cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/zr56-jb08
title: Electrical Input Filters of Ganglion Cells in Wild Type and Degenerating rd10 Mouse Retina as a Template for Selective Electrical Stimulation

description: Bionic vision systems are currently limited by indiscriminate activation of all retinal ganglion cells (RGCs) – despite the dozens of known RGC types which each encode a different visual message. Here, we use spike-triggered averaging to explore how electrical responsiveness varies across RGC types toward the goal of using this variation to create type-selective electrical stimuli. A battery of visual stimuli and a randomly distributed sequence of electrical pulses were delivered to healthy and degenerating (4-week-old rd10) mouse retinas. Ganglion cell spike trains were recorded during stimulation using a 60-channel microelectrode array. Hierarchical clustering divided the recorded RGC populations according to their visual and electrical response patterns. Novel electrical stimuli were presented to assess type-specific selectivity. In healthy retinas, responses fell into 35 visual patterns and 14 electrical patterns. In degenerating retinas, responses fell into 12 visual and 23 electrical patterns. Few correspondences between electrical and visual response patterns were found except for the known correspondence of ON visual type with upward deflecting electrical type and OFF cells with downward electrical profiles. Further refinement of the approach presented here may yet yield the elusive nuances necessary for type-selective stimulation. This study greatly deepens our understanding of electrical input filters in the context of detailed visual response characterization and includes the most complete examination yet of degenerating electrical input filters.
DOI: 10.5281/zenodo.8042906
title: Conformational changes in the essential E. coli septal cell wall synthesis complex suggest an activation mechanism

description: Molecular dynamics trajectories and data analysis notebooks to accompany the manuscript "Conformational changes in the essential E. coli septal cell wall synthesis complex suggest an activation mechanism" This dataset consists of a single zip archive. The archives contains two folders: one for molecular dynamics trajectories and one for data analysis notebooks. Each folder contains a README file describing files within.
DOI: 10.5281/zenodo.8042905
title: Conformational changes in the essential E. coli septal cell wall synthesis complex suggest an activation mechanism

description: Molecular dynamics trajectories and data analysis notebooks to accompany the manuscript "Conformational changes in the essential E. coli septal cell wall synthesis complex suggest an activation mechanism" This dataset consists of a single zip archive. The archives contains two folders: one for molecular dynamics trajectories and one for data analysis notebooks. Each folder contains a README file describing files within.
DOI: 10.6084/m9.figshare.23524584
title: DFT data for Figures

description: Density functional calculation data for generating the Figures used in <strong>https://doi.org/10.1002/adma.202211966</strong>
DOI: 10.6084/m9.figshare.23524584.v1
title: DFT data for generating Figures of <strong>https://doi.org/10.1002/adma.202211966</strong>

description: Density functional calculation data for generating the Figures used in <strong>https://doi.org/10.1002/adma.202211966</strong>
DOI: 10.5517/ccdc.csd.cc2ckjfx
title: CCDC 2191589: Experimental Crystal Structure Determination

description: Related Article: Diana Cebotari, Jordi Buils, Olga Garbuz, Greta Balan, Jérôme Marrot, Vincent Guérineau, David Touboul, Mohamed Haouas, Mireia Segado-Centelles, Carles Bo, Aurelian Gulea, Sébastien Floquet|2023|J.Inorg.Biochem.|245|112258|doi:10.1016/j.jinorgbio.2023.112258
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.cc2d2hll
title: CCDC 2206939: Experimental Crystal Structure Determination

description: Related Article: Wei Liu, Zexin Wang, Nannan Chen, Renfu Li, Tong Wu, Jingming Zhou, Yuqiu Ye, Yongbo Wei, Yanbing Ke, Hong Jiang, Lijun Xie|2023|J.Photochem.Photobiol.A:Chem.|443|114868|doi:10.1016/j.jphotochem.2023.114868
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.cc138db3
title: CCDC 1020964: Experimental Crystal Structure Determination

description: Related Article: Sangita Das, Partha Pratim Das, James W. Walton, Ching Kheng Quah, Kakali Ghosal, Maitree Bhattacharyya|2023|Dyes Pigm.|217|111413|doi:10.1016/j.dyepig.2023.111413
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.cc2fjmwh
title: CCDC 2250317: Experimental Crystal Structure Determination

description: Related Article: Patricia Fernandez de Larrinoa, Jordan Parmentier, Antoine Kichler, Thierry Achard, Monique Dontenwill, Christel Herold-Mende, Sylvie Fournel, Benoît Frisch, Béatrice Heurtault, Stéphane Bellemin-Laponnaz|2023|Int.J.Pharm.|641|123071|doi:10.1016/j.ijpharm.2023.123071
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.cc2fjmxj
title: CCDC 2250318: Experimental Crystal Structure Determination

description: Related Article: Patricia Fernandez de Larrinoa, Jordan Parmentier, Antoine Kichler, Thierry Achard, Monique Dontenwill, Christel Herold-Mende, Sylvie Fournel, Benoît Frisch, Béatrice Heurtault, Stéphane Bellemin-Laponnaz|2023|Int.J.Pharm.|641|123071|doi:10.1016/j.ijpharm.2023.123071
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.cc2fjmyk
title: CCDC 2250319: Experimental Crystal Structure Determination

description: Related Article: Patricia Fernandez de Larrinoa, Jordan Parmentier, Antoine Kichler, Thierry Achard, Monique Dontenwill, Christel Herold-Mende, Sylvie Fournel, Benoît Frisch, Béatrice Heurtault, Stéphane Bellemin-Laponnaz|2023|Int.J.Pharm.|641|123071|doi:10.1016/j.ijpharm.2023.123071
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.cc2fpnb4
title: CCDC 2255136: Experimental Crystal Structure Determination

description: Related Article: Yuan-Ji Yan, Ya-Ting La, Xun Li, Yang Zhang, Wen-Kui Dong, Yu-Jie Ding|2023|J.Mol.Struct.|1288|135843|doi:10.1016/j.molstruc.2023.135843
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.cc2fpnc5
title: CCDC 2255137: Experimental Crystal Structure Determination

description: Related Article: Yuan-Ji Yan, Ya-Ting La, Xun Li, Yang Zhang, Wen-Kui Dong, Yu-Jie Ding|2023|J.Mol.Struct.|1288|135843|doi:10.1016/j.molstruc.2023.135843
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/c5474723x
title: MassIVE MSV000092177 - Zhao_DNADamageTurboID_P36_5600
title: This dataset consists of 12 raw MS files and associated peak lists and results files, acquired on AB Sciex TripleTOF 5600 mass spectrometer operated in Data Dependent Acquisition mode. Samples were generated by Yichao Zhao. Immunoprecipitation and mass spectrometry acquisition was performed by Zhen-Yuan Lin. Analysis was performed by Yichao Zhao, Zhen-Yuan Lin, Anne-Claude Gingras and Daniel Durocher. The files are associated with a manuscript submitted for publication by Yichao Zhao et al. The main goal of this paper was to identify genes that suppress DNA damage in human cells. This dataset comprehensively maps the interactome of GNB1L using proximity labeling coupled to affinity purification. Daniel Durocher is the corresponding author of the manuscript (durocher@lunenfeld.ca); Anne-Claude Gingras should be contacted for questions on this dataset (gingras@lunenfeld.ca) This submission is associated with 3 Supplementary Files (in addition to this README file) Table 1 describes the composition of this dataset Table 2 lists all the peptide identification evidence (as per iProphet) Table 3 lists the SAINTexpress interactions
DOI: 10.5281/zenodo.8044437
title: Lactobacillus johnsonii N6.2 total lipid and fractionated lipids profiling by qualitative lipidomic LC-MS/MS analysis

description: Table presents both MS-1 (precursor mass-matched) and MS-2 (precursor mass- and spectral-matched) annotations. The values in the table represent the peak area. The column labels, RT: retention time; m/z: mass-to-charge ratio; SL (simple lipids), GL (glycolipids), and PL (phospholipids) represent lipid fractions and TL: total lipids. Table is provided as an xlsx file.
DOI: 10.5281/zenodo.8044436
title: Lactobacillus johnsonii N6.2 total lipid and fractionated lipids profiling by qualitative lipidomic LC-MS/MS analysis

description: Table presents both MS-1 (precursor mass-matched) and MS-2 (precursor mass- and spectral-matched) annotations. The values in the table represent the peak area. The column labels, RT: retention time; m/z: mass-to-charge ratio; SL (simple lipids), GL (glycolipids), and PL (phospholipids) represent lipid fractions and TL: total lipids. Table is provided as an xlsx file.
DOI: 10.5281/zenodo.8044444
title: List of significantly differentially expressed genes (p.ajust &lt; 0.01, |log2FC| &gt; 0.3).

description: DEGs were obtained by pair-wise differential expression analysis of <em>Lactobacillus johnsonii </em>N6.2 total lipid stimulation vs. the vehicle control. Table is provided as an xlsx file.
DOI: 10.5281/zenodo.8044443
title: List of significantly differentially expressed genes (p.ajust &lt; 0.01, |log2FC| &gt; 0.3).

description: DEGs were obtained by pair-wise differential expression analysis of <em>Lactobacillus johnsonii </em>N6.2 total lipid stimulation vs. the vehicle control. Table is provided as an xlsx file.
DOI: 10.25345/c50g3h808
title: MassIVE MSV000092178 - Quantifying Cell Heterogeneity and Subpopulations Using Single Cell Metabolomics
title: We combined the Single-probe single cell MS(SCMS) experimental technique with a bioinformatics software package, SinCHet-MS (Single Cell Heterogeneity for Mass Spectrometry), to characterize changes of tumor heterogeneity, quantify cell subpopulations, and prioritize the metabolite biomarkers of each subpopulation.
DOI: 10.5281/zenodo.8040753
title: YouTube8M-MusicTextClips

description: <strong>YouTube8M-MusicTextClips Dataset</strong> This page includes the YouTube8M-MusicTextClips dataset from our CVPR 2023 paper: Language-Guided Music Recommendation for Video via Prompt Analogies<br> Daniel McKee<sup>1</sup>, Justin Salamon<sup>2</sup>, Josef Sivic<sup>2,3</sup>, Bryan Russell<sup>2</sup><br> <sup>1</sup>University of Illinois at Urbana-Champaign, <sup>2</sup>Adobe Research, <sup>3</sup>Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University The dataset is licensed under a Research-only, non-commercial Adobe Research License. Please see our attached LICENSE file for more information. <strong>Dataset Description</strong> The YouTube8M-MusicTextClips dataset consists of over 4k high-quality human text descriptions of music found in video clips from the YouTube8M dataset. For each selected YouTube music video, we extracted 10 second clips at the middle of the video for annotation. We provided annotators with only the <em>audio</em> corresponding to this clip. Thus, text annotations describe audio alone, not the visual content of the clip. The dataset annotations are divided into train and test split files. As the dataset is meant mainly for evaluation, there are 3169 annotated clips from the test set and only 1000 annotated clips from the train set. Each file contains the following information for each sample: video_id: The YouTube ID corresponding to the video containing an annotated clip start: Start time (in seconds) of the annotated clip in the video end: End time (in seconds) of the annotated clip in the video text: The text annotation describing the music from the annotated clip For more information, please check our project page and paper: https://www.danielbmckee.com/language-guided-music-for-video/ <strong>Citation</strong> If you use this dataset, please cite our paper: McKee, D., Salamon, J., Sivic, J., & Russell, B. (2023). Language-Guided Music Recommendation for Video via Prompt Analogies. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023). Bibtex: <code><code>@InProceedings{mckee2023language, author = {McKee, Daniel and Salamon, Justin and Sivic, Josef and Russell, Bryan}, title = {Language-Guided Music Recommendation for Video via Prompt Analogies}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2023}, }</code></code>
DOI: 10.5281/zenodo.8040754
title: YouTube8M-MusicTextClips

description: <strong>YouTube8M-MusicTextClips Dataset</strong> This page includes the YouTube8M-MusicTextClips dataset from our CVPR 2023 paper: Language-Guided Music Recommendation for Video via Prompt Analogies<br> Daniel McKee<sup>1</sup>, Justin Salamon<sup>2</sup>, Josef Sivic<sup>2,3</sup>, Bryan Russell<sup>2</sup><br> <sup>1</sup>University of Illinois at Urbana-Champaign, <sup>2</sup>Adobe Research, <sup>3</sup>Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University The dataset is licensed under a Research-only, non-commercial Adobe Research License. Please see our attached LICENSE file for more information. <strong>Dataset Description</strong> The YouTube8M-MusicTextClips dataset consists of over 4k high-quality human text descriptions of music found in video clips from the YouTube8M dataset. For each selected YouTube music video, we extracted 10 second clips at the middle of the video for annotation. We provided annotators with only the <em>audio</em> corresponding to this clip. Thus, text annotations describe audio alone, not the visual content of the clip. The dataset annotations are divided into train and test split files. As the dataset is meant mainly for evaluation, there are 3169 annotated clips from the test set and only 1000 annotated clips from the train set. Each file contains the following information for each sample: video_id: The YouTube ID corresponding to the video containing an annotated clip start: Start time (in seconds) of the annotated clip in the video end: End time (in seconds) of the annotated clip in the video text: The text annotation describing the music from the annotated clip For more information, please check our project page and paper: https://www.danielbmckee.com/language-guided-music-for-video/ <strong>Citation</strong> If you use this dataset, please cite our paper: McKee, D., Salamon, J., Sivic, J., & Russell, B. (2023). Language-Guided Music Recommendation for Video via Prompt Analogies. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023). Bibtex: <code><code>@InProceedings{mckee2023language, author = {McKee, Daniel and Salamon, Justin and Sivic, Josef and Russell, Bryan}, title = {Language-Guided Music Recommendation for Video via Prompt Analogies}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2023}, }</code></code>
DOI: 10.6084/m9.figshare.23519106
title: Infografico_DescricaoArquivistica_20230508_assinado.pdf

description: Os gráficos apresentados foram gerados a partir de pesquisas em repositórios, extraídos de artigos digitais disponíveis, e que possuíam assuntos de tema “Descrição Arquivística”. As palavras-chave e autores que dialogam entre si, demonstram através destes gráficos a fluidez e precisão quando da recuperação de informações, principalmente nos acervos permanentes, onde a descrição é elemento arquivístico imprescindível.
DOI: 10.6084/m9.figshare.23519106.v1
title: Infografico_DescricaoArquivistica_20230508_assinado.pdf

description: Os gráficos apresentados foram gerados a partir de pesquisas em repositórios, extraídos de artigos digitais disponíveis, e que possuíam assuntos de tema “Descrição Arquivística”. As palavras-chave e autores que dialogam entre si, demonstram através destes gráficos a fluidez e precisão quando da recuperação de informações, principalmente nos acervos permanentes, onde a descrição é elemento arquivístico imprescindível.
DOI: 10.5066/p9ujfbx8
title: Soil surface elevation change data from rod surface elevation tables (rSET) from mangrove forests at Ding Darling National Wildlife Refuge, Sanibel Island, Florida (2018-2022)

description: This study monitored soil surface elevation change from mangrove forests fertilized with nitrogen and phosphorus from 2018-2021. The mangroves selected at Ding Darling National Wildlife Refuge (NWR) have been previously exposed to high nutrient loading from agricultural discharge into the Caloosahatchee River, which elevated soil phosphorus levels to 3-4 times ambient before treatments were impose. Sea-level rise vulnerability with additional nitrogen and phosphorus is a concern for these mangrove ecosystems.
DOI: 10.5281/zenodo.7826696
title: L1BSR dataset

description: Training dataset used in the paper L1BSR: Exploiting Detector Overlap for Self-Supervised Single-Image Super-Resolution of Sentinel-2 L1B Imagery. The Sentinel-2 satellite carries 12 CMOS detectors for the VNIR bands, with adjacent detectors having overlapping fields of view that result in overlapping regions in level-1 B (L1B) images. This dataset includes 3740 pairs of overlapping image crops extracted from two L1B products. Each crop has a height of around 400 pixels and a variable width that depends on the overlap width between detectors for RGBN bands, typically around 120-200 pixels. In addition to detector parallax, there is also cross-band parallax for each detector, resulting in shifts between bands. Pre-registration is performed for both cross-band and cross-detector parallax, with a precision of up to a few pixels (typically less than 10 pixels). Data notice: contains modified Copernicus Sentinel data 2023.
DOI: 10.5281/zenodo.7826695
title: L1BSR dataset

description: Training dataset used in the paper L1BSR: Exploiting Detector Overlap for Self-Supervised Single-Image Super-Resolution of Sentinel-2 L1B Imagery. The Sentinel-2 satellite carries 12 CMOS detectors for the VNIR bands, with adjacent detectors having overlapping fields of view that result in overlapping regions in level-1 B (L1B) images. This dataset includes 3740 pairs of overlapping image crops extracted from two L1B products. Each crop has a height of around 400 pixels and a variable width that depends on the overlap width between detectors for RGBN bands, typically around 120-200 pixels. In addition to detector parallax, there is also cross-band parallax for each detector, resulting in shifts between bands. Pre-registration is performed for both cross-band and cross-detector parallax, with a precision of up to a few pixels (typically less than 10 pixels). Data notice: contains modified Copernicus Sentinel data 2023.
DOI: 10.3886/e192204
title: Causal Stories AERA Open Paper

description: The data from this study are from the first year of a longitudinal study of Michigan's Partnership Policy for Turnaround, which began in 2018. The interview protocols attempted to probe details about policy implementation as well as leaders' and policymakers' beliefs about the causes and solutions to turnaround in identified districts/schools.
description: Purposive Sampling
description: face-to-face interview; telephone interview;
DOI: 10.3886/e192204v1
title: Causal Stories AERA Open Paper

description: The data from this study are from the first year of a longitudinal study of Michigan's Partnership Policy for Turnaround, which began in 2018. The interview protocols attempted to probe details about policy implementation as well as leaders' and policymakers' beliefs about the causes and solutions to turnaround in identified districts/schools.
description: Purposive Sampling
description: face-to-face interview; telephone interview;
DOI: 10.6084/m9.figshare.22728644
title: Supplementary files for optimizers manuscript

description: Performance figures for all genes in Vogelstein et al. 2013 driver gene set. Included zip files: <br> coef_weights.zip: sum of coefficient weight magnitudes for each regularization parameter coefs_count.zip: number of nonzero coefficients (model sparsity) for each regularization parameter loss_curves.zip: loss function (log loss/L1 penalty/total loss) for each regularization parameter parameter_vs_perf.zip: mutation status classification performance for each regularization parameter
DOI: 10.6084/m9.figshare.22728644.v2
title: Supplementary files for optimizers manuscript

description: Performance figures for all genes in Vogelstein et al. 2013 driver gene set. Included zip files: <br> coef_weights.zip: sum of coefficient weight magnitudes for each regularization parameter coefs_count.zip: number of nonzero coefficients (model sparsity) for each regularization parameter loss_curves.zip: loss function (log loss/L1 penalty/total loss) for each regularization parameter parameter_vs_perf.zip: mutation status classification performance for each regularization parameter
DOI: 10.6084/m9.figshare.22728644.v1
title: Supplementary files for optimizers manuscript

description: Performance figures for all genes in Vogelstein et al. 2013 driver gene set.
DOI: 10.5281/zenodo.8044505
title: Exploring Machine Learning-Based Methods for anomalies detection: Evidence from cryptocurrencies returns

description: The data consists of 4500 observation for each of the 6 cryptocurrencies.
DOI: 10.5281/zenodo.8044504
title: Exploring Machine Learning-Based Methods for anomalies detection: Evidence from cryptocurrencies returns

description: The data consists of 4500 observation for each of the 6 cryptocurrencies.