cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/5vx9fk85zt.1
title: 2023_MBL_western_blots_translatome_manuscript_part2

description: The files contained herein are the original, unmodified western blots shown as panels in the main and supplementary figures for the manuscript.
DOI: 10.17632/3dp7jfv2vh.1
title: SISAP 2023 Indexing challenge –⁠ Learned Metric Index: Raw data, analyses, figures

description: ==== For complete code, description, data, and steps to reproduce, visit: https://github.com/LearnedMetricIndex/LearnedMetricIndex/tree/paper-sisap23-indexing-challenge ==== This repository contains the data for our submission to the SISAP 2023 Indexing challenge. We used a strip-down version of the Learned Metric Index (LMI), which is an index for approximate nearest neighbor search on complex data using machine learning and probability-based navigation. **Getting started** Follow the instructions in README.md –⁠ https://github.com/LearnedMetricIndex/LearnedMetricIndex/tree/paper-sisap23-indexing-challenge **Contents** 1. result/ - contains the raw .h5 files of each experiment (with varying hyperparameters), 2088 experiment in total 2. res.csv - contains the evaluation of every experiment (1 row) in terms of recall and query time 3. 02-Analyze-results.ipynb - Jupyter notebook used to analyze the results and plot the figures 4. cat.pdf, nobjects.pdf - figures used in the paper **Related Publications** &gt; M. Antol, J. Ol'ha, T. Slanináková, V. Dohnal: [Learned Metric Index—Proposition of learned indexing for unstructured data](https://www.sciencedirect.com/science/article/pii/S0306437921000326?casa_token=EvG8iaWkqQUAAAAA:xgfbutrsNGcBXnTN-U4MQ65hgmPE3fAyzwqtijzGC-JRrkO1IYNmcN3A8yMsSOT3CCoHpqVtMA). Information Systems, 2021 - Elsevier (2021) &gt; T. Slanináková, M. Antol, J. Ol'ha, V. Kaňa, V. Dohnal: [Learned Metric Index—Proposition of learned indexing for unstructured data](https://link.springer.com/chapter/10.1007/978-3-030-89657-7_7). SISAP 2021 - Similarity Search and Applications pp 81-94 (2021) &gt; J. Ol'ha, T. Slanináková, M. Gendiar, M. Antol, V. Dohnal: [Learned Indexing in Proteins: Extended Work on Substituting Complex Distance Calculations with Embedding and Clustering Techniques](https://arxiv.org/abs/2208.08910), and [Learned Indexing in Proteins: Substituting Complex Distance Calculations with Embedding and Clustering Techniques](https://link.springer.com/chapter/10.1007/978-3-031-17849-8_22) SISAP 2022 - Similarity Search and Applications pp 274-282 (2022) &gt; T. Slanináková, M. Antol, J. Ol'ha, V. Kaňa, V. Dohnal, S. Ladra, M. A. Martinez-Prieto: [Reproducible experiments with Learned Metric Index Framework](https://www.sciencedirect.com/science/article/pii/S0306437923000911). Information Systems, Volume 118, September 2023, 102255 (2023) **Mendeley dataset**: https://data.mendeley.com/datasets/8wp73zxr47/12 ** Authors** - Terézia Slanináková, Masaryk University - David Procházka, Masaryk University - Jaroslav Oľha, Masaryk University - Matej Antol, Masaryk University - Vlastislav Dohnal, Masaryk University
DOI: 10.17632/9zdxpy7vnt.1
title: Supplementary data for CyberFamily

description: This dataset includes the CyberFamily game materials used to evaluate the game prototype during the user studies. CyberFamily is a collaborative cybersecurity awareness game that is targeted to be played by children aged 9-12 and their parents. This proposed game aims to facilitate parent-child communication and knowledge sharing along with increasing awareness of cybersecurity issues. The uploaded documents are the content materials of the CyberFamily game. It contains a picture of the maze used in the game, one file with all the challenge cards for the parents, and one file with all the challenge cards for the children. This game is proposed as part of our ongoing research and is under active revision.
DOI: 10.5281/zenodo.8432075
title: Validated CFD Model for Multimode Gasoline Compression Ignition Engine

description: A validated CFD model for the multimode combustion engine developed under the DOE funded project DE-EE0008478 (Co-optimized Mixed-Mode Engine and Fuel Demonstrator for Improved Fuel Economy while Meeting Emissions Requirements). It incorporates advanced physics-based fuel surrogate models for thermophysical properties and reaction kinetics. The combustion modes include spark ignition (SI), low temperature combustion (LTC), and compression ignition (CI). The real fuel model was validated for RON60, RON70, RON80, RON90, and two biofuel blends. The validation cases can be found in https://doi.org/10.2172/1887341.
DOI: 10.5281/zenodo.8432074
title: Validated CFD Model for Multimode Gasoline Compression Ignition Engine

description: A validated CFD model for the multimode combustion engine developed under the DOE funded project DE-EE0008478 (Co-optimized Mixed-Mode Engine and Fuel Demonstrator for Improved Fuel Economy while Meeting Emissions Requirements). It incorporates advanced physics-based fuel surrogate models for thermophysical properties and reaction kinetics. The combustion modes include spark ignition (SI), low temperature combustion (LTC), and compression ignition (CI). The real fuel model was validated for RON60, RON70, RON80, RON90, and two biofuel blends. The validation cases can be found in https://doi.org/10.2172/1887341.
DOI: 10.5281/zenodo.8325631
title: First exploration of the runaway greenhouse transition with a GCM

description: Raw outputs of the Generic-PCM simulations presented in Chaverot et al. (2023). The files are named <em>AA_XWm2.nc</em> where AA is the name of the setup (W01, W1, W10, WCO2, E1, ECO2) and X is the insolation (in W.m<sup>-2</sup>) received by the planet in the simulation. Files corresponding to the dry transition assuming 1bar and 1.5bar of water vapor are named <em>W1_dry1bar_XWm2.nc</em> and <em>W1_dry1.5barXWm2.nc</em> respectively.
DOI: 10.5281/zenodo.8325630
title: First exploration of the runaway greenhouse transition with a GCM

description: Raw outputs of the Generic-PCM simulations presented in Chaverot et al. (2023). The files are named <em>AA_XWm2.nc</em> where AA is the name of the setup (W01, W1, W10, WCO2, E1, ECO2) and X is the insolation (in W.m<sup>-2</sup>) received by the planet in the simulation. Files corresponding to the dry transition assuming 1bar and 1.5bar of water vapor are named <em>W1_dry1bar_XWm2.nc</em> and <em>W1_dry1.5barXWm2.nc</em> respectively.
DOI: 10.48324/dandi.000575/0.231010.1811
title: Dataset of human medial temporal lobe neurons during a visual working memory task

description: We present an electrophysiological dataset recorded from thirteen subjects during a visual working memory task. Subjects were epilepsy patients undergoing intracranial monitoring for localization of epileptic seizures. Here, we recorded single neuron firing in 13 epilepsy patients (7 male) while they performed a visual working memory task. The task is a change detection task designed to examine the visual working memory of subjects. In each trial, arrays of colored squares were presented and had to be memorized. The number of squares determined the set size: 1, 2, 4 or 6. There was a total 192 trials per session. Each trial started with a warning signal (0.4 s) that was a red fixation dot. The fixation dot was then changed to black (0.4 – 0.5 s, jittered). A memory array (encoding period, 0.8 s) was followed by a delay (retention interval, 0.9 s). After the delay, a test array was shown (2 s) followed by a jittered inter-trial interval of 1.3 to 2.3 s. The participants indicated by button press (“Same” or “Different”, forced choice) whether the test array differed from the memory array. If the arrays differed, only one square changed in colour, but all squares remained on the same location. The fixation dot was visible on the screen during the whole trial period. Eight different colours were used for the memory and test array (yellow, red, green, blue, magenta, cyan, grey, black). Before starting the sessions, participants conducted trial runs in a practice session to learn the task. In this session we verified if participants were colour-blind and could discriminate all colours. Practice sessions were repeated until the participant understood the task and was able to follow the pace of the trials.
DOI: 10.5281/zenodo.8432152
title: Binned dissolved organic carbon (DOC), dissolved organic nitrogen (DON), and dissolved organic phosphorus (DOP) concentration observations in the ocean

description: Here we provided binned dissolved organic carbon (DOC), dissolved organic nitrogen (DON), and dissolved organic phosphorus (DOP) concentration observations in the ocean used for manuscript "Global patterns of surface ocean dissolved organic matter stoichiometry " submitted to Global Biogeochemical Cycles. DOC and DON concentrations observations are from a compilation of DOM data obtained from global ocean observations from 1994 to 2021 (Hansell et al., 2021, https://doi.org/10.25921/s4f4-ye35) DOP concentration observations are from the DOPv2021 database (Liang et al., 2022, https://doi.org/10.1038/s41597-022-01873-7) We binned the data into the OCIM2 grid with a resolution of 2˚x2˚ with 24 vertical layers. More info about OCIM2 grid can be found on https://tdevries.eri.ucsb.edu/models-and-data-products/
DOI: 10.5281/zenodo.8432115
title: Motif HOCOMOCO files preprocessed for DAStk

description: Pre-scanned motif files HOCOMOCO_v11_p1e-6_grch38.tar.gz, HOCOMOCO_v11_p1e-6_hg19.tar.gz and HOCOMOCO_v11_p1e-6_mm10.tar.gz for the <code>GRCh38/hg38</code>, <code>hg19</code> and <code>mm10</code> reference genomes, respectively. These files are input files for DAStk (https://github.com/Dowell-Lab/DAStk) and have been generated from HOCOMOCO's v11 mononucleotide model, background-corrected for each reference genome, as described in the following paper: <em>Kulakovskiy, I.V., Vorontsov, I.E., Yevshin, I.S., Sharipov, R.N., Fedorova, A.D., Rumynskiy, E.I., Medvedeva, Y.A., Magana-Mora, A., Bajic, V.B., Papatsenko, D.A., et al. (2018). HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis. Nucleic Acids Res 46, D252–D259.</em>
description: {"references": ["Tripodi, I.J.; Allen, M.A.; Dowell, R.D. Detecting Differential Transcription Factor Activity from ATAC-Seq Data. Molecules 2018, 23, 1136.", "Kulakovskiy, I.V., Vorontsov, I.E., Yevshin, I.S., Sharipov, R.N., Fedorova, A.D., Rumynskiy, E.I., Medvedeva, Y.A., Magana-Mora, A., Bajic, V.B., Papatsenko, D.A., et al. (2018). HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis. Nucleic Acids Res 46, D252\u2013D259."]}
DOI: 10.5281/zenodo.8432114
title: Motif HOCOMOCO files preprocessed for DAStk

description: Pre-scanned motif files HOCOMOCO_v11_p1e-6_grch38.tar.gz, HOCOMOCO_v11_p1e-6_hg19.tar.gz and HOCOMOCO_v11_p1e-6_mm10.tar.gz for the <code>GRCh38/hg38</code>, <code>hg19</code> and <code>mm10</code> reference genomes, respectively. These files are input files for DAStk (https://github.com/Dowell-Lab/DAStk) and have been generated from HOCOMOCO's v11 mononucleotide model, background-corrected for each reference genome, as described in the following paper: <em>Kulakovskiy, I.V., Vorontsov, I.E., Yevshin, I.S., Sharipov, R.N., Fedorova, A.D., Rumynskiy, E.I., Medvedeva, Y.A., Magana-Mora, A., Bajic, V.B., Papatsenko, D.A., et al. (2018). HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis. Nucleic Acids Res 46, D252–D259.</em>
description: {"references": ["Tripodi, I.J.; Allen, M.A.; Dowell, R.D. Detecting Differential Transcription Factor Activity from ATAC-Seq Data. Molecules 2018, 23, 1136.", "Kulakovskiy, I.V., Vorontsov, I.E., Yevshin, I.S., Sharipov, R.N., Fedorova, A.D., Rumynskiy, E.I., Medvedeva, Y.A., Magana-Mora, A., Bajic, V.B., Papatsenko, D.A., et al. (2018). HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis. Nucleic Acids Res 46, D252\u2013D259."]}
DOI: 10.48324/dandi.000576/0.231010.1811
title: Dataset of neurons and intracranial EEG from human amygdala during aversive dynamic visual stimulation

description: We present an electrophysiological dataset collected from the amygdalae of nine subjects attending a visual dynamic stimulation of emotional aversive content. The subjects were patients affected by epilepsy who underwent preoperative invasive monitoring in the mesial temporal lobe. Subjects were presented with dynamic visual sequences of fearful faces (aversive condition), interleaved with sequences of neutral landscapes (neutral condition).

We provide simultaneous recordings of intracranial EEG (iEEG) and neuronal spike times and waveforms, and metadata related to the task, subjects, sessions and electrodes in the NIX standard. We technically validated this dataset and provide here the spike sorting quality metrics and the spectra of iEEG signals.
This dataset allows the investigation of amygdalar response to dynamic aversive stimuli at multiple spatial scales, from the macroscopic EEG to the neuronal firing in the human brain.
DOI: 10.5281/zenodo.7081901
title: Copy number variations and their effect on the plasma proteome | Associations

description: Results and supplementary data from a GWAS investigation the relationship of CNVs and blood protein measurements.
description: The NSPHS was funded by the Foundation for Strategic Research (UG) and the European Commission FP6 (UG). Sequencing was performed by NGI (National Genomics Infrastructure), Sweden. Protein measurements were carried out by Olink Proteomics AB in Uppsala, Sweden. The computations and data handling were enabled by resources in project sens2016007 provided by the Swedish National Infrastructure for Computing (SNIC) at Uppsala Multidisciplinary Centre for Advanced Computational Science (UPPMAX), partially funded by the Swedish Research Council through grant agreement no. 2018-05973. Short read sequencing was funded by the Science for Life Laboratory (SciLifeLab) and Swedish Genomes Program, which has been made available by support from the Knut and Alice Wallenberg Foundation. Long-read sequencing was supported by the Marcus Borgström's and Hedström's foundations. This work was also funded by the Swedish Medical Research Council (2019-01497) and the Swedish Heart-Lung foundation (nr. 20200687).
DOI: 10.5281/zenodo.7081902
title: Characterizing Copy Number Variations and their association with plasma proteins using short and long-read sequencing | Association Results

description: Results from a GWAS investigation the relationship of CNVs and blood protein measurements.
description: The NSPHS was funded by the Foundation for Strategic Research (UG) and the European Commission FP6 (UG). Sequencing was performed by NGI (National Genomics Infrastructure), Sweden. Protein measurements were carried out by Olink Proteomics AB in Uppsala, Sweden. The computations and data handling were enabled by resources in project sens2016007 provided by the Swedish National Infrastructure for Computing (SNIC) at Uppsala Multidisciplinary Centre for Advanced Computational Science (UPPMAX), partially funded by the Swedish Research Council through grant agreement no. 2018-05973. Short read sequencing was funded by the Science for Life Laboratory (SciLifeLab) and Swedish Genomes Program, which has been made available by support from the Knut and Alice Wallenberg Foundation. Long-read sequencing was supported by the Marcus Borgström's and Hedström's foundations. This work was also funded by the Swedish Medical Research Council (2019-01497) and the Swedish Heart-Lung foundation (nr. 20200687).
DOI: 10.5281/zenodo.7861803
title: Copy number variations and their effect on the plasma proteome | Associations

description: Results from a GWAS investigation the relationship of CNVs and blood protein measurements.
description: The NSPHS was funded by the Foundation for Strategic Research (UG) and the European Commission FP6 (UG). Sequencing was performed by NGI (National Genomics Infrastructure), Sweden. Protein measurements were carried out by Olink Proteomics AB in Uppsala, Sweden. The computations and data handling were enabled by resources in project sens2016007 provided by the Swedish National Infrastructure for Computing (SNIC) at Uppsala Multidisciplinary Centre for Advanced Computational Science (UPPMAX), partially funded by the Swedish Research Council through grant agreement no. 2018-05973. Short read sequencing was funded by the Science for Life Laboratory (SciLifeLab) and Swedish Genomes Program, which has been made available by support from the Knut and Alice Wallenberg Foundation. Long-read sequencing was supported by the Marcus Borgström's and Hedström's foundations. This work was also funded by the Swedish Medical Research Council (2019-01497) and the Swedish Heart-Lung foundation (nr. 20200687).
DOI: 10.5281/zenodo.8432233
title: Copy number variations and their effect on the plasma proteome | Associations

description: Results and supplementary data from a GWAS investigation the relationship of CNVs and blood protein measurements.
description: The NSPHS was funded by the Foundation for Strategic Research (UG) and the European Commission FP6 (UG). Sequencing was performed by NGI (National Genomics Infrastructure), Sweden. Protein measurements were carried out by Olink Proteomics AB in Uppsala, Sweden. The computations and data handling were enabled by resources in project sens2016007 provided by the Swedish National Infrastructure for Computing (SNIC) at Uppsala Multidisciplinary Centre for Advanced Computational Science (UPPMAX), partially funded by the Swedish Research Council through grant agreement no. 2018-05973. Short read sequencing was funded by the Science for Life Laboratory (SciLifeLab) and Swedish Genomes Program, which has been made available by support from the Knut and Alice Wallenberg Foundation. Long-read sequencing was supported by the Marcus Borgström's and Hedström's foundations. This work was also funded by the Swedish Medical Research Council (2019-01497) and the Swedish Heart-Lung foundation (nr. 20200687).
DOI: 10.5517/ccdc.csd.cc23hc44
title: CCDC 1951175: Experimental Crystal Structure Determination

description: Related Article: Jing shi, Jie Li, Ming-Hui Zhang, Zhao-Qiang Zhu, Kai Chen, Lin Huang, Carl Redshaw|2023|Inorg.Chem.Commun.|157|111411|doi:10.1016/j.inoche.2023.111411
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.cc23hc55
title: CCDC 1951176: Experimental Crystal Structure Determination

description: Related Article: Jing shi, Jie Li, Ming-Hui Zhang, Zhao-Qiang Zhu, Kai Chen, Lin Huang, Carl Redshaw|2023|Inorg.Chem.Commun.|157|111411|doi:10.1016/j.inoche.2023.111411
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.cc23hc66
title: CCDC 1951177: Experimental Crystal Structure Determination

description: Related Article: Jing shi, Jie Li, Ming-Hui Zhang, Zhao-Qiang Zhu, Kai Chen, Lin Huang, Carl Redshaw|2023|Inorg.Chem.Commun.|157|111411|doi:10.1016/j.inoche.2023.111411
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.cc23hc77
title: CCDC 1951178: Experimental Crystal Structure Determination

description: Related Article: Jing shi, Jie Li, Ming-Hui Zhang, Zhao-Qiang Zhu, Kai Chen, Lin Huang, Carl Redshaw|2023|Inorg.Chem.Commun.|157|111411|doi:10.1016/j.inoche.2023.111411
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.cc23hc88
title: CCDC 1951179: Experimental Crystal Structure Determination

description: Related Article: Jing shi, Jie Li, Ming-Hui Zhang, Zhao-Qiang Zhu, Kai Chen, Lin Huang, Carl Redshaw|2023|Inorg.Chem.Commun.|157|111411|doi:10.1016/j.inoche.2023.111411
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.cc2fs8h0
title: CCDC 2257652: Experimental Crystal Structure Determination

description: Related Article: Chao Zhang, Ting-wei Wang, Zu-jia Lu, Zhen-xin Yi, Bao-long Kuang, Zhi-ming Xie, Yan Li, Jian-guo Zhang|2023|New J.Chem.|47|17592|doi:10.1039/D3NJ02775A
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: 10.25345/c50r9mf2r
title: MassIVE MSV000093084 - Binding partners of Annexin A2 in MDA-MB-231 cells
title: A mass-spec of proteins found bound to Annexin A2 following a immunoprecipitation of Annexin A2 from cell lysate of MDAMB231 cells plated 1) on plastic or 2) on collagen-1