OASIS-3 is a retrospective compilation of data for 1378 participants that were collected across several ongoing projects through the WUSTL Knight ADRC over the course of 30years. Participants include 755 cognitively normal adults and 622 individuals at various stages of cognitive decline ranging in age from 42-95yrs. All participants were assigned a new random identifier and all dates were removed and normalized to reflect days from entry into study. The dataset contains 2842 MR sessions which include T1w, T2w, FLAIR, ASL, SWI, time of flight, resting-state BOLD, and DTI sequences. Many of the MR sessions are accompanied by volumetric segmentation files produced through FreeSurfer processing. PET imaging from different tracers, PIB, AV45, and FDG, totaling over 2157 raw imaging scans and the accompanying post-processed files from the Pet Unified Pipeline (PUP) are also available in OASIS-3.

OASIS-3 and OASIS-4 are the latest releases in the Open Access Series of Imaging Studies (OASIS) that is aimed at making neuroimaging datasets freely available to the scientific community. By compiling and freely distributing this multimodal dataset generated by the Knight ADRC and its affiliated studies, we hope to facilitate future discoveries in basic and clinical neuroscience. OASIS-4 contains MR, clinical, cognitive, and biomarker data for 663 individuals aged 21 to 94 that presented with memory complaints. This is a unique dataset and not an update to the OASIS-3 Longitudinal Multimodal Neuroimaging dataset.

BackgroundAccording to the World Health Organization (WHO), dementia is the seventh leading reason of death among all illnesses and one of the leading causes of disability among the world’s elderly people. Day by day the number of Alzheimer’s patients is rising. Considering the increasing rate and the dangers, Alzheimer’s disease should be diagnosed carefully. Machine learning is a potential technique for Alzheimer’s diagnosis but general users do not trust machine learning models due to the black-box nature. Even, some of those models do not provide the best performance because of using only neuroimaging data.ObjectiveTo solve these issues, this paper proposes a novel explainable Alzheimer’s disease prediction model using a multimodal dataset. This approach performs a data-level fusion using clinical data, MRI segmentation data, and psychological data. However, currently, there is very little understanding of multimodal five-class classification of Alzheimer’s disease.MethodFor predicting five class classifications, 9 most popular Machine Learning models are used. These models are Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), Multi-Layer Perceptron (MLP), K-Nearest Neighbor (KNN), Gradient Boosting (GB), Adaptive Boosting (AdaB), Support Vector Machine (SVM), and Naive Bayes (NB). Among these models RF has scored the highest value. Besides for explainability, SHapley Additive exPlanation (SHAP) is used in this research work.Results and conclusionsThe performance evaluation demonstrates that the RF classifier has a 10-fold cross-validation accuracy of 98.81% for predicting Alzheimer’s disease, cognitively normal, non-Alzheimer’s dementia, uncertain dementia, and others. In addition, the study utilized Explainable Artificial Intelligence based on the SHAP model and analyzed the causes of prediction. To the best of our knowledge, we are the first to present this multimodal (Clinical, Psychological, and MRI segmentation data) five-class classification of Alzheimer’s disease using Open Access Series of Imaging Studies (OASIS-3) dataset. Besides, a novel Alzheimer’s patient management architecture is also proposed in this work.

Analysis of ‘MRI and Alzheimers’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/jboysen/mri-and-alzheimers on 21 November 2021.

--- Dataset description provided by original source is as follows ---

Context:

The Open Access Series of Imaging Studies (OASIS) is a project aimed at making MRI data sets of the brain freely available to the scientific community. By compiling and freely distributing MRI data sets, we hope to facilitate future discoveries in basic and clinical neuroscience. OASIS is made available by the Washington University Alzheimer’s Disease Research Center, Dr. Randy Buckner at the Howard Hughes Medical Institute (HHMI)( at Harvard University, the Neuroinformatics Research Group (NRG) at Washington University School of Medicine, and the Biomedical Informatics Research Network (BIRN).

Content:

• Cross-sectional MRI Data in Young, Middle Aged, Nondemented and Demented Older Adults: This set consists of a cross-sectional collection of 416 subjects aged 18 to 96. For each subject, 3 or 4 individual T1-weighted MRI scans obtained in single scan sessions are included. The subjects are all right-handed and include both men and women. 100 of the included subjects over the age of 60 have been clinically diagnosed with very mild to moderate Alzheimer’s disease (AD). Additionally, a reliability data set is included containing 20 nondemented subjects imaged on a subsequent visit within 90 days of their initial session.
• Longitudinal MRI Data in Nondemented and Demented Older Adults: This set consists of a longitudinal collection of 150 subjects aged 60 to 96. Each subject was scanned on two or more visits, separated by at least one year for a total of 373 imaging sessions. For each subject, 3 or 4 individual T1-weighted MRI scans obtained in single scan sessions are included. The subjects are all right-handed and include both men and women. 72 of the subjects were characterized as nondemented throughout the study. 64 of the included subjects were characterized as demented at the time of their initial visits and remained so for subsequent scans, including 51 individuals with mild to moderate Alzheimer’s disease. Another 14 subjects were characterized as nondemented at the time of their initial visit and were subsequently characterized as demented at a later visit.

Acknowledgements:

When publishing findings that benefit from OASIS data, please include the following grant numbers in the acknowledgements section and in the associated Pubmed Central submission: P50 AG05681, P01 AG03991, R01 AG021910, P20 MH071616, U24 RR0213

Inspiration:

Can you predict dementia? Alzheimer’s?

--- Original source retains full ownership of the source dataset ---

Context:

The Open Access Series of Imaging Studies (OASIS) is a project aimed at making MRI data sets of the brain freely available to the scientific community. By compiling and freely distributing MRI data sets, we hope to facilitate future discoveries in basic and clinical neuroscience. OASIS is made available by the Washington University Alzheimer’s Disease Research Center, Dr. Randy Buckner at the Howard Hughes Medical Institute (HHMI)( at Harvard University, the Neuroinformatics Research Group (NRG) at Washington University School of Medicine, and the Biomedical Informatics Research Network (BIRN).

Content:

• Cross-sectional MRI Data in Young, Middle Aged, Nondemented and Demented Older Adults: This set consists of a cross-sectional collection of 416 subjects aged 18 to 96. For each subject, 3 or 4 individual T1-weighted MRI scans obtained in single scan sessions are included. The subjects are all right-handed and include both men and women. 100 of the included subjects over the age of 60 have been clinically diagnosed with very mild to moderate Alzheimer’s disease (AD). Additionally, a reliability data set is included containing 20 nondemented subjects imaged on a subsequent visit within 90 days of their initial session.
• Longitudinal MRI Data in Nondemented and Demented Older Adults: This set consists of a longitudinal collection of 150 subjects aged 60 to 96. Each subject was scanned on two or more visits, separated by at least one year for a total of 373 imaging sessions. For each subject, 3 or 4 individual T1-weighted MRI scans obtained in single scan sessions are included. The subjects are all right-handed and include both men and women. 72 of the subjects were characterized as nondemented throughout the study. 64 of the included subjects were characterized as demented at the time of their initial visits and remained so for subsequent scans, including 51 individuals with mild to moderate Alzheimer’s disease. Another 14 subjects were characterized as nondemented at the time of their initial visit and were subsequently characterized as demented at a later visit.

Acknowledgements:

When publishing findings that benefit from OASIS data, please include the following grant numbers in the acknowledgements section and in the associated Pubmed Central submission: P50 AG05681, P01 AG03991, R01 AG021910, P20 MH071616, U24 RR0213

Inspiration:

Can you predict dementia? Alzheimer’s?

BackgroundAccording to the World Health Organization (WHO), dementia is the seventh leading reason of death among all illnesses and one of the leading causes of disability among the world’s elderly people. Day by day the number of Alzheimer’s patients is rising. Considering the increasing rate and the dangers, Alzheimer’s disease should be diagnosed carefully. Machine learning is a potential technique for Alzheimer’s diagnosis but general users do not trust machine learning models due to the black-box nature. Even, some of those models do not provide the best performance because of using only neuroimaging data.ObjectiveTo solve these issues, this paper proposes a novel explainable Alzheimer’s disease prediction model using a multimodal dataset. This approach performs a data-level fusion using clinical data, MRI segmentation data, and psychological data. However, currently, there is very little understanding of multimodal five-class classification of Alzheimer’s disease.MethodFor predicting five class classifications, 9 most popular Machine Learning models are used. These models are Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), Multi-Layer Perceptron (MLP), K-Nearest Neighbor (KNN), Gradient Boosting (GB), Adaptive Boosting (AdaB), Support Vector Machine (SVM), and Naive Bayes (NB). Among these models RF has scored the highest value. Besides for explainability, SHapley Additive exPlanation (SHAP) is used in this research work.Results and conclusionsThe performance evaluation demonstrates that the RF classifier has a 10-fold cross-validation accuracy of 98.81% for predicting Alzheimer’s disease, cognitively normal, non-Alzheimer’s dementia, uncertain dementia, and others. In addition, the study utilized Explainable Artificial Intelligence based on the SHAP model and analyzed the causes of prediction. To the best of our knowledge, we are the first to present this multimodal (Clinical, Psychological, and MRI segmentation data) five-class classification of Alzheimer’s disease using Open Access Series of Imaging Studies (OASIS-3) dataset. Besides, a novel Alzheimer’s patient management architecture is also proposed in this work.

In addition to OASIS-3 data, we introduce ‘OASIS-3_AV1451’ a cross-sectional TAU PET dataset for 451 subjects including 451 PET sessions and post-processed PUP. This set is a subset of OASIS-3 subjects that have also undergone TAU (AV1451) PET imaging.

Objective: To develop and evaluate a model for staging cortical amyloid deposition using PET with high generalizability.

Methods: 3027 subjects (1763 Cognitively Unimpaired (CU), 658 Impaired, 467 Alzheimer’s disease (AD) dementia, 111 non-AD dementia, and 28 with missing diagnosis) from six cohorts (EMIF-AD, ALFA, ABIDE, ADC, OASIS-3, ADNI) who underwent amyloid PET were retrospectively included; 1049 subjects had follow-up scans. Applying dataset-specific cut-offs to global Standard Uptake Value ratio (SUVr) values from 27 regions, single-tracer and pooled multi-tracer regional rankings were constructed from the frequency of abnormality across 400 CU subjects (100 per tracer). The pooled multi-tracer ranking was used to create a staging model consisting of four clusters of regions as it displayed a high and consistent correlation with each single-tracer ranking. Relationships between amyloid stage, clinical variables and longitudinal cognitive decline were investigated.

Results: SUV...

Curated T1w MRI and CT scans from diverse sources, including the CERMEP-iDB-MRXFDG, OASIS3, NeuroMorphometrics1, IXI 2, and an in-house dataset.

Historical price and volatility data for Oasis in Vietnamese Dong across different time periods.

BackgroundAccording to the World Health Organization (WHO), dementia is the seventh leading reason of death among all illnesses and one of the leading causes of disability among the world’s elderly people. Day by day the number of Alzheimer’s patients is rising. Considering the increasing rate and the dangers, Alzheimer’s disease should be diagnosed carefully. Machine learning is a potential technique for Alzheimer’s diagnosis but general users do not trust machine learning models due to the black-box nature. Even, some of those models do not provide the best performance because of using only neuroimaging data.ObjectiveTo solve these issues, this paper proposes a novel explainable Alzheimer’s disease prediction model using a multimodal dataset. This approach performs a data-level fusion using clinical data, MRI segmentation data, and psychological data. However, currently, there is very little understanding of multimodal five-class classification of Alzheimer’s disease.MethodFor predicting five class classifications, 9 most popular Machine Learning models are used. These models are Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), Multi-Layer Perceptron (MLP), K-Nearest Neighbor (KNN), Gradient Boosting (GB), Adaptive Boosting (AdaB), Support Vector Machine (SVM), and Naive Bayes (NB). Among these models RF has scored the highest value. Besides for explainability, SHapley Additive exPlanation (SHAP) is used in this research work.Results and conclusionsThe performance evaluation demonstrates that the RF classifier has a 10-fold cross-validation accuracy of 98.81% for predicting Alzheimer’s disease, cognitively normal, non-Alzheimer’s dementia, uncertain dementia, and others. In addition, the study utilized Explainable Artificial Intelligence based on the SHAP model and analyzed the causes of prediction. To the best of our knowledge, we are the first to present this multimodal (Clinical, Psychological, and MRI segmentation data) five-class classification of Alzheimer’s disease using Open Access Series of Imaging Studies (OASIS-3) dataset. Besides, a novel Alzheimer’s patient management architecture is also proposed in this work.

The Occupational and Skills Information System (OaSIS) provides a comprehensive framework of the skills and competencies that are usually required to work in different occupations. It also provides context for the work environment in which these occupations are performed. This dataset provides information on 900 occupations including ratings for over 240 descriptors belonging to one of the following categories: Skills, Abilities, Personal attributes, Knowledge, Interests, Work activities and Work context. Standardized dimensions and scales were established at the category level for all categories, except Work context, for which specific dimensions and scales were adopted. All the descriptors under Skills and Abilities have ratings attributed by proficiency level required by occupation on a scale of 1 to 5. Descriptors under Personal attributes are measured by Importance on a scale of 1 to 5. Descriptors under Knowledge are measured by the knowledge level on a scale of 1 to3. Descriptors under Interests are measured by predominance ranking of Holland Codes. Descriptors under Work activities are measured by complexity on a scale of 1 to 5. Descriptors under Work context are measured by one or two of the following dimensions: - Frequency (1-5 scale) - 1 - Once a year or more but not every month - 2 - Once a month or more but not every week - 3 - Once a week or more but not every day - 4 - Every day, a few times per day - 5 - Every day, many times per day - Duration (1-5 scale) - 1 - Very little time - 2 - Less than half the time - 3 - About half the time - 4 - More than half the time - 5 - All the time, or almost all the time - Degree of responsibility (1-5 scale) - Physical distance from others (1-5 scale) - Degree of consequence of error (1-5 scale) - Degree of impact (1-5 scale) - Degree of freedom to make decisions (1-5 scale) - Degree of freedom to determine tasks and priorities (1-5 scale) - Degree of competition (1-5 scale) - Degree of automation (1-3 scale) - Work schedule (1-3 scale) - Worked hours in a typical week (1-3 scale) This dataset is of national relevance.

The Mindboggle-101 surface and volume brain atlases were generated as part of the Mindboggle project (http://mindboggle.info) and are licensed under a Creative Commons License. The anatomical labels are combined across individually labeled brain volumes or surfaces from Mindboggle-101 according to the Desikan-Killiany-Tourville (DKT) labeling protocol: "101 labeled brain images and a consistent human cortical labeling protocol" Arno Klein, Jason Tourville. Frontiers in Brain Imaging Methods. 6:171. DOI: 10.3389/fnins.2012.00171 Volume atlas construction The joint fusion algorithm (Hongzhi Wang, 2013; distributed with ANTs) was used to construct probabilistic labels of the 20 OASIS-TRT brains, including a single volume of probabilities corresponding to the winning labels. Joint fusion was performed on the 20 brains after antsRegistration warped them to the OASIS-30 Atropos template. Results are also given after affine transformation to the OASIS-30 Atropos template in MNI152 space, and after resampling to 2mm^3 resolution. Code and documentation: CHANGELOG.txt: log of changes make_jointfusion_atlas.txt: code to make a joint fusion atlas resample2mm.txt: code to resample a volume to 2mm^3 resolution make_freesurfer_classifier_atlas.txt: software to make a FreeSurfer classifier atlas Joint fusion volume atlases (2013) OASIS-TRT-20_jointfusion_DKT31_CMA_labels_in_OASIS-30_v2.nii.gz OASIS-TRT-20_jointfusion_DKT31_CMA_labels_in_MNI152_v2.nii.gz OASIS-TRT-20_jointfusion_DKT31_CMA_labels_in_MNI152_2mm_v2.nii.gz Joint fusion label probabilities (2013) OASIS-TRT-20_jointfusion_DKT31_CMA_label_probabilities_in_OASIS-30_v2.nii.gz OASIS-TRT-20_jointfusion_DKT31_CMA_label_probabilities_in_MNI152_v2.nii.gz OASIS-TRT-20_jointfusion_DKT31_CMA_label_probabilities_in_MNI152_2mm_v2.nii.gz Cortical surface atlases (FreeSurfer classifier .gcs file) freesurfer_atlas_DKT31labels101subjects.tar.gz: from all 101 participants (2016) DKTatlas40.tar.gz: from 40 participants (2012)

Historical price and volatility data for Oasis Metaverse in Taiwan New Dollar across different time periods.

Using OASIS at cassegrain focus of the 188-cm telescope of the Okayama Astrophysical Observatory (OAO), infrared images centered at 2.35 micron were taken of the crash of fragment K from the Shoemaker-Levy 9 comet into Jupiter. The data consist of 160 images with detached labels and documentation.

T2-weighted (T2w)

No description is available. Visit https://dataone.org/datasets/d297a6dc16d1cb54c604375e42b43c27 for complete metadata about this dataset.

About this dataset

Context

The dataset tabulates the Oasis town population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Oasis town. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 163 (47.11% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Oasis town population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Oasis town is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Oasis town is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Oasis town Population by Age. You can refer the same here

Customs records of are available for OASIS TEXTILE INTL HOLDINGS HK 3.L.. Learn about its Importer, supply capabilities and the countries to which it supplies goods

Seamounts are of great interest to science, industry and conservation because of their potential role as 'stirring rods' of the oceans, their enhanced productivity, their high local biodiversity, and the growing exploitation of their natural resources. This is accompanied by rising concern about the threats to seamount ecosystems, e.g. through over-fishing and the impact of trawling. OASIS described the functioning characteristics of seamount ecosystems. OASIS' integrated hydrographic, biogeochemical and biological information. Based on two case studies. The scientific results, condensed in conceptual and mass balanced ecosystem models, were applied to outline a model management plan as well as site-specific management plans for the seamounts investigated. OASIS addressed five main objectives: Objective 1: To identify and describe the physical forcing mechanisms effecting seamount systems Objective 2: To assess the origin, quality and dynamics of particulate organic material within the water column and surface sediment at seamounts. Objective 3: To describe aspects of the biodiversity and the ecology of seamount biota, to assess their dynamics and the maintenance of their production. Objective 4: Modelling the trophic ecology of seamount ecosystems. Objective 5: Application of scientific knowledge to practical conservation.