PRIO is hosting a copy of this dataset with permission from Global Data Lab. Please see their webpage for more information about this data.

Used for assembly, checkout, and lab-scale testing of prototype hardware.

The size of the Development Lab Box market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX% during the forecast period.

This dataset shows the New York City Housing Authority (NYCHA) Development Data Book as of January 1, 2019.

The size of the Lab Development Testing market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX% during the forecast period.

This is the CSV file of clinical trial data used for an interactive visualization at Aero Data Lab (https://www.aerodatalab.org/birds-eye-view-of-research-landscape).

Medical Instruments Development Lab Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

ObjectiveThe German Health Data Lab is going to provide access to German statutory health insurance claims data ranging from 2009 to the present for research purposes. Due to evolving data formats within the German Health Data Lab, there is a need to standardize this data into a Common Data Model to facilitate collaborative health research and minimize the need for researchers to adapt to multiple data formats. For this purpose we selected transforming the data to the Observational Medical Outcomes Partnership Common Data Model.MethodsWe developed an Extract, Transform, and Load (ETL) pipeline for two distinct German Health Data Lab data formats: Format 1 (2009-2016) and Format 3 (2019 onwards). Due to the identical format structure of Format 1 and Format 2 (2017 -2018), the ETL pipeline of Format 1 can be applied on Format 2 as well. Our ETL process, supported by Observational Health Data Sciences and Informatics tools, includes specification development, SQL skeleton creation, and concept mapping. We detail the process characteristics and present a quality assessment that includes field coverage and concept mapping accuracy using example data.ResultsFor Format 1, we achieved a field coverage of 92.7%. The Data Quality Dashboard showed 100.0% conformance and 80.6% completeness, although plausibility checks were disabled. The mapping coverage for the Condition domain was low at 18.3% due to invalid codes and missing mappings in the provided example data. For Format 3, the field coverage was 86.2%, with Data Quality Dashboard reporting 99.3% conformance and 75.9% completeness. The Procedure domain had very low mapping coverage (2.2%) due to the use of mocked data and unmapped local concepts The Condition domain results with 99.8% of unique codes mapped. The absence of real data limits the comprehensive assessment of quality.ConclusionThe ETL process effectively transforms the data with high field coverage and conformance. It simplifies data utilization for German Health Data Lab users and enhances the use of OHDSI analysis tools. This initiative represents a significant step towards facilitating cross-border research in Europe by providing publicly available, standardized ETL processes (https://github.com/FraunhoferMEVIS/ETLfromHDLtoOMOP) and evaluations of their performance.

Abstract

An effective policy response to the economic impacts of the COVID-19 pandemic requires an enormous range of data to inform the design and response of programs. Public health measures require data on the spread of the disease, beliefs in the population, and capacity of the health system. Relief efforts depend on an understanding of hardships being faced by various segments of the population. Food policy requires measurement of agricultural production and hunger. In such a rapidly evolving pandemic, these data must be collected at a high frequency. Given the unexpected nature of the shock and urgency with which a response was required, Indian policymakers needed to formulate policies affecting India's 1.4 billion people, without the detailed evidence required to construct effective programs. To help overcome this evidence gap, the World Bank, IDinsight, and the Development Data Lab sought to produce rigorous and responsive data for policymakers across six states in India: Jharkhand, Rajasthan, Uttar Pradesh, Andhra Pradesh, Bihar, and Madhya Pradesh.

Geographic coverage

Jharkhand, Rajasthan, Uttar Pradesh, Andhra Pradesh, Bihar, and Madhya Pradesh

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

The samples for these surveys were drawn from surveys and impact evaluations previously conducted by the World Bank, the Ministry of Rural Development, India and IDInsight. A detailed note on the sampling frames is available for download.

Sampling deviation

Details will be made available after all rounds of data collection and analysis is complete.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The survey questionnaire consists of the following modules: - Module 0: Introduction - Module 1: Migration - Module 2: Labor and Income - Module 3: Consumption - Module 4: Agriculture - Module 5: Access to Relief - Module 6: Health

Response rate

~55%

Data on analysis for on-site PAH and microplastic sensors

Data on analysis performed in the lab during development and characterisation of the sensors - For development of other sensors or models/software/tools by WATERUN consortium partners

Dataset Description: Lab Data Extracted from Marham.pk This dataset contains comprehensive details about various medical tests and laboratory services extracted from Marham.pk, a popular healthcare platform in Pakistan. The dataset provides structured information on different tests, their types, pricing, locations, and other relevant attributes.

Key Features of the Dataset The dataset includes the following important attributes:

Test Name – The name of the medical test (e.g., CBC, Lipid Profile, Blood Sugar Test). Test Type – The category or type of test (e.g., Blood Test, Radiology, Urine Test). Lab Name – The name of the laboratory offering the test. Lab Location – The city or specific area where the lab is located. Test Price – The cost of the test in Pakistani Rupees (PKR). Test Description – A brief overview of the purpose and details of the test. Availability – Indicates whether the test is available in a specific lab. Sample Requirements – Specifies whether fasting, urine, or blood samples are needed. Processing Time – The estimated time required to complete and deliver the test results. Discounts & Offers – Any special discounts provided by labs. Potential Use Cases This dataset can be utilized for various applications, including:

✅ Healthcare Analysis – Understanding pricing trends and availability of medical tests across different regions. ✅ Price Comparison – Comparing the cost of tests across multiple labs. ✅ Medical App Development – Integrating with health applications for users to find and book tests online. ✅ Data Visualization – Creating dashboards to display test availability and pricing insights. ✅ Predictive Analytics – Analyzing trends to predict future demands for medical tests.

ObjectiveThe German Health Data Lab is going to provide access to German statutory health insurance claims data ranging from 2009 to the present for research purposes. Due to evolving data formats within the German Health Data Lab, there is a need to standardize this data into a Common Data Model to facilitate collaborative health research and minimize the need for researchers to adapt to multiple data formats. For this purpose we selected transforming the data to the Observational Medical Outcomes Partnership Common Data Model.MethodsWe developed an Extract, Transform, and Load (ETL) pipeline for two distinct German Health Data Lab data formats: Format 1 (2009-2016) and Format 3 (2019 onwards). Due to the identical format structure of Format 1 and Format 2 (2017 -2018), the ETL pipeline of Format 1 can be applied on Format 2 as well. Our ETL process, supported by Observational Health Data Sciences and Informatics tools, includes specification development, SQL skeleton creation, and concept mapping. We detail the process characteristics and present a quality assessment that includes field coverage and concept mapping accuracy using example data.ResultsFor Format 1, we achieved a field coverage of 92.7%. The Data Quality Dashboard showed 100.0% conformance and 80.6% completeness, although plausibility checks were disabled. The mapping coverage for the Condition domain was low at 18.3% due to invalid codes and missing mappings in the provided example data. For Format 3, the field coverage was 86.2%, with Data Quality Dashboard reporting 99.3% conformance and 75.9% completeness. The Procedure domain had very low mapping coverage (2.2%) due to the use of mocked data and unmapped local concepts The Condition domain results with 99.8% of unique codes mapped. The absence of real data limits the comprehensive assessment of quality.ConclusionThe ETL process effectively transforms the data with high field coverage and conformance. It simplifies data utilization for German Health Data Lab users and enhances the use of OHDSI analysis tools. This initiative represents a significant step towards facilitating cross-border research in Europe by providing publicly available, standardized ETL processes (https://github.com/FraunhoferMEVIS/ETLfromHDLtoOMOP) and evaluations of their performance.

Dataset Description

This dataset contains all the tests used for the low-cost sensor development during the iSCAPE project. The dataset is divided in a series of tests, each of them described on a yaml file with the test name. Each csv file contains time series data of each experiment, and the yaml files contain the lists of devices used in each test. The tests are described in the comment of the yaml file, and are meant to be self explanatory. The conditions of the test and the purpose vary, and their reports are also included.

Sensors

The sensors used are herein referred as Citizen Kits or Smart Citizen Kits, and the Living Lab Station or Smart Citizen Station. These are a set of modular hardware components that feature a selection of low cost sensors for environmental monitoring listed below. The Smart Citizen Station is meant to expand the capabilities of the Smart Citizen Kit, aiming to measure pollutants with more advanced sensors. The hardware is licensed under CERN Open Hardware License V1.2 and is fully described in the HardwareX Open Access publication: https://doi.org/10.1016/j.ohx.2019.e00070. The sensor documentation can be found at https://docs.smartcitizen.me and with this DOI at Zenodo: https://doi.org/10.5281/zenodo.2555029.

In the list below, the different sensors for the Citizen Kits are detailed, and their [CHANNELS] in the csv files above linked.

Air temperature (ºC): Sensirion SHT-31 [TEMP]

Relative Humidity (%rh): Sensirion SHT-31 [HUM]

Noise level (dBA): Invensense ICS-434342 [NOISE_A]

Ambient light (lux): Rohm BH1721FVC [LIGHT]

Barometric pressure (kPa): NXP MPL3115A26 [PRESS]

Particulate Matter PM 1 / 2.5 / 10 (µg/m3) Planttower PMS 5003 [EXT_PM_1,EXT_PM_25,EXT_PM_10]

In the list below, the different sensors for the Citizen Kits are detailed, and their [CHANNELS] in the csv files above linked.

Air Temperature (ºC) Sensirion SHT-31 [TEMP]

Relative Humidity (% REL) Sensirion SHT-31 [HUM]

Noise Level (dBA) Invensense ICS-434342 [NOISE_A]

Ambient Light (Lux) Rohm BH1721FVC [LIGHT]

Barometric pressure and AMSL (Pa and Meters) NXP MPL3115A26 [PRESS]

Carbon Monoxide (µg/m3 (Periodic Baseline Calibration Required) SGX MICS-4514 [NA]

Nitrogen Dioxide (µg/m3 (Periodic Baseline Calibration Required) SGX MICS-4514 [NA]

Carbon Monoxide (ppm) Alphasense CO-B4 [GB_1W, GB_1A]

Nitrogen Dioxide (ppb) Alphasense NO2-B43F [GB_2W, GB_2A]

Ozone (ppb) Alphasense OX-B431 [GB_3W, GB_3A]

Gases Board Temperature (ºC) Sensirion SHT-31 [GB_TEMP] or [EXT_TEMP]

Gases Board Rel. Humidity (% REL) Sensirion SHT-31 [GB_HUM] or [EXT_HUM]

PM 1 (µg/m3) Plantower PMS5003 [EXT_PM_1] or [EXT_PM_A_1], [EXT_PM_B_1] for each PM sensor in the case of the Living Lab Station

PM 2.5 (µg/m3) Plantower PMS5003 [EXT_PM_25] or [EXT_PM_A_25], [EXT_PM_B_25] for each PM sensor in the case of the Living Lab Station

PM 10 (µg/m3) Plantower PMS5003 [EXT_PM_10] or [EXT_PM_A_10], [EXT_PM_B_10] for each PM sensor in the case of the Living Lab Station

PN between 0.3um<0.5um particle size (#/l) Plantower PMS5003 [EXT_PN_03] or [EXT_PN_A_03], [EXT_PN_B_03] for each PM sensor in the case of the Living Lab Station

PN between 0.5um<1um particle size (#/l) Plantower PMS5003 [EXT_PN_05] or [EXT_PN_A_05], [EXT_PN_B_05] for each PM sensor in the case of the Living Lab Station

PN between 1m<2.5um particle size (#/l) Plantower PMS5003 [EXT_PN_1] or [EXT_PN_A_1], [EXT_PN_B_1] for each PM sensor in the case of the Living Lab Station

PN between 2.5m<5um particle size (#/l) Plantower PMS5003 [EXT_PN_25] or [EXT_PN_A_25], [EXT_PN_B_25] for each PM sensor in the case of the Living Lab Station

PN between 5m<10um particle size (#/l) Plantower PMS5003 [EXT_PN_5] or [EXT_PN_A_5], [EXT_PN_B_5] for each PM sensor in the case of the Living Lab Station

PN between >10um particle size (#/l) Plantower PMS5003 [EXT_PN_10] or [EXT_PN_A_10], [EXT_PN_B_10] for each PM sensor in the case of the Living Lab Station

How to find the data

Each yaml file contains the description of a test. Each test is comprised of recordings of several devices in the same location and during the same period. Each yaml file is comprised of the following fields:

author: who has been in charge of performing the test (internal reference - not relevant)

comment: describing in general terms what was done in the test, and with what purpose

commit: the firmware commit (in the case of Smart Citizen devices) with which the test was performed, for development purposes only

devices: a descriptor containing different fields for traceability (below)

id: the test name

project: within the test was performed, in this case it is always iscape

report: if there is any report analysing the test

type_test: indoor, oudoor test or other.

Description of devices entry

For each device that was used in the test, two generic types are used:

low cost sensors (type: STATION or KIT)

high end sensors (type: REFERENCE)

For low cost Smart Citizen sensors, the fields are:

alphasense: electrochemical sensors device ids, by pollutant (for manufacturer calibration) and slots in which they were placed

device_id: device id in Smartcitizen API

fileNameInfo: not used

fileNameProc: (only if source = csv is specified) 2019-03_EXT_UCD_URBAN_BACKGROUND_API_CITY_COUNCIL_REF.csv

fileNameRaw: (only if source = csv is used) raw file name

frequency: original recording frequency

location: for timezone correction only, not accurate

max_date: last recording date

min_date: first recording date

name: self-explanatory

pm_sensor: if there was a pm sensor connected (all of them are PMS5003 if no sensor is specified)

source: api or csv

type: STATION (KIT + Alphasense + PM board with two PMS5003) or KIT

version: smartcitizen hardware version

For high end sensors, the fields are:

channels: which channels the device was recording for internal convertion

names: which are the columns in the csv file

pollutants: which pollutants do they respectively refer to

units: the units of these pollutants

equipment: the brand of the analyser

fileNameProc: same as above

fileNameRaw: same as above

index: format in which the timeindex is done, for parsing purposes

format: (example '%Y-%m-%d %H:%M:%S')

frequency: frequency at which the device was recorded

name: column name

location: same as above

name: name of the device

type: REFERENCE (always for these devices)

source: csv

iSCAPE Dataset Reference Numbers:

The datasets here presented are related to the following iSCAPE dataset reference numbers:

DS_TS_054

DS_TS_062

DS_TS_063

DS_TS_065

DS_TS_067

DS_TS_068

DS_TS_069

DS_TS_070

DS_TS_071

DS_TS_072

DS_TS_073

DS_TS_074

DS_TS_075

DS_TS_076

DS_TS_077

DS_TS_078

DS_TS_079

DS_TS_080

DS_TS_081

DS_TS_084

DS_TS_088

DS_TS_089

DS_TS_090

DS_TS_092

Health index

Discover the booming Development Lab Box market! This comprehensive analysis reveals key trends, growth drivers, and regional insights for 2025-2033, including detailed market segmentation by application (vocational education, R&D, corporate training) and technology (DSP, ARM, DSP+ARM). Explore the competitive landscape and future market forecasts.

Development Medical Lab Services Trade Limited Company Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Objective: To develop a clinical informatics pipeline designed to capture large-scale structured EHR data for a national patient registry.

Materials and Methods: The EHR-R-REDCap pipeline is implemented using R-statistical software to remap and import structured EHR data into the REDCap-based multi-institutional Merkel Cell Carcinoma (MCC) Patient Registry using an adaptable data dictionary.

Results: Clinical laboratory data were extracted from EPIC Clarity across several participating institutions. Labs were transformed, remapped and imported into the MCC registry using the EHR labs abstraction (eLAB) pipeline. Forty-nine clinical tests encompassing 482,450 results were imported into the registry for 1,109 enrolled MCC patients. Data-quality assessment revealed highly accurate, valid labs. Univariate modeling was performed for labs at baseline on overall survival (N=176) using this clinical informatics pipeline.

Conclusion: We demonstrate feasibility of the facile eLAB workflow. EHR data is successfully transformed, and bulk-loaded/imported into a REDCap-based national registry to execute real-world data analysis and interoperability.

Methods eLAB Development and Source Code (R statistical software):

eLAB is written in R (version 4.0.3), and utilizes the following packages for processing: DescTools, REDCapR, reshape2, splitstackshape, readxl, survival, survminer, and tidyverse. Source code for eLAB can be downloaded directly (https://github.com/TheMillerLab/eLAB).

eLAB reformats EHR data abstracted for an identified population of patients (e.g. medical record numbers (MRN)/name list) under an Institutional Review Board (IRB)-approved protocol. The MCCPR does not host MRNs/names and eLAB converts these to MCCPR assigned record identification numbers (record_id) before import for de-identification.

Functions were written to remap EHR bulk lab data pulls/queries from several sources including Clarity/Crystal reports or institutional EDW including Research Patient Data Registry (RPDR) at MGB. The input, a csv/delimited file of labs for user-defined patients, may vary. Thus, users may need to adapt the initial data wrangling script based on the data input format. However, the downstream transformation, code-lab lookup tables, outcomes analysis, and LOINC remapping are standard for use with the provided REDCap Data Dictionary, DataDictionary_eLAB.csv. The available R-markdown ((https://github.com/TheMillerLab/eLAB) provides suggestions and instructions on where or when upfront script modifications may be necessary to accommodate input variability.

The eLAB pipeline takes several inputs. For example, the input for use with the ‘ehr_format(dt)’ single-line command is non-tabular data assigned as R object ‘dt’ with 4 columns: 1) Patient Name (MRN), 2) Collection Date, 3) Collection Time, and 4) Lab Results wherein several lab panels are in one data frame cell. A mock dataset in this ‘untidy-format’ is provided for demonstration purposes (https://github.com/TheMillerLab/eLAB).

Bulk lab data pulls often result in subtypes of the same lab. For example, potassium labs are reported as “Potassium,” “Potassium-External,” “Potassium(POC),” “Potassium,whole-bld,” “Potassium-Level-External,” “Potassium,venous,” and “Potassium-whole-bld/plasma.” eLAB utilizes a key-value lookup table with ~300 lab subtypes for remapping labs to the Data Dictionary (DD) code. eLAB reformats/accepts only those lab units pre-defined by the registry DD. The lab lookup table is provided for direct use or may be re-configured/updated to meet end-user specifications. eLAB is designed to remap, transform, and filter/adjust value units of semi-structured/structured bulk laboratory values data pulls from the EHR to align with the pre-defined code of the DD.

Data Dictionary (DD)

EHR clinical laboratory data is captured in REDCap using the ‘Labs’ repeating instrument (Supplemental Figures 1-2). The DD is provided for use by researchers at REDCap-participating institutions and is optimized to accommodate the same lab-type captured more than once on the same day for the same patient. The instrument captures 35 clinical lab types. The DD serves several major purposes in the eLAB pipeline. First, it defines every lab type of interest and associated lab unit of interest with a set field/variable name. It also restricts/defines the type of data allowed for entry for each data field, such as a string or numerics. The DD is uploaded into REDCap by every participating site/collaborator and ensures each site collects and codes the data the same way. Automation pipelines, such as eLAB, are designed to remap/clean and reformat data/units utilizing key-value look-up tables that filter and select only the labs/units of interest. eLAB ensures the data pulled from the EHR contains the correct unit and format pre-configured by the DD. The use of the same DD at every participating site ensures that the data field code, format, and relationships in the database are uniform across each site to allow for the simple aggregation of the multi-site data. For example, since every site in the MCCPR uses the same DD, aggregation is efficient and different site csv files are simply combined.

Study Cohort

This study was approved by the MGB IRB. Search of the EHR was performed to identify patients diagnosed with MCC between 1975-2021 (N=1,109) for inclusion in the MCCPR. Subjects diagnosed with primary cutaneous MCC between 2016-2019 (N= 176) were included in the test cohort for exploratory studies of lab result associations with overall survival (OS) using eLAB.

Statistical Analysis

OS is defined as the time from date of MCC diagnosis to date of death. Data was censored at the date of the last follow-up visit if no death event occurred. Univariable Cox proportional hazard modeling was performed among all lab predictors. Due to the hypothesis-generating nature of the work, p-values were exploratory and Bonferroni corrections were not applied.