132) and rs3737528 of <i>POMP</i> (ρ = 0.12) was observed with measure of lifetime UV exposure (CUVAF value). Reduced <i>LOXL1</i> gene expression was observed in cultured tenon fibroblasts from the patients that correlated with differential methylation of the Sp-1 binding sites at -253, -243bp upstream to the transcription start site of <i>LOXL1</i> promoter region. Our results suggest a possible interaction for <i>LOXL1</i> gene haplotype (GAGC) with the measure of ocular UV exposure in pseudoexfoliation syndrome.
DOI: 10.6084/m9.figshare.24018114
title: Machine Learning Model with Texture Analysis for Automatic Classification of Histopathological Images of Ocular Adnexal Mucosa-associated Lymphoid Tissue Lymphoma of Two Different Origins

description: The purpose of this study was to develop artificial intelligence algorithms that can distinguish between orbital and conjunctival mucosa-associated lymphoid tissue (MALT) lymphomas in pathological images. Tissue blocks with residual MALT lymphoma and data from histological and flow cytometric studies and molecular genetic analyses such as gene rearrangement were procured for 129 patients treated between April 2008 and April 2020. We collected pathological hematoxylin and eosin-stained (HE) images of lymphoma from these patients and cropped 10 different image patches at a resolution of 2048 × 2048 from pathological images from each patient. A total of 990 images from 99 patients were used to create and evaluate machine-learning models. Each image patch of three different magnification rates at ×4, ×20, and ×40 underwent texture analysis to extract features, and then seven different machine-learning algorithms were applied to the results to create models. Cross-validation on a patient-by-patient basis was used to create and evaluate models, and then 300 images from the remaining 30 cases were used to evaluate the average accuracy rate. Ten-fold cross-validation using the support vector machine with linear kernel algorithm was identified as the best algorithm for discriminating between conjunctival mucosa-associated lymphoid tissue and orbital MALT lymphomas, with an average accuracy rate under cross-validation of 85%. There were ×20 magnification HE images that were more accurate in distinguishing orbital and conjunctival MALT lymphomas among ×4, ×20, and ×40. Artificial intelligence algorithms can successfully distinguish HE images between orbital and conjunctival MALT lymphomas.
DOI: 10.6084/m9.figshare.24018114.v1
title: Machine Learning Model with Texture Analysis for Automatic Classification of Histopathological Images of Ocular Adnexal Mucosa-associated Lymphoid Tissue Lymphoma of Two Different Origins

description: The purpose of this study was to develop artificial intelligence algorithms that can distinguish between orbital and conjunctival mucosa-associated lymphoid tissue (MALT) lymphomas in pathological images. Tissue blocks with residual MALT lymphoma and data from histological and flow cytometric studies and molecular genetic analyses such as gene rearrangement were procured for 129 patients treated between April 2008 and April 2020. We collected pathological hematoxylin and eosin-stained (HE) images of lymphoma from these patients and cropped 10 different image patches at a resolution of 2048 × 2048 from pathological images from each patient. A total of 990 images from 99 patients were used to create and evaluate machine-learning models. Each image patch of three different magnification rates at ×4, ×20, and ×40 underwent texture analysis to extract features, and then seven different machine-learning algorithms were applied to the results to create models. Cross-validation on a patient-by-patient basis was used to create and evaluate models, and then 300 images from the remaining 30 cases were used to evaluate the average accuracy rate. Ten-fold cross-validation using the support vector machine with linear kernel algorithm was identified as the best algorithm for discriminating between conjunctival mucosa-associated lymphoid tissue and orbital MALT lymphomas, with an average accuracy rate under cross-validation of 85%. There were ×20 magnification HE images that were more accurate in distinguishing orbital and conjunctival MALT lymphomas among ×4, ×20, and ×40. Artificial intelligence algorithms can successfully distinguish HE images between orbital and conjunctival MALT lymphomas.
DOI: 10.5281/zenodo.10369089
title: PanE dataset
DOI: 10.5281/zenodo.10369090
title: PanE dataset
DOI: 10.5285/0b4e44aa-cef8-4c86-e063-6c86abc04a36
title: Inorganic carbon and total alkalinity at the Western Channel Observatory from the L4 site from 2008 to 2022

description: Dissolved inorganic carbon and total alkalinity measurements sampled from station L4, part of the Western Channel Observatory, and analysed at Plymouth Marine Laboratory, 2008 to 2022. These measurements, together with temperature and salinity (Fishwick, 2018), total phosphate and silicate (Woodward & Harris 2023), (all available on the WCO website https://www.westernchannelobservatory.org.uk/data/), are used to calculate additional carbonate system parameters (pH, pCO2, saturation states of calcite and aragonite, HCO3- and CO32-) using the programme CO2sys [Lewis & Wallace, 1998], with dissociation constants from Lueker et al. [2000] and for KHSO4 from Dickson [1990]. Standard deviations on the values were propagated through to the calculated parameters following Orr et al. [2018]. Measurements are made as part of NERC National Capability time-series monitoring at the Western Channel Observatory (WCO).
DOI: 10.5281/zenodo.10390576
title: Arctic Climate Response to European Radiative Forcing: A Deep Learning Approach (Example codes)

description: ## IntroductionThis folder contains example code used in our paper with the title "Arctic Climate Response to European Radiative Forcing: A Deep Learning Approach". These scripts are intended to demonstrate key functionalities and calculations described in the paper.

## Files Description

1. **DL_Model_test.py**   - Description: Loads the trained deep learning algorithms featured in the paper.   - Functionality: Demonstrates the use of the model with example data points.

2. **SIC_class_contribution.py**   - Description: Calculates the class contribution for Sea Ice Concentration (SIC).   - Note: The procedure can be adapted for other fields in a similar manner.

## Requirementsthe requred linrary are listed in the code header
DOI: 10.5281/zenodo.10390577
title: Arctic Climate Response to European Radiative Forcing: A Deep Learning Approach (Example codes)

description: ## IntroductionThis folder contains example code used in our paper with the title "Arctic Climate Response to European Radiative Forcing: A Deep Learning Approach". These scripts are intended to demonstrate key functionalities and calculations described in the paper.

## Files Description

1. **DL_Model_test.py**   - Description: Loads the trained deep learning algorithms featured in the paper.   - Functionality: Demonstrates the use of the model with example data points.

2. **SIC_class_contribution.py**   - Description: Calculates the class contribution for Sea Ice Concentration (SIC).   - Note: The procedure can be adapted for other fields in a similar manner.

## Requirementsthe requred linrary are listed in the code header
DOI: 10.34740/kaggle/dsv/7208455
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.25446/oxford.24786258.v2
title: Pandemic PACT Grant Tracker (raw)

description: Pandemic PACT Grant Tracking data (raw)................
DOI: 10.6084/m9.figshare.24826332
title: Influence of air-sea CO2 disequilibrium on biological carbon pump sequestration inventories and timescales

description: This dataset provides output from carbon sequestration calculations presented in the following paper: Nowicki, M., DeVries, T., & Siegel, D. A. (2024). The influence of air-sea CO2 disequilibrium on carbon sequestration by the ocean's biological carbon pump. <i>Global Biogeochemical Cycles</i>.The dataset provides dissolved inorganic carbon (DIC) concentrations computed using the full nonlinear and linearized models under both instantaneous and realistic gas exchange scenarios, as described in the above referenced paper. The average sequestration times under both gas exchange scenarios are also included, as well as the air-sea CO2 equilibration timescale and biological DIC flux (referred to as Jbio in the paper) vectors used in all of these computations. The dataset also provides the fraction of biogenic DIC sequestered for 0-1000 years under both gas exchange scenarios, referred to as f(x,t) in Equation 9 and used to create Figure 5 in the above referenced paper. This dataset only includes years 0-1000 of the 7000 year simulation shown in Figure 5. Longer simulations can be obtained by contacting the author. Also included is a MATLAB script with examples for creating some of the figures. The script loads in the dataset and extracts the relevant variables for computing the DIC inventories shown in Table 2, as well as for creating Figures 1-5. If using this dataset, please cite the original publication of Nowicki et al. (2024).
DOI: 10.17632/5skx2c69nr.1
title: Lipid dysmetabolism in ceruloplasmin-deficient mice revealed both in vivo and ex vivo by MRI, MRS and NMR analyses

description: - Raw images of liver histological sections stained either with hematoxylin-eosin or with anti-F4/80 antigen antibody (counterstaining with hematoxylin)
- Raw images of adipose tissue histological sections stained either with hematoxylin-eosin or with anti-F4/80 antigen antibody (counterstaining with hematoxylin)
- Raw data from MRS and NMR spectra (files can be used with MestreNova software, Mestrelab Research, S.L. Santiago de Compostela, Spain)
DOI: 10.5281/zenodo.3938817
title: Criminal Network: The Sicilian Mafia. "Montagna Operation"

description: The datasets reported relate to criminal networks directly obtained from Court data:



Meetings dataset, accounts for the physical meetings among suspected (police stakeout). It is composed by 101 nodes and 256 edges.

Phone Calls dataset refers to phone calls among individuals (police eavesdropping). It is composed by 100 nodes and 124 edges.

Roles dataset address the role of the suspected as well as their relationship with the other suspected, and the request from the judge (e.g. arrested/in jail/etc.).


Meetings and Phone Calls datasets have in common 47 nodes (i.e., suspected).

“Our datasets were derived from the pre-trial detention order issued by the Court of Messina’s preliminary investigation judge on March 14, 2007, which was towards the end of the major anti-mafia operation referred to as “Montagna Operation”. This operation was concluded in 2007 by the Public Prosecutor’s Office of Messina (Sicily) and was conducted by the Special Operations Unit of the Italian Police (the R.O.S. Reparto Operativo Speciale of the Carabinieri is specializing in anti-Mafia investigations). This particular investigation was a prominent operation focused on two Mafia clans, known as the “Mistretta” family and the “Batanesi clan”. From 2003 to 2007, these families were found to had infiltrated several economic activities including major infrastructure works, through a cartel of entrepreneurs close to the Sicilian Mafia.” (From https://arxiv.org/abs/2003.05303)

For more details, please refer to:



Journal publication PLoS ONE: https://journals.plos.org/plosone/article/citation?id=10.1371/journal.pone.0236476

Preliminary analysis in our conference paper: https://link.springer.com/chapter/10.1007/978-3-030-36683-4_36

Our pre-print on ArXiv: https://arxiv.org/abs/2003.05303

Our GitHub Repository: https://github.com/lcucav/networkdistruption
DOI: 10.5281/zenodo.3938818
title: Criminal Network: The Sicilian Mafia. "Montagna Operation"

description: The datasets reported relate to criminal networks directly obtained from Court data:



Meetings dataset, accounts for the physical meetings among suspected (police stakeout). It is composed by 101 nodes and 256 edges.

Phone Calls dataset refers to phone calls among individuals (police eavesdropping). It is composed by 100 nodes and 124 edges.

Roles dataset address the role of the suspected as well as their relationship with the other suspected, and the request from the judge (e.g. arrested/in jail/etc.).


Meetings and Phone Calls datasets have in common 47 nodes (i.e., suspected).

“Our datasets were derived from the pre-trial detention order issued by the Court of Messina’s preliminary investigation judge on March 14, 2007, which was towards the end of the major anti-mafia operation referred to as “Montagna Operation”. This operation was concluded in 2007 by the Public Prosecutor’s Office of Messina (Sicily) and was conducted by the Special Operations Unit of the Italian Police (the R.O.S. Reparto Operativo Speciale of the Carabinieri is specializing in anti-Mafia investigations). This particular investigation was a prominent operation focused on two Mafia clans, known as the “Mistretta” family and the “Batanesi clan”. From 2003 to 2007, these families were found to had infiltrated several economic activities including major infrastructure works, through a cartel of entrepreneurs close to the Sicilian Mafia.” (From https://arxiv.org/abs/2003.05303)

For more details, please refer to:



Journal publication PLoS ONE: https://journals.plos.org/plosone/article/citation?id=10.1371/journal.pone.0236476

Preliminary analysis in our conference paper: https://link.springer.com/chapter/10.1007/978-3-030-36683-4_36

Our pre-print on ArXiv: https://arxiv.org/abs/2003.05303

Our GitHub Repository: https://github.com/lcucav/networkdistruption
DOI: 10.21227/nccz-6781
title: A dataset of pilots' cognitive collapse

description: The dataset encompasses the recordings of 10 participants over 3 separate sessions in the simulated flight experiment paradigm. The experiment aims to elicit the pilots' cognitive collapse state and to early warn of the tipping points, for details you can refer to the paper "An Early Warning Approach for Pilots’ Cognitive Tipping Points Based Multi-modal Signals". The dataset includes EEG, eye movement, task performance data, and experimenter's recording. It has a total of 30 trials and is approximately 4.7G in size.EEG signal. EEG data was recorded using the 32-channel NeuSen W wireless digital EEG recording system at 1000 Hz, placed according to the international 10-20 system. It can be read and manipulated using libraries such as EEGLAB or MNE.
DOI: 10.5258/soton/wp00766
title: Piloting the collection of geographic information from Longitudinal Population Studies (LPS) for future climate-health research. Report version 1.0

description: Piloting the collection of geographic information from Longitudinal Population Studies (LPS) for future climate-health research to harmonise boundaries across multiple study sites.
DOI: 10.25446/oxford.24786258.v3
title: Pandemic PACT Grant Tracker (raw)

description: Pandemic PACT Grant Tracking data (raw)................
DOI: 10.25446/oxford.24786258
title: Pandemic PACT Grant Tracker (raw)

description: Pandemic PACT Grant Tracking data (raw)................
DOI: 10.5281/zenodo.10364614
title: Ship logs from ARCTOS Barents Sea Polar Front 2021-05 cruise

description: PolarFront 2021-05 ship logs. Original (ISO 8859-1 encoded) text files from the ship logger on Helmer Hanssen.
DOI: 10.5281/zenodo.10364615
title: Ship logs from ARCTOS Barents Sea Polar Front 2021-05 cruise

description: PolarFront 2021-05 ship logs. Original (ISO 8859-1 encoded) text files from the ship logger on Helmer Hanssen.
DOI: 10.5281/zenodo.10390629
title: Large-Scale Traveling Ionospheric Disturbances over the European sector during the geomagnetic storm on March 23-24, 2023: energy deposition in the source regions and the propagation characteristics

description: IMAGE 2D Ionospheric Equivalent Currents for 23 and 24 March 2023 (https://space.fmi.fi/image/). 

We thank the institutes who maintain the IMAGE Magnetometer Array (https://space.fmi.fi/image/): Tromsø Geophysical Observatory of UiT the Arctic University of Norway (Norway), Finnish Meteorological Institute (Finland), Institute of Geophysics Polish Academy of Sciences (Poland), GFZ German Research Centre for Geosciences (Germany), Geological Survey of Sweden (Sweden), Swedish Institute of Space Physics (Sweden), Sodankylä Geophysical Observatory of the University of Oulu (Finland), DTU Technical University of Denmark (Denmark), and Science Institute of the University of Iceland (Iceland). The provisioning of data from AAL, GOT, HAS, NRA, VXJ, FKP, ROE, BFE, BOR, HOV, SCO, KUL, and NAQ is supported by the ESA contracts number 4000128139/19/D/CT as well as 4000138064/22/D/KS. The authors would like to thank Dr. Liisa Juusola for providing the IMAGE 2D Ionospheric Equivalent Currents data.
DOI: 10.5281/zenodo.10390628
title: Large-Scale Traveling Ionospheric Disturbances over the European sector during the geomagnetic storm on March 23-24, 2023: energy deposition in the source regions and the propagation characteristics

description: IMAGE 2D Ionospheric Equivalent Currents for 23 and 24 March 2023 (https://space.fmi.fi/image/). 

We thank the institutes who maintain the IMAGE Magnetometer Array (https://space.fmi.fi/image/): Tromsø Geophysical Observatory of UiT the Arctic University of Norway (Norway), Finnish Meteorological Institute (Finland), Institute of Geophysics Polish Academy of Sciences (Poland), GFZ German Research Centre for Geosciences (Germany), Geological Survey of Sweden (Sweden), Swedish Institute of Space Physics (Sweden), Sodankylä Geophysical Observatory of the University of Oulu (Finland), DTU Technical University of Denmark (Denmark), and Science Institute of the University of Iceland (Iceland). The provisioning of data from AAL, GOT, HAS, NRA, VXJ, FKP, ROE, BFE, BOR, HOV, SCO, KUL, and NAQ is supported by the ESA contracts number 4000128139/19/D/CT as well as 4000138064/22/D/KS. The authors would like to thank Dr. Liisa Juusola for providing the IMAGE 2D Ionospheric Equivalent Currents data.
DOI: 10.5281/zenodo.10390820
title: Cultivar and harvest time of almonds modify the antioxidant and nutritional profile of almonds through gut microbiota modifications (Supplementary materials)
DOI: 10.5281/zenodo.10390819
title: Cultivar and harvest time of almonds modify the antioxidant and nutritional profile of almonds through gut microbiota modifications (Supplementary materials)
DOI: 10.5517/ccdc.csd.cc2d3x3k
title: CCDC 2208288: Experimental Crystal Structure Determination

description: Related Article: Rose Jordan, Iván Maisuls, Shruthi S. Nair, Benjamin Dietzek-Ivanšić, Cristian A. Strassert, Axel Klein|2023|Dalton Trans.|52|18220|doi:10.1039/D3DT03225F
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.cc2dk3rv
title: CCDC 2220987: Experimental Crystal Structure Determination

description: Related Article: Andrey D. Vinokurov, Taygib M. Iliyasov, Kirill A. Karpenko, Yana V. Derkach, Anton A. Ryabov, Sergey V. Gorbunov, Alexander S. Goloveshkin, Anatoly N. Vereshchagin|2023|Tetrahedron|149|133685|doi:10.1016/j.tet.2023.133685
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.cc2fqx14
title: CCDC 2256336: Experimental Crystal Structure Determination

description: Related Article: Dong-Lin Zhao, Xue-Yan Huo, Pan-Pan Li, Xiao-Long Yuan, Sheng-Ying Li, Lei Du, Li-Jun Huang, Peng Zhang|2023|Tetrahedron|149|133744|doi:10.1016/j.tet.2023.133744
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.cc2d1tlw
title: CCDC 2206288: Experimental Crystal Structure Determination

description: Related Article: Phuong-Y Mai, Erwan Poupon, Jamal Ouazzani, Rany B. Mbeng Obame, Philippe Lopes, Pascal Retailleau, Jean-François Gallard, Karine Leblanc, Xavier Moppert, Bernard Costa, Géraldine Le Goff, Mehdi A. Beniddir|2023|Tetrahedron|149|133712|doi:10.1016/j.tet.2023.133712
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.