cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14457/tu.the.2023.101
title: แนวทางการออกแบบแพลตฟอร์มทางเลือกสำหรับศิลปินวาดภาพบนกำแพง (graffiti artist)

description: งานวิจัยนี้มีวัตถุประสงค์เพื่อศึกษาและนำเสนอแนวทางการสร้างทางเลือกสำหรับศิลปินวาดภาพบนกำแพงและท้องถนน (Graffiti Artist) โดยการออกแบบพื้นที่บนแพลตฟอร์มจากพื้นฐานความต้องการและอุปสรรคในการใช้งานของศิลปินเพื่อลดผลกระทบที่ไม่ดีต่อเมืองและชุมชน งานวิจัยนี้เป็นงานวิจัยเชิงคุณภาพ (Qualitative Research) ทำการเก็บข้อมูลโดยการสัมภาษณ์เชิงลึก (In-Depth Interview) และเก็บข้อมูลจากแบบสอบถาม System Usability Scale (SUSQuestionnaire) ซึ่งกลุ่มตัวอย่างแบ่งเป็น 4 กลุ่ม ได้แก่ กลุ่มผู้ชื่นชอบงานศิลปะกราฟฟิตี้/นักท่องเที่ยว จำนวน 4 คน กลุ่มศิลปินกราฟฟิตี้ จำนวน 4 คน กลุ่มผู้เชี่ยวชาญการออกแบบแพลตฟอร์มจำนวน 3 คน และกลุ่มเจ้าของบ้านและเจ้าของกิจการ จำนวน 5 คน รวมทั้งสิ้น 16 คน ผลการวิจัยพบว่า ปัจจัยที่ส่งผลต่อการจัดการผลงานกราฟฟิตี้มากที่สุด 3 อันดับแรกได้แก่ 1) ความสะดวกแก่ผู้สนใจงานกราฟฟิตี้ 2) ประโยชน์สาหรับศิลปิน และ 3) การรักษาสภาพแวดล้อมให้ชุมชน ในส่วนของด้านธุรกิจควรคำนึงถึงการส่งเสริมแพลตฟอร์มให้เข้าถึงกลุ่มผู้ใช้งานจำนวนมาก ในส่วนของแนวทางการออกแบบแพลตฟอร์มควรคำนึงถึงความง่ายในการใช้งานเป็นหลัก และแพลตฟอร์มควรมีส่วนประกอบดังนี้ 1) Registration 2) News & Feeds 3) Search 4) Cam & Explore 5) Notification 6) Messenger 7) Profile และ 8) Report ผลการวิจัยนี้สามารถนำไปใช้เป็นแนวทางนำร่องพัฒนาต่อยอดแพลตฟอร์มทางเลือกให้ศิลปินวาดภาพบนกำแพงและท้องถนน (Graffiti Artist) ให้ได้ประโยชน์บนพื้นฐานความต้องการ จัดการอุปสรรคและผลกระทบจากการใช้งานโดยยังคำนึงถึงการควบคุมและลดผลกระทบที่ไม่ดีต่อเมืองและชุมชน
DOI: 10.14457/tu.the.2022.1533
title: อุปสรรคในการปฏิบัติตามกรอบมาตรฐาน ISO/IEC 29110 สำหรับกระบวนการพัฒนาซอฟต์แวร์ของรัฐวิสาหกิจแห่งหนึ่ง

description: การวิจัยครั้งนี้มีวัตถุประสงค์เพื่อศึกษาปัจจัยที่ก่อให้เกิดอุปสรรคในการนำมาตรฐาน ISO/IEC 29110 มาปรับใช้ในกระบวนการบริหารจัดการโครงการและการพัฒนาซอฟต์แวร์ รวมทั้งเสนอแนะแนวทางในการกำหนดเป้าหมายและนโยบายในการนำมาตรฐาน ISO/IEC 29110 มาปรับใช้ โดยทำการวิเคราะห์ช่องว่างเพื่อค้นหาปัจจัยภายในองค์กร ที่ก่อให้เกิดอุปสรรคในการนำมาตรฐาน ISO/IEC 29110 มาใช้ในกระบวนการบริหารจัดการโครงการและการพัฒนาซอฟต์แวร์ โดยรวบรวมข้อมูลจากการสัมภาษณ์เชิงลึกผู้บริหารจำนวน 4 ท่าน และการสนทนากลุ่มกับทีมนักพัฒนาซอฟต์แวร์ทีมละ 6 ท่าน จำนวน 3 ทีม รวมทั้งจัดอันดับความสำคัญของอุปสรรคที่พบ ผลการวิจัยพบว่า อุปสรรคที่สำคัญที่ทำให้การนำมาตรฐาน ISO/IEC 29110 มาปรับใช้ไม่เป็นไปตามนโยบายหรือเป้าหมายขององค์กร เกิดจากปัจจัย “ด้านนโยบายและกลยุทธ์” เป็นอันดับหนึ่ง ส่วน “ด้านบุคลากร” และ “ด้านลักษณะการทำงานของผู้บริหาร” เป็นอุปสรรคที่สำคัญที่อยู่ในอันดับที่สองร่วม ซึ่งจากผลการวิจัยทำให้ทราบถึงอุปสรรคที่แท้จริง และสามารถสร้างเป็นข้อเสนอแนะเพื่อใช้เป็นแนวทางในการปรับปรุงนโยบายขององค์กรต่อไป
DOI: 10.23729/549c4c4d-66e9-49f4-833a-71b526605f76
title: Entrapped Pores Concrete publication set

description: Publication set data used in Ahmed, H., Kuva, J., Punkki, J., 2024. Analysing entrapped pores in concrete via x-ray computed tomography: Influence of workability and compaction time. Construction and Building Materials 417. 135311. https://doi.org/10.1016/j.conbuildmat.2024.135322 Collective preview entry at https://doi.org/10.23729/137a8d02-50c5-4030-be74-2ae7621426df Preview dataset found at https://doi.org/10.23729/f5d1004c-44ed-458f-9977-6978021f29a6 . To obtain data, please contact fairdata-pas@gtk.fi .
DOI: 10.5281/zenodo.10870537
title: LUVLi Pre-trained Model

description: Introduction

Modern face alignment methods have become quite accurate at predicting the locations of facial landmarks, but they do not typically estimate the uncertainty of their predicted locations nor predict whether landmarks are visible. In this paper, we present a novel framework for jointly predicting landmark locations, associated uncertainties of these predicted locations, and landmark visibilities. We model these as mixed random variables and estimate them using a deep network trained with our proposed Location, Uncertainty, and Visibility Likelihood (LUVLi) loss. In addition, we release an entirely new labeling of a large face alignment dataset with over 19,000 face images in a full range of head poses. Each face is manually labeled with the ground-truth locations of 68 landmarks, with the additional information of whether each landmark is unoccluded, self-occluded (due to extreme head poses), or externally occluded. Not only does our joint estimation yield accurate estimates of the uncertainty of predicted landmark locations, but it also yields state-of-the-art estimates for the landmark locations themselves on multiple standard face alignment datasets. Our method’s estimates of the uncertainty of predicted landmark locations could be used to automatically identify input images on which face alignment fails, which can be critical for downstream tasks.

To foster further research into this topic, we are publicly releasing our pre-trained LUVLi models. Please see our CVPR 2020 paper titled LUVLi Face Alignment: Estimating Landmarks’ Location, Uncertainty, and Visibility Likelihood for details

At a Glance

-The size of the unzipped model is ~700MB.

-The unzipped folder contains: (i) a README.md file and (ii) pre-trained models and logs. The pre-trained models could be loaded in our publicly released LUVLi implementation.

Other Resources

Citation

If you use the LUVLi data in your research, please cite our paper:

@inproceedings{kumar2020luvli,
  title={{LUVLi} Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood},
  author={Kumar, Abhinav and Marks, Tim K. and Mou, Wenxuan and Wang, Ye and Jones, Michael and Cherian, Anoop and Koike-Akino, Toshiaki and Liu, Xiaoming and Feng, Chen},
  booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2020}
}


License

The LUVLi data is released under CC-BY-SA-4.0 license.

All data:

Created by Mitsubishi Electric Research Laboratories (MERL), 2022,2023

SPDX-License-Identifier: CC-BY-SA-4.0
DOI: 10.5281/zenodo.10870538
title: LUVLi Pre-trained Model

description: Introduction

Modern face alignment methods have become quite accurate at predicting the locations of facial landmarks, but they do not typically estimate the uncertainty of their predicted locations nor predict whether landmarks are visible. In this paper, we present a novel framework for jointly predicting landmark locations, associated uncertainties of these predicted locations, and landmark visibilities. We model these as mixed random variables and estimate them using a deep network trained with our proposed Location, Uncertainty, and Visibility Likelihood (LUVLi) loss. In addition, we release an entirely new labeling of a large face alignment dataset with over 19,000 face images in a full range of head poses. Each face is manually labeled with the ground-truth locations of 68 landmarks, with the additional information of whether each landmark is unoccluded, self-occluded (due to extreme head poses), or externally occluded. Not only does our joint estimation yield accurate estimates of the uncertainty of predicted landmark locations, but it also yields state-of-the-art estimates for the landmark locations themselves on multiple standard face alignment datasets. Our method’s estimates of the uncertainty of predicted landmark locations could be used to automatically identify input images on which face alignment fails, which can be critical for downstream tasks.

To foster further research into this topic, we are publicly releasing our pre-trained LUVLi models. Please see our CVPR 2020 paper titled LUVLi Face Alignment: Estimating Landmarks’ Location, Uncertainty, and Visibility Likelihood for details

At a Glance

-The size of the unzipped model is ~700MB.

-The unzipped folder contains: (i) a README.md file and (ii) pre-trained models and logs. The pre-trained models could be loaded in our publicly released LUVLi implementation.

Other Resources

Citation

If you use the LUVLi data in your research, please cite our paper:

@inproceedings{kumar2020luvli,
  title={{LUVLi} Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood},
  author={Kumar, Abhinav and Marks, Tim K. and Mou, Wenxuan and Wang, Ye and Jones, Michael and Cherian, Anoop and Koike-Akino, Toshiaki and Liu, Xiaoming and Feng, Chen},
  booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2020}
}


License

The LUVLi data is released under CC-BY-SA-4.0 license.

All data:

Created by Mitsubishi Electric Research Laboratories (MERL), 2022,2023

SPDX-License-Identifier: CC-BY-SA-4.0
DOI: 10.5281/zenodo.10214025
title: Simulation data for Loop-extruders alter bacterial chromosome topology to direct entropic forces for segregation

description: Polymer configuration data from simulations of loop-extrusion on a replicating bacterial chromosome, used for the manuscript "Loop-extruders alter bacterial chromosome topology to direct entropic forces for segregation". New version includes data for replication factory models and an unconfined polymer.
DOI: 10.5281/zenodo.10877077
title: Simulation data for Loop-extruders alter bacterial chromosome topology to direct entropic forces for segregation

description: Polymer configuration data from simulations of loop-extrusion on a replicating bacterial chromosome, used for the manuscript "Loop-extruders alter bacterial chromosome topology to direct entropic forces for segregation". New version includes data for more specific off-loading, as well as fewer SMCs.
DOI: 10.5281/zenodo.8105231
title: Simulation data for Loop-extruders alter bacterial chromosome topology to direct entropic forces for segregation

description: Polymer configuration data from simulations of loop-extrusion on a replicating bacterial chromosome, used for the manuscript "Loop-extruders alter bacterial chromosome topology to direct entropic forces for segregation". New version includes data for more specific off-loading, as well as fewer SMCs.
DOI: 10.5281/zenodo.8105232
title: Simulation data for Loop-extruders alter bacterial chromosome topology to direct entropic forces for segregation

description: Polymer configuration data from simulations of loop-extrusion on a replicating bacterial chromosome, used for the manuscript "Loop-extruders alter bacterial chromosome topology to direct entropic forces for segregation".
DOI: 10.6084/m9.figshare.25439026
title: database.xlsx

description: In this cross-sectional study, we determined the relative impact of long-term occupational exposure to pesticides on physical performance and perception of tiredness. Experimental data was collected in locus from agricultural communities and included surveys to assess the duration of exposure to pesticides, social status, habitual physical activity levels, presence of common mental disorders (CMD), and self-reported tiredness. Plasmatic cholinesterase (PChE), body composition and traditional functional performance tests (Handgrip strength – HGS; Time up and go – TUG; and Sit-to-stand - STS) were obtained. From the 127 individuals tested, cluster analysis yielded 80 individuals divided in Direct Exposed (n = 37) and Indirect Exposed (n = 43); Tired (n = 16), and Not Tired (n = 64). PChE values were within the reference values (5209.64 – 13943.53 U/L). Pesticide exposure had no influence on PChE levels, CMD or fatigue (p &gt; 0.05), while Self-reported tiredness had (p &lt; 0.05). Principal Component Analyses showed that HGS; STS and TUG (i.e., physical performance variables) are negatively influenced by two independent factors: pesticide exposure and self-reported tiredness. We conclude that chronic pesticide exposure and tiredness can negatively impact physical performance, independently, without clinically significant changes in PChE levels that is a biomarker used to track pesticide intoxication. Functional physical tests can be a useful tool to identify chronic pesticide exposure, and help with the limitations of commonly used parameters (i.e. PChE and CMD). Self-reported tiredness is a confounding variable.
DOI: 10.6084/m9.figshare.25439026.v1
title: database.xlsx

description: In this cross-sectional study, we determined the relative impact of long-term occupational exposure to pesticides on physical performance and perception of tiredness. Experimental data was collected in locus from agricultural communities and included surveys to assess the duration of exposure to pesticides, social status, habitual physical activity levels, presence of common mental disorders (CMD), and self-reported tiredness. Plasmatic cholinesterase (PChE), body composition and traditional functional performance tests (Handgrip strength – HGS; Time up and go – TUG; and Sit-to-stand - STS) were obtained. From the 127 individuals tested, cluster analysis yielded 80 individuals divided in Direct Exposed (n = 37) and Indirect Exposed (n = 43); Tired (n = 16), and Not Tired (n = 64). PChE values were within the reference values (5209.64 – 13943.53 U/L). Pesticide exposure had no influence on PChE levels, CMD or fatigue (p &gt; 0.05), while Self-reported tiredness had (p &lt; 0.05). Principal Component Analyses showed that HGS; STS and TUG (i.e., physical performance variables) are negatively influenced by two independent factors: pesticide exposure and self-reported tiredness. We conclude that chronic pesticide exposure and tiredness can negatively impact physical performance, independently, without clinically significant changes in PChE levels that is a biomarker used to track pesticide intoxication. Functional physical tests can be a useful tool to identify chronic pesticide exposure, and help with the limitations of commonly used parameters (i.e. PChE and CMD). Self-reported tiredness is a confounding variable.
DOI: 10.5281/zenodo.10783042
title: PyCOMPSs Probabilistic Tsunami Forecast (PTF) - Kos-Bodrum 2017 earthquake and tsunami test-case results
DOI: 10.5281/zenodo.10783041
title: PyCOMPSs Probabilistic Tsunami Forecast (PTF) - Kos-Bodrum 2017 earthquake and tsunami test-case results
DOI: 10.5281/zenodo.10783182
title: PyCOMPSs Probabilistic Tsunami Forecast (PTF) - Boumerdes-2003 earthquake and tsunami test-case results
DOI: 10.5281/zenodo.10783183
title: PyCOMPSs Probabilistic Tsunami Forecast (PTF) - Boumerdes-2003 earthquake and tsunami test-case results
DOI: 10.6084/m9.figshare.25480273
title: Supplementary Material for: A short history of B cell HLA epitopes

description: Background: HLA epitopes are currently in the focus of transplantation immunogenetics. The main reason is the complexity of the HLA System with the &gt;38,000 alleles [1] the number of which increases steadily. These alleles are determined by the current state of the art typing methods like second and third generation sequencing. Screening for HLA antibodies is hampered by the lack of specific target beads with all possible alleles described. 
Summary: A way to circumvent the problem is to define HLA epitopes. The number of antibody-confirmed epitopes, on the other hand, found to be 72 for HLA class I and 74 for HLA-class II. Here, we elaborate on the current knowledge on these HLA epitopes. Absolute definitions of these structures are not yet available. 
Key messages: Making use of eplets is a comparable way allowing statistical analyses. However, one should keep in mind, that the results obtained are approximative, or perhaps better associative. Continuous collaboration needed for the full understanding of the HLA epitopes. The reactivity towards epitopes remains patient specific.
DOI: 10.6084/m9.figshare.22786203
title: Dislocation array reflection enhances strain hardening of a dual-phase heterostructured high-entropy alloy

description: Piling-ups of geometrically necessary dislocation (GND) arrays against interfaces are known to produce hetero-deformation induced (HDI) strengthening and strain hardening to enhance the strength and ductility of heterostructured materials. Here we report an interesting dislocation mechanism that can produce strong HDI hardening: consecutive reflections of GND planar piling-up arrays near the opposite phase boundaries in a heterostructured AlCoCrFeNi<sub>2</sub> high entropy alloy (HEA). In contrast, dislocation transmission was found at grain boundaries in the fcc phase. The discovery here provides guidance for future materials design, which may improve the combination of strength and ductility of metallic materials. A hitherto unknown consecutive reflection of GND arrays near phase boundaries of a heterostructured HEA is observed and proposed as a key contributor to HDI hardening.
DOI: 10.6084/m9.figshare.23256265
title: Accelerated exploration of high-performance multi-principal element alloys: data-driven high-throughput calculations and active learning method

description: We propose an active learning guided density functional theory calculation framework for rapid screening of multi-principal element alloys (MPEAs) with superior mechanical properties. Using this framework, we fast construct the datasets of the bulk modulus (<i>B</i>), shear modulus (<i>G</i>), yield strength, and Pugh’s ratio of 12,698 noble metal MPEAs. These datasets were obtained with density functional theory prediction accuracy (<i>R</i><sup>2 </sup>= 0.98 and 0.96 for <i>B</i> and <i>G</i>, respectively) based on active learning guided 120 DFT calculated data. Analysis of the dataset shows that Ni and Au would enhance the yield strength and the toughness of these noble MPEAs, respectively. An active learning guided density functional theory calculation framework, which reduces computation time by three orders of magnitude, was proposed for rapidly screening multi-principal element alloys with superior mechanical properties.
DOI: 10.6084/m9.figshare.23256265.v1
title: Accelerated exploration of high-performance multi-principal element alloys: data-driven high-throughput calculations and active learning method

description: We propose an active learning guided density functional theory calculation framework for rapid screening of multi-principal element alloys (MPEAs) with superior mechanical properties. Using this framework, we fast construct the datasets of the bulk modulus (<i>B</i>), shear modulus (<i>G</i>), yield strength, and Pugh’s ratio of 12,698 noble metal MPEAs. These datasets were obtained with density functional theory prediction accuracy (<i>R</i><sup>2 </sup>= 0.98 and 0.96 for <i>B</i> and <i>G</i>, respectively) based on active learning guided 120 DFT calculated data. Analysis of the dataset shows that Ni and Au would enhance the yield strength and the toughness of these noble MPEAs, respectively. An active learning guided density functional theory calculation framework, which reduces computation time by three orders of magnitude, was proposed for rapidly screening multi-principal element alloys with superior mechanical properties.
DOI: 10.34740/kaggle/ds/4634681
title: Devin AI Release Video Comments
DOI: 10.5517/ccdc.csd.cc237d2v
title: CCDC 1943516: Experimental Crystal Structure Determination

description: Related Article: J. Moussa, F. Juliá, L. M. Chamoreau, P. González-Herrero|2024|New J.Chem.|48|5577|doi:10.1039/D3NJ05209E
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.cc27l3kj
title: CCDC 2072987: Experimental Crystal Structure Determination

description: Related Article: Yao Liu, Ruiqi Wang, Qinheng Zheng, Xiangguo Hu, Fang Liu, Nan Yan|2024|Org.Chem.Front.|11|2047|doi:10.1039/D3QO01259J
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.cc27pxtp
title: CCDC 2076653: Experimental Crystal Structure Determination

description: Related Article: Yao Liu, Ruiqi Wang, Qinheng Zheng, Xiangguo Hu, Fang Liu, Nan Yan|2024|Org.Chem.Front.|11|2047|doi:10.1039/D3QO01259J
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.cc2c3f97
title: CCDC 2178038: Experimental Crystal Structure Determination

description: Related Article: Ana Lozančić, Sanja Burazer, Sanja Renka, Krešimir Molčanov, Lidija Molčanov, Marijana Jurić|2024|CrystEngComm|26|1892|doi:10.1039/D3CE01267K
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.cc2c3fb8
title: CCDC 2178039: Experimental Crystal Structure Determination

description: Related Article: Ana Lozančić, Sanja Burazer, Sanja Renka, Krešimir Molčanov, Lidija Molčanov, Marijana Jurić|2024|CrystEngComm|26|1892|doi:10.1039/D3CE01267K
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.cc2cklvd
title: CCDC 2191664: Experimental Crystal Structure Determination

description: Related Article: Kirandeep, Jaspreet Kaur, Indu Sharma, Ennio Zangrando, Kaushik Pal, Surinder Kumar Mehta, Ramesh Kataria|2023|J.Mater.Res.Tech|22|278|doi:10.1016/j.jmrt.2022.10.167
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.cc2d9cfj
title: CCDC 2213537: Experimental Crystal Structure Determination

description: Related Article: Wei Feng, Xiaoying Wu, Ran Song, Daoshan Yang, Wen Si, Jian Lv|2024|Org.Chem.Front.|11|2015|doi:10.1039/D3QO01766D
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.cc2ddwwl
title: CCDC 2216930: Experimental Crystal Structure Determination

description: Related Article: Ying Zhao, Dan Wu, Yidan Qiao, Guo-Ping Yang, Lu-Fang Ma, Yao-Yu Wang|2024|Inorg.Chem.Front.|11|2071|doi:10.1039/D3QI02527F
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.