cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14457/tu.the.2022.426
title: การศึกษาความเป็นไปได้ในการพัฒนาที่ดิน ตำบลอ่าวนาง จังหวัดกระบี่

description: ในการศึกษาความเป็นไปได้ในการพัฒนาโครงการครั้งนี้ ผู้จัดทำมีจุดประสงค์เพื่อศึกษาศักยภาพของที่ดินที่ได้ทำการศึกษา โดยมีที่ตั้งอยู่ในซอยอ่าวนาง 11 ตำบลอ่าวนาง อำเภอเมือง จังหวัดกระบี่ ประกอบด้วยโฉนดที่ดินทั้งสิ้น 8 แปลงติดต่อกัน รวมเนื้อที่ 1 ไร่ 97.6 ตารางวา หรือ 497.6 ตารางวา ว่าสามารถใช้ประโยชน์ด้านใดได้บ้าง (Site Looking for use) โดยใช้แนวคิดการพัฒนาให้เกิดประโยชน์สูงสุดและดีที่สุด (Highest and best use) โดยมีขั้นตอนการศึกษากลั่นกรองความคิดในการพัฒนาในด้านต่างๆ ทั้งด้านกฎหมาย ด้านกายภาพ ด้านการตลาด และด้านการเงินซึ่งจากผลการวิเคราะห์พบว่า ที่ดินที่ทำการศึกษานั้นมีความเป็นไปได้ในการพัฒนาเป็นโครงการประเภทโรงแรม เนื่องจากมีความต้องการของตลาดและความคุ้มค่าในการลงทุน โดยโรงแรม Eleven Aonang มีลักษณะของโครงการเป็นโรงแรมระดับ 3 ดาว ประกอบด้วยห้องพักทั้งหมด 100 ห้อง โดยโครงการประกอบไปด้วย 1 อาคาร 5 ชั้น ขนาด 6,250 ตารางเมตร โดยในโรงแรมจะประกอบไปด้วย โถงต้อนรับ ห้องรับแขก ห้องสัมมนา ร้านอาคาร สระว่ายน้ำ และห้องพัก 2 แบบ โดยห้องพักแบบ Standard Type ขนาด 24.5 ตารางเมตร คิดเป็นสัดส่วนร้อยละ 70 และห้องพักแบบ Superior Type ขนาด 31.5 ตารางเมตร คิดเป็นสัดส่วนร้อยละ 30 ของจำนวนห้องทั้งหมดกลุ่มลูกค้าเป้าหมายหลักของโรงแรมคือ กลุ่มลูกค้าที่เดินทางมาท่องเที่ยวทั้งชาวไทยและชาวต่างชาติ เป็นกลุ่มลูกค้าที่ต้องการความสะดวกและที่พักสบาย ชอบการบริการที่ดี โดยมีกำลังซื้อในระดับปานกลาง และกลุ่มเป้าหมายรองเป็นกลุ่มลูกค้าที่มาสัมมนา หรือจัดงานประชุมขนาดใหญ่ โดยอาจจะต้องการพื้นที่ทำกิจกรรมภายในโรงแรม แต่ยังคงคำนึงถึงเรื่องความคุ้มค่าของราคา ซึ่งส่วนใหญ่ผู้ที่เข้าพักสัมมนาจะมีบริษัทเป็นผู้ออกค่าใช้จ่ายให้ ดังนั้นลูกค้ากลุ่มนี้จะเน้นเรื่องกายภาพของโรงแรมเป็นหลักทั้งนี้แหล่งเงินทุนของโครงการจะมี 2 แหล่งคือกู้ยืมจากธนาคารร้อยละ 60 และเงินทุนเจ้าของโครงการร้อยละ 40 โดยผู้จัดทำได้วิเคราะห์ความเป็นไปได้ทางการลงทุนผ่านเครื่องทางการเงินพบว่า โครงการมีมูลค่าปัจจุบันสุทธิ (NPV) ที่ 162,760,131.86 บาท มีอัตราผลตอบแทนภายใน (IRR) ร้อยละ 18.80 นอกจากนี้ยังทำการตั้งสมมติฐานเพื่อวิเคราะห์สถานการณ์ทางการเงินและความอ่อนไหวของโครงการที่อาจเกิดขึ้น ตามปัจจัยต่างๆ ที่ควบคุมไม่ได้ จากการศึกษาและวิเคราะห์ความเป็นไปได้ในด้านต่างๆ จึงสามารถสรุปได้ว่า โครงการมีความคุ้นค่าในการลงทุน
DOI: 10.6084/m9.figshare.20299275
title: Pull Requests Acceptance Across Progamming Languages

description: Replication package for the paper O. Kuhejda and B. Rossi, <em>"Pull Requests Acceptance: A Study Across Programming Languages" </em>accepted at 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA'23). Content <strong>Projects.zip</strong>: JSON files containing the data mined from GitHub and GHTorrent for the analysis; <strong>Scripts.zip</strong>: source code used for the data mining process, running of linters and classification analysis (Python/R). For instructions and pre-requisites, please refer to READMEs: <strong>scripts/README.org</strong> and <strong>scripts/git-contrast/README.org</strong>. Please note that the script <strong>pr_classification.py</strong> is a modified version of the file created by Lenarduzzi et al. under the CC BY 4.0 license. The original file is available at <sub>https://figshare.com/s/d47b6f238b5c92430dd7?file=14949029</sub> <strong>*_projects.png</strong>: tables about the descriptive statistics of the projects analyzed;
DOI: 10.6084/m9.figshare.20721424.v2
title: Solution of Practical Vehicle Routing Problem for Monitoring Water Distribution Networks

description: Public repository that makes available the randomly created VRPWDN instances and republishes the ADVRP benchmark instances proposed by Almoustafa et al. (2013).
DOI: 10.11583/dtu.21316608.v2
title: Ready-To-Train AI4Arctic Sea Ice Challenge Dataset

description: The AI4Arctic Sea Ice Challenge Datasets are produced for the AI4EO sea ice competition initiated by the European Space Agency (ESA) ɸ-lab. The purpose of the competition is to develop deep learning models to automatically produce sea ice charts including sea ice concentration, stage-of-development and floe size (form) information. The training datasets contain Sentinel-1 active microwave Synthetic Aperture Radar (SAR) data and corresponding passive MicroWave Radiometer (MWR) data from the AMSR2 satellite sensor. While SAR data has ambiguities between open water and sea ice, it has a high spatial resolution, whereas MWR data has good contrast between open water and ice. However, the coarse resolution of the AMSR2 MWR observations introduces a new set of obstacles, e.g. land spill-over, which can lead to erroneous sea ice predictions along the coastline adjacent to open water. Label data in the challenge datasets are ice charts, that have been produced by the Greenland ice service at the Danish Meteorological Institute (DMI) and the Canadian Ice Service (CIS) for the safety of navigation. The challenge datasets also contain other auxiliary data such as the distance to land and numerical weather prediction model data. The scenes are from the time period from January 8 2018 to December 21 2021. Two versions of the dataset exist, the '<em>raw'</em> and '<em>ready-to-train'-</em>versions with corresponding test datasets<em>. </em>The datasets each consist of the same 513 training and 20 test (without label data) scenes. The ‘<em>ready-to-train’</em>-version has been further prepared for model training, such as downsampled data from 40 to 80 m pixel spacing, standard scaled, converted ice charts (sea ice concentration, stage of development and floe size), removal of nan values, mask alignment etc. This is the Ready-To-Train version. Further details are described in the common manual that is published together with the datasets; “AI4Arctic_challenge-dataset-manual”. Code with a get-started toolkit for the '<em>ready-to-train</em>' dataset: https://github.com/astokholm/AI4ArcticSeaIceChallenge A quick challenge video overview of the challenge is available at: https://youtu.be/iuXIeLPyKfg This item is part of the Collection https://doi.org/10.11583/DTU.c.6244065 Version 2 has 20 additional scenes and has been reprocessed to accommodate the updated mean and STandard Deviation (std). Furthermore, SOD and FLOE variables have been slightly altered from version 1, as the dominant ice code threshold was incorrectly set to 70% and 50%, respectively, instead of the 65%, which was otherwise specified in the dataset manual.
DOI: 10.11583/dtu.21316608
title: Ready-To-Train AI4Arctic Sea Ice Challenge Dataset

description: The AI4Arctic Sea Ice Challenge Datasets are produced for the AI4EO sea ice competition initiated by the European Space Agency (ESA) ɸ-lab. The purpose of the competition is to develop deep learning models to automatically produce sea ice charts including sea ice concentration, stage-of-development and floe size (form) information. The training datasets contain Sentinel-1 active microwave Synthetic Aperture Radar (SAR) data and corresponding passive MicroWave Radiometer (MWR) data from the AMSR2 satellite sensor. While SAR data has ambiguities between open water and sea ice, it has a high spatial resolution, whereas MWR data has good contrast between open water and ice. However, the coarse resolution of the AMSR2 MWR observations introduces a new set of obstacles, e.g. land spill-over, which can lead to erroneous sea ice predictions along the coastline adjacent to open water. Label data in the challenge datasets are ice charts, that have been produced by the Greenland ice service at the Danish Meteorological Institute (DMI) and the Canadian Ice Service (CIS) for the safety of navigation. The challenge datasets also contain other auxiliary data such as the distance to land and numerical weather prediction model data. The scenes are from the time period from January 8 2018 to December 21 2021. Two versions of the dataset exist, the '<em>raw'</em> and '<em>ready-to-train'-</em>versions with corresponding test datasets<em>. </em>The datasets each consist of the same 512 training and 20 test (without label data) scenes. The ‘<em>ready-to-train’</em>-version has been further prepared for model training, such as downsampled data from 40 to 80 m pixel spacing, standard scaled, converted ice charts (sea ice concentration, stage of development and floe size), removal of nan values, mask alignment etc. This is the Ready-To-Train version. Further details are described in the common manual that is published together with the datasets; “AI4Arctic_challenge-dataset-manual”. Code with a get-started toolkit for the '<em>ready-to-train</em>' dataset: https://github.com/astokholm/AI4ArcticSeaIceChallenge A quick challenge video overview of the challenge is available at: https://youtu.be/iuXIeLPyKfg This item is part of the Collection https://doi.org/10.11583/DTU.c.6244065 Version 2 has 20 additional scenes and has been reprocessed to accommodate the updated mean and STandard Deviation (std). Furthermore, SOD and FLOE variables have been slightly altered from version 1, as the dominant ice code threshold was incorrectly set to 70% and 50%, respectively, instead of the 65%, which was otherwise specified in the dataset manual. Version 3 removes a scene with a faulty ice chart.
DOI: 10.5517/ccdc.csd.cc29w8l2
title: CCDC 2141374: Experimental Crystal Structure Determination

description: Related Article: Xinxin Lv, Penghui Gao, Xiaowei Zhao, Zhiyong Jiang|2023|J.Org.Chem.|88|9459|doi:10.1021/acs.joc.3c00469
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: 10.25919/q3g1-ve93
title: Parkes observations for project P1054 semester 2023APRS_06

description: With this proposal we ask time to continue our timing follow-up campaign of pulsars discovered with the MeerKAT telescope. Twenty sources have been discovered in targeted observations of Fermi unidentified point sources, one in association with a Supernova Remnant, 13 in the Magellanic Clouds, 71 in Globular Clusters and 74 in a survey of the Galactic plane. A large fraction of the new discoveries are recycled pulsars (including at least one double neutron star system and several 'spider' binaries), or young pulsars. Timing observations have an essential role in exploiting the full potential of any pulsar discovery, allowing the precise measurement of rotational, astrometric and orbital parameters which, in turn, give us powerful tools to improve our understanding of the physics in extreme environments as well as of the population of neutron stars as a whole. The UWL receiver of the Parkes telescope is a sensitive, versatile instrument that is allowing us to successfully time these new sources, in the bright-end of TRAPUM discoveries.
DOI: 10.13020/zqw9-mk81
title:  Lake Superior moored temperature and currents, Sep 2005-May 2015 

description: From 2005 to the present, investigators from the Large Lakes Observatory at the University of Minnesota, Duluth have been deploying sub-surface moorings instrumented with temperature and current sensors at a variety of sites around Lake Superior. Temperature sensors at a range of depths, and profiling current meters were used to characterize the physical properties of the lake's water column. At one site, a nearly continuous time series of temperature throughout the water column exists from 2005 to the present,  including winter months. Other sites are occupied for shorter spans. This archival entry covers the period 2005-2015.
description: Temperature data is presented in two different forms. First, individual, raw temperature records are presented in directories corresponding to individual mooring deployments. In addition, a  separate directory includes hourly, gridded data from each individual mooring deployment, which are likely of more interest to investigators. 

Acoustic Doppler Current Profiler (ADCP) data is presented in raw form.

All data is presented in MATLAB format.
DOI: 10.21227/3qe9-e178
title: Pulmonary lobe segmentation of COVID-19 CT scans

description: We chose 8 publicly available CT volumes of COVID-19 positive patients which were available from https://doi.org/10.5281/zenodo.3757476 and used 3D slicer to generate volumetric annotations of 512*512 dimension for 5 lung lobes namely right upper lobe, right middle lobe, right lower lobe, left upper lobe and left lower lobe. These annotations are validated by a radiologist with over 15 years of experience.
DOI: 10.5281/zenodo.1048161
title: Theia Oso Land Cover Map 2106

description: http://osr-cesbio.ups-tlse.fr/~oso/posts/2017-03-30-carte-s2-2016/
description: {"references": ["Inglada, J., Vincent, A., Arias, M., Tardy, B., Morin, D., & Rodes, I., Operational high resolution land cover map production at the country scale using satellite image time series, Remote Sensing, 9(1), 95 (2017).  http://dx.doi.org/10.3390/rs9010095"]}
DOI: 10.5281/zenodo.1993596
title: 2017 Metropolitan France Land Cover Map - CESBIO - OSO

description: This archive contains the land cover map of metropolitan France for the year 2017 using Sentinel-2 imagery produced by CESBIO. The algorithm description is available in:

Inglada, J.; Vincent, A.; Arias, M.; Tardy, B.; Morin, D.; Rodes, I. Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series. Remote Sens. 2017, 9, 95. https://doi.org/10.3390/rs9010095<br>
DOI: 10.5281/zenodo.6538861
title: Theia OSO Land Cover Map 2020

description: Land cover map of France based on Sentinel-2 satellite images with iota² chain (https://framagit.org/iota2-project/iota2/)
description: {"references": ["Inglada, J., Vincent, A., Arias, M., Tardy, B., Morin, D., & Rodes, I., Operational high resolution land cover map production at the country scale using satellite image time series, Remote Sensing, 9(1), 95 (2017). http://dx.doi.org/10.3390/rs9010095"]}
DOI: 10.5281/zenodo.7895449
title: Natural grasslands across mainland France: a dataset including a 10 m raster and ground reference points

description: The data provided here include the first 10 m raster of natural grasslands across mainland France and related ground reference points. The latter consist of 1,770 field observations that describe natural and artificial grasslands from respectively a compilation of hundreds of field-based vegetation maps and the European Union Land Parcel Identification System (LPIS). The raster data of natural grasslands were derived from five annual 10 m land cover maps of France from 2016-2020. More details can be found on the following reference : Panhelleux, L., Rapinel, S., Hubert-Moy, L., 2023. Natural grasslands across mainland France: a dataset including a 10 m raster and ground reference points. Data in Brief 109348. https://doi.org/10.1016/j.dib.2023.109348
DOI: 10.5281/zenodo.7477577
title: Multi-wavelength study of ionospheric irregularities

description: The data contains supporting metadata for a research article.
DOI: 10.18462/iir.rankine.2020.1170
title: The effect of internal efficiency of expander on the working fluid selection.
DOI: 10.11583/dtu.13675000
title: Supplementary data for: "Hygrothermal assessment of north facing, cold attic spaces under the eaves with varying structural roof scenarios"

description: Research data and supplementary files for the journal publication.<br>The research project was co-funded by the Landowners' Investment Foundation (Grundejernes Investeringsfond) (GI) and supervised by Associate Professor Søren Peter Bjarløv.Status: FinishedEffective start/end date: 01/01/2014 to 01/01/2019<br>Supplementary data for: Nickolaj Feldt Jensen, Søren Peter Bjarløv, Christopher Just Johnston, Hygrothermal assessment of north-facing, cold attic spaces under the eaves with varying structural roof scenarios, 2019, Journal of Building Physics, Vol 44, Issue 1, pp. 3-36, https://doi.org/10.1177/1744259119891753<br>The dataset contains the following elements:1) Raw data_Phase12) Raw data_Phase1_Re-arranged3) Raw data_Phase24) Raw data_Pressure measurements5) Raw data_Tracer gas experiment6) Deriving process for Equation 87) Polynomial trendlines and flow relation8) Processed data_Phase29) Summary of key results10) Supplementary graphs for research article11) VTT mould model12) Final report on the "Ventilation conditions in cold attic rooms under the eaves in structures with diffusion-open roofing underlay, Phase 2", in Danish (Skunkrum etape 2 slutrapport)13) Short report with links to publications and project folders, in Danish (Skunkrum Etape 2 - kort rapport til GI + links)<br>
DOI: 10.11583/dtu.13675000.v1
title: Supplementary data for: "Hygrothermal assessment of north facing, cold attic spaces under the eaves with varying structural roof scenarios"

description: Research data and supplementary files for the journal publication.<br>The research project was co-funded by the Landowners' Investment Foundation (Grundejernes Investeringsfond) (GI) and supervised by Associate Professor Søren Peter Bjarløv.Status: FinishedEffective start/end date: 01/01/2014 to 01/01/2019<br>Supplementary data for: Nickolaj Feldt Jensen, Søren Peter Bjarløv, Christopher Just Johnston, Hygrothermal assessment of north-facing, cold attic spaces under the eaves with varying structural roof scenarios, 2019, Journal of Building Physics, Vol 44, Issue 1, pp. 3-36, https://doi.org/10.1177/1744259119891753<br>The dataset contains the following elements:1) Raw data_Phase12) Raw data_Phase1_Re-arranged3) Raw data_Phase24) Raw data_Pressure measurements5) Raw data_Tracer gas experiment6) Deriving process for Equation 87) Polynomial trendlines and flow relation8) Processed data_Phase29) Summary of key results10) Supplementary graphs for research article11) VTT mould model12) Final report on the "Ventilation conditions in cold attic rooms under the eaves in structures with diffusion-open roofing underlay, Phase 2", in Danish (Skunkrum etape 2 slutrapport)13) Short report with links to publications and project folders, in Danish (Skunkrum Etape 2 - kort rapport til GI + links)<br>
DOI: 10.5519/qd.1o8vcibj
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (5588977 records)
DOI: 10.6084/m9.figshare.23552545.v1
title: A spatio-temporal analysis of marine diatom communities associated with pristine and aged plastics

description: Complex microbial communities colonize plastic substrates over time, strongly influencing their fate and potential impacts on marine ecosystems. Among the first colonizers, diatoms play an important role in the development of this ‘plastiphere’. We investigated 936 biofouling samples and the factors influencing diatom communities associated with plastic colonization. These factors included geographic location (up to 800 km apart), duration of substrate submersion (1 to 52 weeks), plastics (5 polymer types) and impact of artificial ageing with UV light. Diatom communities colonizing plastic debris were primarily determined by their geographic location and submersion time, with the strongest changes occurring within two weeks of submersion. Several taxa were identified as early colonizers (e.g. <i>Cylindrotheca</i>, <i>Navicula</i> and <i>Nitzschia</i> spp.) with known strong adhesion capabilities. To a lesser extent, plastic-type and UV-ageing significantly affected community composition, with 14 taxa showing substrate-specificity. This study highlights the role of plastics types-state for colonization in the ocean.
DOI: 10.6084/m9.figshare.23552545
title: A spatio-temporal analysis of marine diatom communities associated with pristine and aged plastics

description: Complex microbial communities colonize plastic substrates over time, strongly influencing their fate and potential impacts on marine ecosystems. Among the first colonizers, diatoms play an important role in the development of this ‘plastiphere’. We investigated 936 biofouling samples and the factors influencing diatom communities associated with plastic colonization. These factors included geographic location (up to 800 km apart), duration of substrate submersion (1 to 52 weeks), plastics (5 polymer types) and impact of artificial ageing with UV light. Diatom communities colonizing plastic debris were primarily determined by their geographic location and submersion time, with the strongest changes occurring within two weeks of submersion. Several taxa were identified as early colonizers (e.g. <i>Cylindrotheca</i>, <i>Navicula</i> and <i>Nitzschia</i> spp.) with known strong adhesion capabilities. To a lesser extent, plastic-type and UV-ageing significantly affected community composition, with 14 taxa showing substrate-specificity. This study highlights the role of plastics types-state for colonization in the ocean.
DOI: 10.15454/p27ldx/0bruuv
title: Metadata_SystemePFAFF_Versailles_EN.txt

description: Fichier métadonnées du "Fichier Système PFAFF", version anglaise; Metadata file of the "PFAFF System File", English version;
DOI: 10.15454/p27ldx/jrlj5f
title: Metadata_Versailles_EN.txt

description: Fichier Métadonnées du "fichier Versailles", version anglaise; Metadata file of the "Versailles file", English version
DOI: 10.15454/p27ldx/oddlsr
title: Metadata_Versailles_FR.txt

description: Fichier métadonnées "fichier Versailles", version française; Metadata file of the "Versailles file", French version
DOI: 10.15454/p27ldx/xvp68g
title: Fichier_SystemePFAFF_Versailles.tab

description: Données phénologiques sur le système PFAFF à Versailles; Phenological data on the PFAFF system in Versailles
DOI: 10.15454/p27ldx/yxysrh
title: Fichier_Versailles.tab

description: Données phénologiques du site de Versailles; Phenological data from the Versailles site
DOI: 10.15454/p27ldx/3adu51
title: Metadata_Fruitiers_Versailles_EN.txt

description: Fichier métadonnées associé au "Fichier Fruitiers", version anglaise; Metadata file associated with the "Fruit Tree File", English version
DOI: 10.15454/p27ldx/cquhr6
title: Fichier_Fruitiers_Versailles.tab

description: Données phénologiques sur des arbres fruitiers à Versailles; Phenological data on fruit trees in Versailles
DOI: 10.15454/p27ldx/ezjusa
title: Metadata_SystemePFAFF_Versailles_FR.txt

description: Fichier métadonnées du "Fichier Système PFAFF", version française ; Metadata file of the "PFAFF System File", French version;
DOI: 10.15454/p27ldx/wbzdpv
title: Metadata_Fruitiers_Versailles_FR.txt

description: Fichier métadonnées pour le "Fichier Fruitiers", version française; Metadata file for the "Fichier Fruitiers", French version
DOI: 10.57745/a1thys
title: Lisezmoi_Versailles_Tout_FR.txt

description: Fichier "Lisez moi" - Il intègre toutes les descriptions des différents fichiers
DOI: 10.57745/gjswg9
title: Versailles_INRAE_TEMPO.xlsx

description: TEMPO file
DOI: 10.57745/lf6pwd
title: Readme_Versailles_All_EN.txt

description: Read me File- You can find all the description of different files
DOI: 10.5281/zenodo.8130360
title: CHD-CDCHD-CN-861-2-CN-DM-861_clinical_isolates_MIC_RUB_2023-05-31

description: MIC values of Chelocardin, amidochelocardin and cystobactamids towards clinical isolates
DOI: 10.5281/zenodo.8130359
title: CHD-CDCHD-CN-861-2-CN-DM-861_clinical_isolates_MIC_RUB_2023-05-31

description: MIC values of Chelocardin, amidochelocardin and cystobactamids towards clinical isolates
DOI: 10.5517/ccdc.csd.cc29224m
title: CCDC 2117149: Experimental Crystal Structure Determination

description: Related Article: Xun Feng, Yapei Shang, Ka Zhang, Manzhou Hong, Junfeng Li, Hongdi Xu, Liya Wang, Zhongjun Li|2022|CrystEngComm|24|4187|doi:10.1039/D2CE00613H
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.