Standardized data from Mobilise-D participants (YAR dataset) and pre-existing datasets (ICICLE, MSIPC2, Gait in Lab and real-life settings, MS project, UNISS-UNIGE) are provided in the shared folder, as an example of the procedures proposed in the publication "Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization" that is currently under review in Scientific data. Please refer to that publication for further information. Please cite that publication if using these data.

The code to standardize an example subject (for the ICICLE dataset) and to open the standardized Matlab files in other languages (Python, R) is available in github (https://github.com/luca-palmerini/Procedure-wearable-data-standardization-Mobilise-D).

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. This feature class is the second division of the PLSS is quarter, quarter-quarter, sixteenth or government lot divisions of the PLSS. The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class.

This dataset consists of cartographic data in digital line graph (DLG) form for the northeastern states (Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode Island and Vermont). Information is presented on two planimetric base categories, political boundaries and administrative boundaries, each available in two formats: the topologically structured format and a simpler format optimized for graphic display. These DGL data can be used to plot base maps and for various kinds of spatial analysis. They may also be combined with other geographically referenced data to facilitate analysis, for example the Geographic Names Information System.

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. This feature class is the second division of the PLSS is quarter, quarter-quarter, sixteenth or government lot divisions of the PLSS. The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class.

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This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. This feature class is the second division of the PLSS is quarter, quarter-quarter, sixteenth or government lot divisions of the PLSS. The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class.

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. This feature class is the second division of the PLSS is quarter, quarter-quarter, sixteenth or government lot divisions of the PLSS. The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class.

Context

Please read the description file of the Data Set. The work I done was adjusting the data into a acceptable file format by kaggle standards.

Content

1 - instance - instance indicator

1 - component - component number (integer)

2 - sup - support in the machine where measure was taken (1..4)

3 - cpm - frequency of the measure (integer)

4 - mis - measure (real)

5 - misr - earlier measure (real)

6 - dir - filter, type of the measure and direction: {vo=no filter, velocity, horizontal, va=no filter, velocity, axial, vv=no filter, velocity, vertical, ao=no filter, amplitude, horizontal, aa=no filter, amplitude, axial, av=no filter, amplitude, vertical, io=filter, velocity, horizontal, ia=filter, velocity, axial, iv=filter, velocity, vertical}

7 - omega - rpm of the machine (integer, the same for components of one example)

8 - class - classification (1..6, the same for components of one example)

9 - comb. class - combined faults

10 - other class - other faults occuring

Acknowledgements

Data Source: https://archive.ics.uci.edu/ml/datasets/Mechanical+Analysis

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. This feature class is the second division of the PLSS is quarter, quarter-quarter, sixteenth or government lot divisions of the PLSS. The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class.

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. This feature class is the second division of the PLSS is quarter, quarter-quarter, sixteenth or government lot divisions of the PLSS. The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class.

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. This feature class is the second division of the PLSS is quarter, quarter-quarter, sixteenth or government lot divisions of the PLSS. The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class.

📦 Dataset Description

This dataset is part of the GarmentIQ project, which focuses on understanding, classifying, and ultimately measuring garments from fashion imagery. This particular dataset supports garment classification, a foundational step in the broader pipeline of GarmentIQ.

The images are sourced from two major fashion platforms: Myntra and Nordstrom, and include a curated selection of high-quality fashion photos. All images are resized to a standardized resolution of 480×736 for consistency in model training.

Keywords: fashion imagery, garment classification, clothes classification, myntra, nordstrom, fashion dataset, image resizing, machine learning, deep learning, CNN, fashion photo dataset, model training.

👗 Garment Image Breakdown

Note:

Using the URL metadata.csv can identify the source of image. See this notebook for example.

• If the image is from Myntra, the URL should contain the domain assets.myntassets.com.
• If the image is from Nordstrom, the URL should contain the domain n.nordstrommedia.com.

📊 What You Can Do with This Data

This dataset provides a rich collection of garment images and classification labels, making it perfect for various use cases in fashion and machine learning:

• Garment Classification: Train models to classify garments based on types such as "short sleeve top," "trousers," and "skirts." See [Demo 2] Garment Classification as an example.
• Fashion Recommendation Systems: Utilize garment classification data for building personalized recommendation engines in e-commerce platforms.
• Model Benchmarking: Benchmark your computer vision models on a wide variety of garment images to evaluate their performance across different article types.

With consistent, high-quality garment images from Myntra and Nordstrom, this dataset can be applied to a wide range of fashion-related AI projects.

📐 Why 480×736 Resolution?

The resolution 480×736 pixels was selected as the standard image size in this dataset for two key reasons:

Nordstrom Standard: Many images are from Nordstrom, which uses 480×736 as its default resolution. Keeping this size avoids distortion and maintains original quality.

Balanced Size & Detail: This resolution preserves key garment features while keeping file sizes efficient for training deep learning models.

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. This feature class is the second division of the PLSS is quarter, quarter-quarter, sixteenth or government lot divisions of the PLSS. The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class.

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. This feature class is the second division of the PLSS is quarter, quarter-quarter, sixteenth or government lot divisions of the PLSS. The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class.

Home inspection dataset for Gemini Long Context kaggle comp

There are three sections to this dataset:

1) Building standards This is a copy of construction codes from: https://ncc.abcb.gov.au/ It describes the standards to which Australian residential homes should be constructed and is a valuable resource for anyone looking to assess a home. In Australia this is the minimum standard for new homes.

2) Examples This is a set of "task examples" designed for in-context learning. It is a set of images of houses and corresponding professional assessment (that I have paid experts for)

3) User data Here is a set of images / videos from the house I am looking to evaluate

In general, the idea is that we use Gemini's long context window to effectively evaluate the User data against the building standards, using the examples to demonstrate to the LLM how we want the assessment to work

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. This feature class is the second division of the PLSS is quarter, quarter-quarter, sixteenth or government lot divisions of the PLSS. The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class.

Abstract

MedAlign is a benchmark dataset of 983 clinician-curated natural language instructions for EHR data, grounded by 275 longitudinal EHRs. It includes reference responses for 303 instructions and supports evaluation of LLMs on healthcare-specific tasks.

Methodology

**IMPORTANT USAGE NOTE: **MedAlign only includes test set examples. No training examples are provided for fine-tuning models.

1. Overview

MedAlign is a longitudinal EHR benchmark for instruction-following with LLMs. The dataset includes:

• 275 patients
• 46,252 clinical notes
• 128 clinical note types
• 3.6 million clinical events

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2. EHR Data

EHR data is sourced from Stanford’s STARR-OMOP database. Data are standardized in the OMOP CDM schema and are scrubbed on identifying PHI information. Complete technical details are included in the paper, but key highlights:

• Dates are jittered within patient to conceal real dates (but preserve deltas between dates)
• Data for patients %3E= 90 years old are removed

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• Unstructured text fields not mappable to OMOP standard concepts are redacted

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• All clinical note text has been scrubbed of PHI variables using hiding-in-plain-sight (HIPS) Carrell et al. 2013.
• HIV test results are redacted.
• Provider names and NPIs are redacted

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3. Instruction Following Benchmark

See "medalign_instructions_responses_v1_2.zip" for instructions, responses, and EHR text timelines.

Please see our Github repo to obtain code for loading the dataset.

Usage

Access to the MedAlign dataset requires the following:

• Verified Affiliation (Academic, Government, Industry Research Lab). Please use your verified email address when applying, **do not use gmail or personal emails. **Applications using personal, unverified email addresses will be rejected.
• Encryption Verification / Attestation for Data Storage
• Signing the terms of the MedAlign Data Set License 1.0
• Providing a short description of your intended research use of MedAlign
• CITI Training

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**These data must remain on your encrypted machine. Redistribution of data is FORBIDDEN and will result in immediate termination of access privileges. **

IMPORTANT NOTES:

• Our policy on derived works aligns with PhysioNet's guidelines, requiring that these artifacts be hosted on Redivis. If you create derived research artifacts based on MedAlign (such as additional annotations or synthetic data), please contact us to discuss hosting arrangements.
• Sending MedAlign data over a non-HIPAA-compliant API is a violation of the DUA.

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Please allow 7-10 business days to process applications.

About this dataset

Context

The dataset tabulates the Standard population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Standard. The dataset can be utilized to understand the population distribution of Standard by age. For example, using this dataset, we can identify the largest age group in Standard.

Key observations

The largest age group in Standard, IL was for the group of age 55 to 59 years years with a population of 31 (10.33%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in Standard, IL was the 70 to 74 years years with a population of 1 (0.33%). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Standard is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Standard total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Standard Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the Standard population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Standard. The dataset can be utilized to understand the population distribution of Standard by age. For example, using this dataset, we can identify the largest age group in Standard.

Key observations

The largest age group in Standard, IL was for the group of age 85+ years with a population of 27 (9.68%), according to the 2021 American Community Survey. At the same time, the smallest age group in Standard, IL was the 70-74 years with a population of 1 (0.36%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Standard is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Standard total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Standard Population by Age. You can refer the same here

This article presents an original database on international standards, constructed using modern data gathering methods. StanDat facilitates studies into the role of standards in the global political economy by (1) being a source for descriptive statistics, (2) enabling researchers to assess scope conditions of previous findings, and (3) providing data for new analyses, for example the exploration of the relationship between standardization and trade, as demonstrated in this article. The creation of StanDat aims to stimulate further research into the domain of standards. Moreover, by exemplifying data collection and dissemination techniques applicable to investigating less-explored subjects in the social sciences, it serves as a model for gathering, systematizing and sharing data in areas where information is plentiful yet not readily accessible for research.