cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2fc2rn
title: CCDC 2244981: Experimental Crystal Structure Determination

description: Related Article: Dumitrela Diaconu, Violeta Mangalagiu, Simona Dunca, Dorina Amariucai-Mantu, Vasilichia Antoci, Tiberiu Roman, Ionel I. Mangalagiu|2023|Heliyon|9|e21518|doi:10.1016/j.heliyon.2023.e21518
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.cc2gw5dy
title: CCDC 2290230: Experimental Crystal Structure Determination

description: Related Article: Vidal Leudja Noume, Vanessa Charnele Tsafack, Jean Francois Zeutsop, Appolinaire Kene Dongmo, Raymond Ngansop Nono, Hans-Georg Stammler, Jean Jules Kezetas Bankeu, Bruno Ndjakou Lenta, Pépin Nkeng-Efouet Alango, Norbert Sewald, Jean Rodolphe Chouna|2023|Biochemical Systematics and Ecology|111|104744|doi:10.1016/j.bse.2023.104744
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: 10.5281/zenodo.10207797
title: Modelos de maturidade em gestão de documentos, informações e dados.

description: A tabela com dados sobre modelos de maturidade em gestão de documentos, informações e dados é resultado de revisão sistemática de literatura com o objetivo de analisar pesquisas sobre modelos de maturidade aplicados na gestão de documentos, visando extrair subsídios que contribuam com a elaboração de um modelo de maturidade a ser utilizado na gestão arquivística de documentos digitais.
A coleta dos trabalhos da revisão ocorreu no dia 5 de junho de 2023 nas seguintes bases de dados científicas: BRAPCI, Emerald Insight, Google Acadêmico, Redalyc e Scopus.
Ao todo foram recuperados 700 registros e selecionadas 40 publicações para análise. Quando foram identificados 65 modelos de maturidade, separados conforme a área de domínio: gestão de documentos (21), gestão da informação (14), gestão de conteúdo (3), gestão de dados (3), governança da informação (7), governança de dados (9), e preservação digital (8).
DOI: 10.5281/zenodo.10207798
title: Modelos de maturidade em gestão de documentos, informações e dados.

description: A tabela com dados sobre modelos de maturidade em gestão de documentos, informações e dados é resultado de revisão sistemática de literatura com o objetivo de analisar pesquisas sobre modelos de maturidade aplicados na gestão de documentos, visando extrair subsídios que contribuam com a elaboração de um modelo de maturidade a ser utilizado na gestão arquivística de documentos digitais.
A coleta dos trabalhos da revisão ocorreu no dia 5 de junho de 2023 nas seguintes bases de dados científicas: BRAPCI, Emerald Insight, Google Acadêmico, Redalyc e Scopus.
Ao todo foram recuperados 700 registros e selecionadas 40 publicações para análise. Quando foram identificados 65 modelos de maturidade, separados conforme a área de domínio: gestão de documentos (21), gestão da informação (14), gestão de conteúdo (3), gestão de dados (3), governança da informação (7), governança de dados (9), e preservação digital (8).
DOI: 10.5281/zenodo.10155615
title: MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis (iPhone Part 3)

description: Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e.g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism. However, there exists a knowledge gap in how to apply geometric reconstruction and photorealism modeling (novel view synthesis) in a unified framework.
To address this gap and promote the development of robust and immersive modeling and rendering with consumer-grade devices, first, we propose a real-world Multi-Sensor Hybrid Room Dataset (MuSHRoom). Our dataset presents exciting challenges and requires state-of-the-art methods to be cost-effective, robust to noisy data and devices, and can jointly learn 3D reconstruction and novel view synthesis, instead of treating them as separate tasks, making them ideal for real-world applications. Second, we benchmark several famous pipelines on our dataset for joint 3D mesh reconstruction and novel view synthesis. Finally, in order to further improve the overall performance, we propose a new method that achieves a good trade-off between the two tasks. Our dataset and benchmark show great potential in promoting the improvements for fusing 3D reconstruction and high-quality rendering in a robust and computationally efficient end-to-end fashion.
DOI: 10.5281/zenodo.10155616
title: MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis (iPhone Part 3)

description: Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e.g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism. However, there exists a knowledge gap in how to apply geometric reconstruction and photorealism modeling (novel view synthesis) in a unified framework.
To address this gap and promote the development of robust and immersive modeling and rendering with consumer-grade devices, first, we propose a real-world Multi-Sensor Hybrid Room Dataset (MuSHRoom). Our dataset presents exciting challenges and requires state-of-the-art methods to be cost-effective, robust to noisy data and devices, and can jointly learn 3D reconstruction and novel view synthesis, instead of treating them as separate tasks, making them ideal for real-world applications. Second, we benchmark several famous pipelines on our dataset for joint 3D mesh reconstruction and novel view synthesis. Finally, in order to further improve the overall performance, we propose a new method that achieves a good trade-off between the two tasks. Our dataset and benchmark show great potential in promoting the improvements for fusing 3D reconstruction and high-quality rendering in a robust and computationally efficient end-to-end fashion.
DOI: 10.5281/zenodo.10154394
title: MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis (iPhone Part 1)

description: Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e.g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism. However, there exists a knowledge gap in how to apply geometric reconstruction and photorealism modeling (novel view synthesis) in a unified framework.
To address this gap and promote the development of robust and immersive modeling and rendering with consumer-grade devices, first, we propose a real-world Multi-Sensor Hybrid Room Dataset (MuSHRoom). Our dataset presents exciting challenges and requires state-of-the-art methods to be cost-effective, robust to noisy data and devices, and can jointly learn 3D reconstruction and novel view synthesis, instead of treating them as separate tasks, making them ideal for real-world applications. Second, we benchmark several famous pipelines on our dataset for joint 3D mesh reconstruction and novel view synthesis. Finally, in order to further improve the overall performance, we propose a new method that achieves a good trade-off between the two tasks. Our dataset and benchmark show great potential in promoting the improvements for fusing 3D reconstruction and high-quality rendering in a robust and computationally efficient end-to-end fashion.
DOI: 10.5281/zenodo.10154395
title: MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis (iPhone Part 1)

description: Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e.g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism. However, there exists a knowledge gap in how to apply geometric reconstruction and photorealism modeling (novel view synthesis) in a unified framework.
To address this gap and promote the development of robust and immersive modeling and rendering with consumer-grade devices, first, we propose a real-world Multi-Sensor Hybrid Room Dataset (MuSHRoom). Our dataset presents exciting challenges and requires state-of-the-art methods to be cost-effective, robust to noisy data and devices, and can jointly learn 3D reconstruction and novel view synthesis, instead of treating them as separate tasks, making them ideal for real-world applications. Second, we benchmark several famous pipelines on our dataset for joint 3D mesh reconstruction and novel view synthesis. Finally, in order to further improve the overall performance, we propose a new method that achieves a good trade-off between the two tasks. Our dataset and benchmark show great potential in promoting the improvements for fusing 3D reconstruction and high-quality rendering in a robust and computationally efficient end-to-end fashion.
DOI: 10.5281/zenodo.10155481
title: MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis (iPhone Part 2)

description: Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e.g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism. However, there exists a knowledge gap in how to apply geometric reconstruction and photorealism modeling (novel view synthesis) in a unified framework.
To address this gap and promote the development of robust and immersive modeling and rendering with consumer-grade devices, first, we propose a real-world Multi-Sensor Hybrid Room Dataset (MuSHRoom). Our dataset presents exciting challenges and requires state-of-the-art methods to be cost-effective, robust to noisy data and devices, and can jointly learn 3D reconstruction and novel view synthesis, instead of treating them as separate tasks, making them ideal for real-world applications. Second, we benchmark several famous pipelines on our dataset for joint 3D mesh reconstruction and novel view synthesis. Finally, in order to further improve the overall performance, we propose a new method that achieves a good trade-off between the two tasks. Our dataset and benchmark show great potential in promoting the improvements for fusing 3D reconstruction and high-quality rendering in a robust and computationally efficient end-to-end fashion.
DOI: 10.5281/zenodo.10155482
title: MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis (iPhone Part 2)

description: Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e.g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism. However, there exists a knowledge gap in how to apply geometric reconstruction and photorealism modeling (novel view synthesis) in a unified framework.
To address this gap and promote the development of robust and immersive modeling and rendering with consumer-grade devices, first, we propose a real-world Multi-Sensor Hybrid Room Dataset (MuSHRoom). Our dataset presents exciting challenges and requires state-of-the-art methods to be cost-effective, robust to noisy data and devices, and can jointly learn 3D reconstruction and novel view synthesis, instead of treating them as separate tasks, making them ideal for real-world applications. Second, we benchmark several famous pipelines on our dataset for joint 3D mesh reconstruction and novel view synthesis. Finally, in order to further improve the overall performance, we propose a new method that achieves a good trade-off between the two tasks. Our dataset and benchmark show great potential in promoting the improvements for fusing 3D reconstruction and high-quality rendering in a robust and computationally efficient end-to-end fashion.
DOI: 10.5281/zenodo.10207828
title: Finite Difference Equations Obtained - Supplementary

description: Finite Difference Equations Obtained - Supplementary
DOI: 10.5281/zenodo.10207829
title: Finite Difference Equations Obtained - Supplementary

description: Finite Difference Equations Obtained - Supplementary
DOI: 10.6084/m9.figshare.24635478.v1
title: Traditional Chinese courtyard size dataset

description: The dataset containing data on climatic variables and courtyard area used for statistical analysis in this study.
DOI: 10.6084/m9.figshare.24635478
title: Traditional Chinese courtyard size dataset

description: The dataset containing data on climatic variables and courtyard area used for statistical analysis in this study.
DOI: 10.6084/m9.figshare.24635478.v2
title: Traditional Chinese courtyard size dataset

description: The dataset containing data on climatic variables and courtyard area used for statistical analysis in this study.
DOI: 10.6084/m9.figshare.24635478.v3
title: Traditional Chinese courtyard size dataset

description: The dataset containing data on climatic variables and courtyard area used for statistical analysis in this study.
DOI: 10.5281/zenodo.10207814
title: Estimating the valence, arousal and balance of dyadic conversations using regression algorithms with autonomic nervous system responses

description: This repository contains extracted data features and all questionnaires from our study "Estimating the valence, arousal and balance of dyadic conversations using regression algorithms with autonomic nervous system responses". 
 
Data_FinalFeatureSet.xlsx contains data for the 42 dyads who completed the study protocol. Rows represent individual participants, with the two participants in the same dyad always on consecutive rows. Columns consist of:

Participant gender and age.
Group that dyads were assigned to. PosInit/NeutInit/NegInit represent positive, neutral or negative initial prompts. Devil1st/NoEmot1st represent which of the two secret prompts was presented first ("devil's advocate" or "no emotion").
A column stating which of the two participants was given the secret prompts (participant on left or right).
A column stating whether the participants had already known each other before the session (Y/N).
Extracted physiological features for 12 intervals: the first baseline (interval 1), 10 conversation intervals (intervals 2-11), and the second baseline (interval 12). Individual features are present for all individual participants while synchrony features exist for dyads (not individuals) and are thus present for only one row of a dyad.
Raw data from three personality questionnaires: the Brief Fear of Negative Evaluation Scale (BFNES), the Questionnaire of Cognitive and Affective Empathy (QCAE) and the Center for Epidemiologic Studies Depression Scale (CESD).
Self-reported results of the Self-Assessment Manikin (SAM) for the 10 conversation intervals, with the three columns in each interval corresponding to valence, arousal and balance.
Note that one dyad's physiological data were corrupted and that dyad was not used for further analysis. Their demographics and questionnaire data are included, but no physiological features were calculated.
 
Questionnaire files include the BFNES, QCAE and CESD as well as three versions of our modified SAM: one with no secret prompts, one with secret prompts for participants who saw the "devil's advocate" prompt first, and one with secret prompts for participants who saw the "no emotion" prompt first.
DOI: 10.5281/zenodo.10207815
title: Estimating the valence, arousal and balance of dyadic conversations using regression algorithms with autonomic nervous system responses

description: This repository contains extracted data features and all questionnaires from our study "Estimating the valence, arousal and balance of dyadic conversations using regression algorithms with autonomic nervous system responses". 
 
Data_FinalFeatureSet.xlsx contains data for the 42 dyads who completed the study protocol. Rows represent individual participants, with the two participants in the same dyad always on consecutive rows. Columns consist of:

Participant gender and age.
Group that dyads were assigned to. PosInit/NeutInit/NegInit represent positive, neutral or negative initial prompts. Devil1st/NoEmot1st represent which of the two secret prompts was presented first ("devil's advocate" or "no emotion").
A column stating which of the two participants was given the secret prompts (participant on left or right).
A column stating whether the participants had already known each other before the session (Y/N).
Extracted physiological features for 12 intervals: the first baseline (interval 1), 10 conversation intervals (intervals 2-11), and the second baseline (interval 12). Individual features are present for all individual participants while synchrony features exist for dyads (not individuals) and are thus present for only one row of a dyad.
Raw data from three personality questionnaires: the Brief Fear of Negative Evaluation Scale (BFNES), the Questionnaire of Cognitive and Affective Empathy (QCAE) and the Center for Epidemiologic Studies Depression Scale (CESD).
Self-reported results of the Self-Assessment Manikin (SAM) for the 10 conversation intervals, with the three columns in each interval corresponding to valence, arousal and balance.
Note that one dyad's physiological data were corrupted and that dyad was not used for further analysis. Their demographics and questionnaire data are included, but no physiological features were calculated.
 
Questionnaire files include the BFNES, QCAE and CESD as well as three versions of our modified SAM: one with no secret prompts, one with secret prompts for participants who saw the "devil's advocate" prompt first, and one with secret prompts for participants who saw the "no emotion" prompt first.
DOI: 10.5281/zenodo.10207835
title: A simple, static and stage mounted direct electron detector based electron backscatter diffraction system

description: Data set for A simple, static and stage mounted direct electron detector based electron backscatter diffraction system
T.Zhang, T. B. Britton
 
Contents
- New in v2.0.0: CAD drawings of the stage
 
- Single Si(100) diffraction patterns at 4 camera lengths, and at 4 corners of the sample
- Horizontal and vertical line scan on Si(100) with 20 grid points
- 20x20 mapping scan on a polycrystalline Cu sample
Scan parameters for the line scans and map are included in logfiles within each subfolder.
 
All pattern files are provided in .h5 format and .tif format. Analyses of the patterns were performed with AstroEBSD and MTEX.
DOI: 10.5281/zenodo.8342488
title: A simple, static and stage mounted direct electron detector based electron backscatter diffraction system

description: Data set for A simple, static and stage mounted direct electron detector based electron backscatter diffraction system
T.Zhang, T. B. Britton
 
Contents
- New in v2.0.0: CAD drawings of the stage
 
- Single Si(100) diffraction patterns at 4 camera lengths, and at 4 corners of the sample
- Horizontal and vertical line scan on Si(100) with 20 grid points
- 20x20 mapping scan on a polycrystalline Cu sample
Scan parameters for the line scans and map are included in logfiles within each subfolder.
 
All pattern files are provided in .h5 format and .tif format. Analyses of the patterns were performed with AstroEBSD and MTEX.
DOI: 10.5281/zenodo.8342489
title: A simple, static and stage mounted direct electron detector based electron backscatter diffraction system

description: This is the complimentary dataset for "A low-cost electron backscatter diffraction system based on a Timepix3 direct electron detector".
 
Contents:
- Single Si(100) diffraction patterns at 4 camera lengths, and at 4 corners of the sample
- Horizontal and vertical line scan on Si(100) with 20 grid points
- 20x20 mapping scan on a polycrystalline Cu sample
 
Scan parameters for the line scans and map are included in logfiles within each subfolder.
 
All pattern files are provided in .h5 format and .tif format. Analyses of the patterns were performed with AstroEBSD and MTEX.
DOI: 10.34740/kaggle/ds/4055804
title: Gladiator Combat Records and Profiles Dataset
DOI: 10.6084/m9.figshare.24634272.v1
title: FiCRoN, a deep learning-based algorithm for the automatic determination of intracellularparasite burden from fluorescence microscopy images

description: The FiCRoN automated method dataset provides fluorescence microscopy images from BMDM macrophages (mouse bone marrow-derived macrophages) infected with <i>Leishmania major</i>. When extracting the FiCRoN.zip file, three main folders appear: <b>Training, Validation and Test.</b> Within the first two main folders there are two secondary folders: <b>O</b><b>riginal_images</b> and another folder with <b>A</b><b>nnotation </b>to detect three cell categories, <b>Amastigotes, Infected Macrophages and Total Macrophages</b> compatible with the FiCRoN method. In the main folder <b>Test</b> there are two secondary folders, <b>O</b><b>riginal_images</b> with MOIs different and another folder with <b>Count</b> generated from the <b>manual counting</b> <b>of three experts</b> and the <b>automatic FiCRoN</b> count.<br>
DOI: 10.6084/m9.figshare.24635520
title: M. sphyraenae mitogenome annotation

description: Prediction of protein-coding genes and RNAs encoded on the Mantamonas sphyraenae mitochondrial genome.
DOI: 10.6084/m9.figshare.24635520.v1
title: M. sphyraenae mitogenome annotation

description: Prediction of protein-coding genes and RNAs encoded on the Mantamonas sphyraenae mitochondrial genome.
DOI: 10.34740/kaggle/dsv/7059868
title: consumer Argentina
DOI: 10.6084/m9.figshare.24634272
title: Dataset of fluorescence microscopy images from BMDM macrophages infected with <i>Leishmania major</i>

description: This dataset provides fluorescence microscopy images from BMDM macrophages (mouse bone marrow-derived macrophages) infected with <i>Leishmania major</i>. When extracting the Leishmania_infection.zip file, three main folders appear: <b>Training, Validation and Test.</b> Within the first two main folders there are two secondary folders: <b>O</b><b>riginal_images</b> and another folder with <b>A</b><b>nnotation </b>of the centroids of each cell, to detect three cell categories, <b>Amastigotes, Infected Macrophages and Total Macrophages</b>. In the main folder <b>Test</b> there are two secondary folders, <b>O</b><b>riginal_images</b> with MOIs different and another folder with <b>Count</b> generated from the <b>manual counting</b> <b>of three experts.</b><br>
DOI: 10.6084/m9.figshare.24634272.v2
title: Dataset of fluorescence microscopy images from BMDM macrophages infected with <i>Leishmania major</i>

description: This dataset provides fluorescence microscopy images from BMDM macrophages (mouse bone marrow-derived macrophages) infected with <i>Leishmania major</i>. When extracting the Leishmania dataset.zip file, three main folders appear: <b>Training, Validation and Test.</b> Within the first two main folders there are two secondary folders: <b>O</b><b>riginal_images</b> and another folder with <b>A</b><b>nnotation </b>to detect three cell categories, <b>Amastigotes, Infected Macrophages and Total Macrophages</b>. In the main folder <b>Test</b> there are two secondary folders, <b>O</b><b>riginal_images</b> with MOIs different and another folder with <b>Count</b> generated from the <b>manual counting</b> <b>of three experts.</b><br>
DOI: 10.34740/kaggle/dsv/7059915
title: Balochi Language to English Translation Dataset