cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6622145
title: OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics

description: Clinical diagnosis of the eye is performed over multifarious data modalities including scalar clinical labels, vectorized biomarkers, two-dimensional fundus images, and three-dimensional Optical Coherence Tomography (OCT) scans. While the clinical labels, fundus images and OCT scans are instrumental measurements, the vectorized biomarkers are interpreted attributes from the other measurements. Clinical practitioners use all these data modalities for diagnosing and treating eye diseases like Diabetic Retinopathy (DR) or Diabetic Macular Edema (DME). Enabling usage of machine learning algorithms within the ophthalmic medical domain requires research into the relationships and interactions between these relevant data modalities. Existing datasets are limited in that: (i) they view the problem as disease prediction without assessing biomarkers, and (ii) they do not consider the explicit relationship among all four data modalities over the treatment period. In this paper, we introduce the Ophthalmic Labels for Investigating Visual Eye Semantics (OLIVES) dataset that addresses the above limitations. This is the first OCT and fundus dataset that includes clinical labels, biomarker labels, and time-series patient treatment information from associated clinical trials. The dataset consists of $1268$ fundus eye images each with 49 OCT scans, and 16 biomarkers, along with 3 clinical labels and a disease diagnosis of DR or DME. In total, there are 96 eyes' data averaged over a period of at least two years with each eye treated for an average of 66 weeks and 7 injections. OLIVES dataset has advantages in other fields of machine learning research including self-supervised learning as it provides alternate augmentation schemes that are medically grounded.
DOI: 10.5281/zenodo.6622144
title: OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics

description: Clinical diagnosis of the eye is performed over multifarious data modalities including scalar clinical labels, vectorized biomarkers, two-dimensional fundus images, and three-dimensional Optical Coherence Tomography (OCT) scans. While the clinical labels, fundus images and OCT scans are instrumental measurements, the vectorized biomarkers are interpreted attributes from the other measurements. Clinical practitioners use all these data modalities for diagnosing and treating eye diseases like Diabetic Retinopathy (DR) or Diabetic Macular Edema (DME). Enabling usage of machine learning algorithms within the ophthalmic medical domain requires research into the relationships and interactions between these relevant data modalities. Existing datasets are limited in that: (i) they view the problem as disease prediction without assessing biomarkers, and (ii) they do not consider the explicit relationship among all four data modalities over the treatment period. In this paper, we introduce the Ophthalmic Labels for Investigating Visual Eye Semantics (OLIVES) dataset that addresses the above limitations. This is the first OCT and fundus dataset that includes clinical labels, biomarker labels, and time-series patient treatment information from associated clinical trials. The dataset consists of $1268$ fundus eye images each with 49 OCT scans, and 16 biomarkers, along with 3 clinical labels and a disease diagnosis of DR or DME. In total, there are 96 eyes' data averaged over a period of at least two years with each eye treated for an average of 66 weeks and 7 injections. OLIVES dataset has advantages in other fields of machine learning research including self-supervised learning as it provides alternate augmentation schemes that are medically grounded.
description: Code: https://github.com/olivesgatech/OLIVES_Dataset
DOI: 10.5281/zenodo.7105232
title: OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics

description: Clinical diagnosis of the eye is performed over multifarious data modalities including scalar clinical labels, vectorized biomarkers, two-dimensional fundus images, and three-dimensional Optical Coherence Tomography (OCT) scans. While the clinical labels, fundus images and OCT scans are instrumental measurements, the vectorized biomarkers are interpreted attributes from the other measurements. Clinical practitioners use all these data modalities for diagnosing and treating eye diseases like Diabetic Retinopathy (DR) or Diabetic Macular Edema (DME). Enabling usage of machine learning algorithms within the ophthalmic medical domain requires research into the relationships and interactions between these relevant data modalities. Existing datasets are limited in that: (i) they view the problem as disease prediction without assessing biomarkers, and (ii) they do not consider the explicit relationship among all four data modalities over the treatment period. In this paper, we introduce the Ophthalmic Labels for Investigating Visual Eye Semantics (OLIVES) dataset that addresses the above limitations. This is the first OCT and fundus dataset that includes clinical labels, biomarker labels, and time-series patient treatment information from associated clinical trials. The dataset consists of $1268$ fundus eye images each with 49 OCT scans, and 16 biomarkers, along with 3 clinical labels and a disease diagnosis of DR or DME. In total, there are 96 eyes' data averaged over a period of at least two years with each eye treated for an average of 66 weeks and 7 injections. OLIVES dataset has advantages in other fields of machine learning research including self-supervised learning as it provides alternate augmentation schemes that are medically grounded.
description: Code: https://github.com/olivesgatech/OLIVES_Dataset
DOI: 10.17034/84db96ac-2827-4c11-ba31-d5c5fe6aa4b6
title: Support Vector Regression Example Code

description: This text file contains an example of how to train a support vector regression robot error compensation model using grid search hyperparameter optimisation, and how the trained model can be used to test on a new robot program. This code is written in Python 3. Dataset embargoed until 31 December 2023.
DOI: 10.17034/e58d16e9-2468-4d60-862c-a51c0bd74e8d
title: 2000 Point UR10 Robot Program

description: This text file contains the 2000 point robot program that was sent to the UR10 robot. This text file must be changed to a .script file for the UR10. The attained positions of the robot were captured using a laser tracker, and then the error in the positions were calculated. This information was then used to train the robot error compensation models. Dataset embargoed until 31 December 2023.
DOI: 10.5281/zenodo.7105289
title: Supplementary information for yqiC and global transcriptome in Salmonella

description: Supplementary information (Additional files 1-22, including 3 files, Table S1-S9, Fig. S1-S10) in the article entitled "Effects of colonization-associated gene <em>yqiC</em> on global transcriptome, cellular respiration, and oxidative stress in <em>Salmonella </em>Typhimurium"
DOI: 10.5281/zenodo.7105288
title: Supplementary information for yqiC and global transcriptome in Salmonella

description: Supplementary information (Additional files 1-22, including 3 files, Table S1-S9, Fig. S1-S10) in the article entitled "Effects of colonization-associated gene <em>yqiC</em> on global transcriptome, cellular respiration, and oxidative stress in <em>Salmonella </em>Typhimurium"
DOI: 10.5281/zenodo.7105346
title: Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.

description: Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.
DOI: 10.5281/zenodo.7105345
title: Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.

description: Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.
DOI: 10.6084/m9.figshare.21187588
title: enchelyopus_cimbrius_zanfona_1.fasta.gz

description: ......
DOI: 10.6084/m9.figshare.21187588.v1
title: enchelyopus_cimbrius_zanfona_1.fasta.gz

description: ......
DOI: 10.57745/tkvka4
title: Raw data - Relation between ellipsoids axes.xlsx

description: :unav
DOI: 10.5281/zenodo.7105287
title: [Supplementary material] Automated Code Generation for Inter-parameter Dependencies in REST APIs

description: This is the supplementary material of the paper entitled Automated Code Generation for Inter-parameter Dependencies in REST APIs.
DOI: 10.5281/zenodo.7105286
title: [Supplementary material] Automated Code Generation for Inter-parameter Dependencies in REST APIs

description: This is the supplementary material of the paper entitled Automated Code Generation for Inter-parameter Dependencies in REST APIs.
DOI: 10.6084/m9.figshare.21187594
title: gaidropsarus_macrophthalmus_zanfona_1.fasta.gz

description: .......
DOI: 10.6084/m9.figshare.21187594.v1
title: gaidropsarus_macrophthalmus_zanfona_1.fasta.gz

description: .......
DOI: 10.17909/g37r-1r51
title: HST Photometry and Astrometry of the Bootes I Ultrafaint Dwarf Galaxy

description: Bootes I is a nearby, relatively bright ultrafaint dwarf galaxy. This dataset consists of two catalogs of sources in the line-of-sight to this ultrafaint galaxy, produced from deep, optical imaging in three fields, taken with the Hubble Space Telescope Advanced Camera for Surveys, Wide Field Camera in the F606W and F814W filters. The first catalog contains photometry that we produced for the sources in the field. The second catalog contains relative proper motions for a subset of the brighter sources, which we were able to derive with a baseline of ~7 years thanks to the existence of earlier-epoch, archival imaging.
DOI: 10.34681/0306-s940
title: Rockwool-Duke Global Child Welfare Database

description: The Rockwool–Duke Global Child Welfare Database (RDGCWD) compiles cross-national aggregate information from the Global North about children who experienced contact with child welfare systems (CWS) or child protective services (CPS) between 2000 and 2020. Data were collected from national child welfare agencies or national or transnational statistical agencies, and include measures of child maltreatment investigations, confirmed child maltreatment, and children in out-of-home care. Population data are included for the measurement of incidence and prevalence of system contact. The RDGCWD provides a unique resource for comparative and longitudinal research on child maltreatment and child welfare systems. However, potential unobserved variation in the definition of child maltreatment or in the administration of child welfare and child protection warrant caution in the interpretation of variation and change in system contact.
DOI: 10.5281/zenodo.7041928
title: Monitoring open access publishing of NWO funded research (2015-2021) data set

description: This is the dataset underlying the report "Monitoring open access publishing of NWO funded research" (https://doi.org/10.5281/zenodo.7041897) The report presents statistics on the extent to which publications from the period 2015–2021 funded by NWO are available in Open Access. The analyses presented in this report also cover publications funded by the Netherlands Organisation for Health Research and Development ZonMw. This report builds on two earlier reports, published in 2020 and 2021, covering publications from the period 2015–2018 and 2015-2020, respectively.
DOI: 10.5281/zenodo.7041929
title: Monitoring open access publishing of NWO funded research (2015-2021) data set

description: This is the dataset underlying the report "Monitoring open access publishing of NWO funded research" (https://doi.org/10.5281/zenodo.7041897) The report presents statistics on the extent to which publications from the period 2015–2021 funded by NWO are available in Open Access. The analyses presented in this report also cover publications funded by the Netherlands Organisation for Health Research and Development ZonMw. This report builds on two earlier reports, published in 2020 and 2021, covering publications from the period 2015–2018 and 2015-2020, respectively.
DOI: 10.5281/zenodo.7105367
title: Monitoring open access publishing of NWO funded research (2015-2021) data set

description: This is the dataset underlying the report "Monitoring open access publishing of NWO funded research" (https://doi.org/10.5281/zenodo.7041897) The report presents statistics on the extent to which publications from the period 2015–2021 funded by NWO are available in Open Access. The analyses presented in this report also cover publications funded by the Netherlands Organisation for Health Research and Development ZonMw. This report builds on two earlier reports, published in 2020 and 2021, covering publications from the period 2015–2018 and 2015-2020, respectively.
DOI: 10.5517/ccdc.csd.cc28ppg4
title: CCDC 2106216: Experimental Crystal Structure Determination

description: Related Article: Xuwei Luo, Lifen Chen, Bo Liu, Zhiwen Yang, Longmeng Wei, Zhanxiang Yuan, Yudong Wen, Yingxiao Mu, Yanping Huo, Hao-Li Zhang, Shaomin Ji|2022|J.Mater.Chem.C|10|13210|doi:10.1039/D2TC02789E
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: 10.6084/m9.figshare.21187609
title: urophycis_floridana_zanfona_1.fasta.gz

description: .......
DOI: 10.6084/m9.figshare.21187609.v1
title: urophycis_floridana_zanfona_1.fasta.gz

description: .......
DOI: 10.6084/m9.figshare.21187645
title: merluccius_bilinearis_zanfona_1.fasta.gz

description: .......
DOI: 10.6084/m9.figshare.21187645.v1
title: merluccius_bilinearis_zanfona_1.fasta.gz

description: .......
DOI: 10.34740/kaggle/dsv/4237755
title: Mahanagar Yatayat Dataset
DOI: 10.6084/m9.figshare.21187651
title: merluccius_polylepis_zanfona_1.fasta.gz

description: .......
DOI: 10.6084/m9.figshare.21187651.v1
title: merluccius_polylepis_zanfona_1.fasta.gz

description: .......
DOI: 10.6084/m9.figshare.21187660
title: merluccius_hubbsi_zanfona_1.fasta.gz

description: .......
DOI: 10.6084/m9.figshare.21187660.v1
title: merluccius_hubbsi_zanfona_1.fasta.gz

description: .......
DOI: 10.5285/8bcf6925cddc4681b96f94d424537b9e
title: MIDAS Open: UK daily temperature data, v202207

description: The UK daily temperature data contain maximum and minimum temperatures (air, grass and concrete slab) measured over a period of up to 24 hours. The measurements were recorded by observation stations operated by the Met Office across the UK and transmitted within NCM, DLY3208 or AWSDLY messages. The data span from 1853 to 2021. For details on measurement techniques, including calibration information and changes in measurements, see section 5.2 of the MIDAS User Guide linked to from this record. Soil temperature data may be found in the UK soil temperature datasets linked from this record. This version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2021. This dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Currently this represents approximately 95% of available daily temperature observations within the full MIDAS collection.
DOI: 10.5285/e3a7f3336ff8464f9ae6534a8e8676e5
title: MIDAS Open: UK hourly solar radiation data, v202207

description: The UK hourly solar radiation data contain the amount of solar irradiance received during the hour ending at the specified time. All sites report 'global' radiation amounts. This is also known as 'total sky radiation' as it includes both direct solar irradiance and 'diffuse' irradiance as a result of light scattering. Some sites also provide separate diffuse and direct irradiation amounts, depending on the instrumentation at the site. For these the sun's path is tracked with two pyrometers - one where the path to the sun is blocked by a suitable disc to allow the scattered sunlight to be measured to give the diffuse measurement, while the other has a tube pointing at the sun to measure direct solar irradiance whilst blanking out scattered sun light. This version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2021. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: SYNOP, HCM, AWSHRLY, MODLERAD, ESAWRADT and DRADR35 messages. The data spans from 1947 to 2021. This dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record.