Raw data outputs 1-18 Raw data output 1. Differentially expressed genes in AML CSCs compared with GTCs as well as in TCGA AML cancer samples compared with normal ones. This data was generated based on the results of AML microarray and TCGA data analysis. Raw data output 2. Commonly and uniquely differentially expressed genes in AML CSC/GTC microarray and TCGA bulk RNA-seq datasets. This data was generated based on the results of AML microarray and TCGA data analysis. Raw data output 3. Common differentially expressed genes between training and test set samples the microarray dataset. This data was generated based on the results of AML microarray data analysis. Raw data output 4. Detailed information on the samples of the breast cancer microarray dataset (GSE52327) used in this study. Raw data output 5. Differentially expressed genes in breast CSCs compared with GTCs as well as in TCGA BRCA cancer samples compared with normal ones. Raw data output 6. Commonly and uniquely differentially expressed genes in breast cancer CSC/GTC microarray and TCGA BRCA bulk RNA-seq datasets. This data was generated based on the results of breast cancer microarray and TCGA BRCA data analysis. CSC, and GTC are abbreviations of cancer stem cell, and general tumor cell, respectively. Raw data output 7. Differential and common co-expression and protein-protein interaction of genes between CSC and GTC samples. This data was generated based on the results of AML microarray and STRING database-based protein-protein interaction data analysis. CSC, and GTC are abbreviations of cancer stem cell, and general tumor cell, respectively. Raw data output 8. Differentially expressed genes between AML dormant and active CSCs. This data was generated based on the results of AML scRNA-seq data analysis. Raw data output 9. Uniquely expressed genes in dormant or active AML CSCs. This data was generated based on the results of AML scRNA-seq data analysis. Raw data output 10. Intersections between the targeting transcription factors of AML key CSC genes and differentially expressed genes between AML CSCs vs GTCs and between dormant and active AML CSCs or the uniquely expressed genes in either class of CSCs. Raw data output 11. Targeting desirableness score of AML key CSC genes and their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section. Raw data output 12. CSC-specific targeting desirableness score of AML key CSC genes and their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section. Raw data output 13. The protein-protein interactions between AML key CSC genes with themselves and their targeting transcription factors. This data was generated based on the results of AML microarray and STRING database-based protein-protein interaction data analysis. Raw data output 14. The previously confirmed associations of genes having the highest targeting desirableness and CSC-specific targeting desirableness scores with AML or other cancers’ (stem) cells as well as hematopoietic stem cells. These data were generated based on a PubMed database-based literature mining. Raw data output 15. Drug score of available drugs and bioactive small molecules targeting AML key CSC genes and/or their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section. Raw data output 16. CSC-specific drug score of available drugs and bioactive small molecules targeting AML key CSC genes and/or their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section. Raw data output 17. Candidate drugs for experimental validation. These drugs were selected based on their respective (CSC-specific) drug scores. CSC is the abbreviation of cancer stem cell. Raw data output 18. Detailed information on the samples of the AML microarray dataset GSE30375 used in this study.

All tracks of crickets in graphic form and raw data (x, y coordinates and other parameters). Examples of source video recordings.

a = 1 missing data point.b = 2 missing data points.c = 3 missing data points.Summary statistics for the study sample (raw data, not log transformed).

XRD Raw data collected. This dataset is associated with the following publication: Nadagouda , M., C. Han , D. Dionysiou, and L. Wang. An innovative zinc oxide-coated zeolite adsorbent for removal of humic acid. JOURNAL OF HAZARDOUS MATERIALS. Elsevier Science Ltd, New York, NY, USA, 313: 283-290, (2016).

Raw data tables and the statistical analysis applied to the data. Files are labeled by figure number. Within each file, each table and linked graph and analysis is annotated by figure number and panel letter. All files are generated in graphpad prism.

This document mainly includes coding sequences of PME-domain and pro-region of Type-1 PME in representative plants, raw data from fusion gene analysis by LIR inference, raw data from repeat sequence studies within four Cruciferae representative species and graphical abstract.

This imaging mass cytometry (IMC) dataset serves as an example to demonstrate raw data processing and downstream analysis tools. The data was generated as part of the Integrated iMMUnoprofiling of large adaptive CANcer patient cohorts (IMMUcan) project (immucan.eu) using the Hyperion imaging system (www.fluidigm.com/products-services/instruments/hyperion). To get an overview on the technology and available analysis strategies, please visit bodenmillergroup.github.io/IMCWorkflow. The individual data files are described below:

• Patient1.zip, Patient2.zip, Patient3.zip, Patient4.zip: raw data files of 4 patient samples. Each .zip archive contains a folder in which one .mcd file (IMC raw data) and multiple .txt files (one per acquisition) can be found.
• compensation.zip: This .zip archive holds a folder which contains one .mcd file and multiple .txt files. Multiple spots of a "spillover slide" were acquired and each .txt file is named based on the spotted metal. This data is used for channel spillover correction. For more information, please refer to the original publication: Compensation of Signal Spillover in Suspension and Imaging Mass Cytometry
• panel.csv: This file contains metadata for each antibody/channel used in the experiment. The full column indicates which channel should be analysed. The ilastik column specifies which channels were used for ilastik pixel classification and the deepcell column indicates the channels used for deepcell segmentation.
• sample_metadata.csv: This file links each patient to their cancer type (SCCHN - head and neck cancer; BCC - breast cancer; NSCLC - lung cancer; CRC - colorectal cancer).

Raw data and descriptive statistic data of the market survey performed with the Add-In XLSTAT 2009.1.02 is provided as Excel-file (CSV). The data include file name, sample name, area, calculated N2O amounts, test result and statistical values.

Over the course of 24 hours, we collected raw (Photoplethysmography (PPG), Acceleration, and Gyro) and processed (steps, calories, sleep, HR, HRV, SPO2, Respiratory Rate, R-R) data samples. Biostrap approaches health insights from a data-driven perspective. Our clinical-grade hardware enables users to accurately track SpO2, HRV, RHR, and a variety of other biometrics with confidence.

Raw data required to generate the example data associated with the NIfTI-MRS data standard.

The data standard can be found on Zenodo (https://doi.org/10.5281/zenodo.5084788).

The generated example data is also on Zenodo (https://doi.org/10.5281/zenodo.5085448).

Example data generation code is available on Github (https://github.com/wexeee/mrs_nifti_standard/tree/master/example_data)

Raw data on sample languages

Raw data for liquid chromatography coupled with mass spectrometry (LC-MS) experiments along with skyline template used to extract peak area from raw data.

Information on data sources for field analyzer manuscript calculations. This dataset is not publicly accessible because: This data was not generated by EPA, but rather used by EPA researchers to calculate basic statistics (R square and slope), as part of this literature review. It can be accessed through the following means: These two old conference proceedings are available in book volumes that can be found in libraries, with page numbers as specified below: - Argent, V.A., Southall, J.M. and D'Costa, E. (1994) Analysis of water for lead and copper using disposable sensor technology. American Water Works Association – Annual Conference, pp. 43-54, New York, New York. - Wiese, P.M. (1989) Monitoring method for lead in first-draw drinking water samples. American Water Works Association - Annual Conference and Exposition, pp. 1309-1313, Los Angeles, California. Format: Data from three tables in two old conference proceedings were used to calculate basic statistics (R square and slope): - Table 2 and 4 in Proceeding "Argent, V.A., Southall, J.M. and D'Costa, E. (1994) Analysis of water for lead and copper using disposable sensor technology. American Water Works Association – Annual Conference, pp. 43-54, New York, New York." - Table 2 in Proceeding "Wiese, P.M. (1989) Monitoring method for lead in first-draw drinking water samples. American Water Works Association - Annual Conference and Exposition, pp. 1309-1313, Los Angeles, California.". This dataset is associated with the following publication: Dore, E., D. Lytle, L. Wasserstrom, J. Swertfeger, and S. Triantafyllidou. Field Analyzers for Lead Quantification in Drinking Water Samples. CRITICAL REVIEWS IN ENVIRONMENTAL SCIENCE AND TECHNOLOGY. CRC Press LLC, Boca Raton, FL, USA, 50(20): 999-999, (2020).

Raw data from HDMSe and SWATH MS analyses of 309 prostate cancer serum samples. Prostate cancer cohort:
309 patients were divided into control (n=112), prostate cancer (PCa) (n=175), and benign prostate hyperplasia (BPH) (n=22). PCa patients were then subdivided into active surveillance (AS) (n=51) or treatment group. Treatments were radiotherapy (pre: n=26, post: n=14), hormone therapy (pre: n=7, post: n=8), prostatectomy (pre: n=21, post: n=8), and radiotherapy (pre: n=23, post: n=17)

Overview The University of Notre Dame (ND) scanning lidar dataset used for the WFIP2 Campaign is provided. The raw dataset contains the radial velocity and backscatter measurements along with the beam location and other lidar parameters in the header. Data Details 1) A Halo photonics scanning lidar, owned by ND, was deployed and operated from 12/17/2015 to 02/09/2016. On 02/09/2016, this lidar was replaced by a Halo photonics scanning lidar owned by the Army Research Lab (ARL). 2) For information on the scanning patterns, refer to attached "ReadMe" file. 3) Data Period from 12/15/2015 to 02/09/2016: One data file per day (24 hours). File name of each daily data file has {boardman} as {optionalfields}. For example: lidar.z07.00.20150414.143000.boardman.csm. 4) Data Period after 02/09/2016: One scan file every 15 minutes, one stare file, and one background file every hour. File names have the following {optionalfields}: {background_boardman} for background files; {scan_boardman} for scan files; and {stare_boardman} for stare files. For example: - lidar.z07.00.20150414.143000.background_boardman - lidar.z07.00.20150414.143000.scan_boardman - lidar.z07.00.20150414.143000.stare_boardman 5) Site information: - Site: Boardman, OR - Latitude: 45.816185° N - Longitude: 119.811766° W - Elevation (meters): 112.0 Data Quality Raw data: no quality control (QC) is applied. Uncertainty The lidar measurements' uncertainty varies with the range of the measurements. Please refer to Pearson et al. (2009) for more details. Constraints 1) Because of the change of lidars, the data were downloaded in different formats. Hence, the raw data (unfiltered) primarily are in two formats: .csm and .hpl. 2) The data were downloaded every one hour or 15 minutes. Hence, the datasets are not concatenated for continuous scans. 3) A lidar offset of +195 deg (to True North) was added to the azimuthal angles from the ND scanning lidars, spanning 12/17/2015 until 02/09/2016. Later, this was corrected for the data from 02/09/2016 as the lidar aligned to True North.

Here you can find raw data and information about each of the 34 datasets generated by the mulset algorithm and used for further analysis in SIMON. Each dataset is stored in separate folder which contains 4 files: json_info: This file contains, number of features with their names and number of subjects that are available for the same dataset data_testing: data frame with data used to test trained model data_training: data frame with data used to train models results: direct unfiltered data from database Files are written in feather format. Here is an example of data structure for each file in repository. File was compressed using 7-Zip available at https://www.7-zip.org/.

Abstract

The harmonized data set on health, created and published by the ERF, is a subset of Iraq Household Socio Economic Survey (IHSES) 2012. It was derived from the household, individual and health modules, collected in the context of the above mentioned survey. The sample was then used to create a harmonized health survey, comparable with the Iraq Household Socio Economic Survey (IHSES) 2007 micro data set.

----> Overview of the Iraq Household Socio Economic Survey (IHSES) 2012:

Iraq is considered a leader in household expenditure and income surveys where the first was conducted in 1946 followed by surveys in 1954 and 1961. After the establishment of Central Statistical Organization, household expenditure and income surveys were carried out every 3-5 years in (1971/ 1972, 1976, 1979, 1984/ 1985, 1988, 1993, 2002 / 2007). Implementing the cooperation between CSO and WB, Central Statistical Organization (CSO) and Kurdistan Region Statistics Office (KRSO) launched fieldwork on IHSES on 1/1/2012. The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

The survey has six main objectives. These objectives are:

1. Provide data for poverty analysis and measurement and monitor, evaluate and update the implementation Poverty Reduction National Strategy issued in 2009.
2. Provide comprehensive data system to assess household social and economic conditions and prepare the indicators related to the human development.
3. Provide data that meet the needs and requirements of national accounts.
4. Provide detailed indicators on consumption expenditure that serve making decision related to production, consumption, export and import.
5. Provide detailed indicators on the sources of households and individuals income.
6. Provide data necessary for formulation of a new consumer price index number.

The raw survey data provided by the Statistical Office were then harmonized by the Economic Research Forum, to create a comparable version with the 2006/2007 Household Socio Economic Survey in Iraq. Harmonization at this stage only included unifying variables' names, labels and some definitions. See: Iraq 2007 & 2012- Variables Mapping & Availability Matrix.pdf provided in the external resources for further information on the mapping of the original variables on the harmonized ones, in addition to more indications on the variables' availability in both survey years and relevant comments.

Geographic coverage

National coverage: Covering a sample of urban, rural and metropolitan areas in all the governorates including those in Kurdistan Region.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

Kind of data

Sample survey data [ssd]

Sampling procedure

----> Design:

Sample size was (25488) household for the whole Iraq, 216 households for each district of 118 districts, 2832 clusters each of which includes 9 households distributed on districts and governorates for rural and urban.

----> Sample frame:

Listing and numbering results of 2009-2010 Population and Housing Survey were adopted in all the governorates including Kurdistan Region as a frame to select households, the sample was selected in two stages: Stage 1: Primary sampling unit (blocks) within each stratum (district) for urban and rural were systematically selected with probability proportional to size to reach 2832 units (cluster). Stage two: 9 households from each primary sampling unit were selected to create a cluster, thus the sample size of total survey clusters was 25488 households distributed on the governorates, 216 households in each district.

----> Sampling Stages:

In each district, the sample was selected in two stages: Stage 1: based on 2010 listing and numbering frame 24 sample points were selected within each stratum through systematic sampling with probability proportional to size, in addition to the implicit breakdown urban and rural and geographic breakdown (sub-district, quarter, street, county, village and block). Stage 2: Using households as secondary sampling units, 9 households were selected from each sample point using systematic equal probability sampling. Sampling frames of each stages can be developed based on 2010 building listing and numbering without updating household lists. In some small districts, random selection processes of primary sampling may lead to select less than 24 units therefore a sampling unit is selected more than once , the selection may reach two cluster or more from the same enumeration unit when it is necessary.

Mode of data collection

Face-to-face [f2f]

Research instrument

----> Preparation:

The questionnaire of 2006 survey was adopted in designing the questionnaire of 2012 survey on which many revisions were made. Two rounds of pre-test were carried out. Revision were made based on the feedback of field work team, World Bank consultants and others, other revisions were made before final version was implemented in a pilot survey in September 2011. After the pilot survey implemented, other revisions were made in based on the challenges and feedbacks emerged during the implementation to implement the final version in the actual survey.

----> Questionnaire Parts:

The questionnaire consists of four parts each with several sections: Part 1: Socio – Economic Data: - Section 1: Household Roster - Section 2: Emigration - Section 3: Food Rations - Section 4: housing - Section 5: education - Section 6: health - Section 7: Physical measurements - Section 8: job seeking and previous job

Part 2: Monthly, Quarterly and Annual Expenditures: - Section 9: Expenditures on Non – Food Commodities and Services (past 30 days). - Section 10 : Expenditures on Non – Food Commodities and Services (past 90 days). - Section 11: Expenditures on Non – Food Commodities and Services (past 12 months). - Section 12: Expenditures on Non-food Frequent Food Stuff and Commodities (7 days). - Section 12, Table 1: Meals Had Within the Residential Unit. - Section 12, table 2: Number of Persons Participate in the Meals within Household Expenditure Other Than its Members.

Part 3: Income and Other Data: - Section 13: Job - Section 14: paid jobs - Section 15: Agriculture, forestry and fishing - Section 16: Household non – agricultural projects - Section 17: Income from ownership and transfers - Section 18: Durable goods - Section 19: Loans, advances and subsidies - Section 20: Shocks and strategy of dealing in the households - Section 21: Time use - Section 22: Justice - Section 23: Satisfaction in life - Section 24: Food consumption during past 7 days

Part 4: Diary of Daily Expenditures: Diary of expenditure is an essential component of this survey. It is left at the household to record all the daily purchases such as expenditures on food and frequent non-food items such as gasoline, newspapers…etc. during 7 days. Two pages were allocated for recording the expenditures of each day, thus the roster will be consists of 14 pages.

Cleaning operations

----> Raw Data:

Data Editing and Processing: To ensure accuracy and consistency, the data were edited at the following stages: 1. Interviewer: Checks all answers on the household questionnaire, confirming that they are clear and correct. 2. Local Supervisor: Checks to make sure that questions has been correctly completed. 3. Statistical analysis: After exporting data files from excel to SPSS, the Statistical Analysis Unit uses program commands to identify irregular or non-logical values in addition to auditing some variables. 4. World Bank consultants in coordination with the CSO data management team: the World Bank technical consultants use additional programs in SPSS and STAT to examine and correct remaining inconsistencies within the data files. The software detects errors by analyzing questionnaire items according to the expected parameter for each variable.

----> Harmonized Data:

• The SPSS package is used to harmonize the Iraq Household Socio Economic Survey (IHSES) 2007 with Iraq Household Socio Economic Survey (IHSES) 2012.
• The harmonization process starts with raw data files received from the Statistical Office.
• A program is generated for each dataset to create harmonized variables.
• Data is saved on the household and individual level, in SPSS and then converted to STATA, to be disseminated.

Response rate

Iraq Household Socio Economic Survey (IHSES) reached a total of 25488 households. Number of households refused to response was 305, response rate was 98.6%. The highest interview rates were in Ninevah and Muthanna (100%) while the lowest rates were in Sulaimaniya (92%).

Background: The Clinician-Administered PTSD Scale (CAPS-5) is a structured diagnostic interview developed to diagnose post-traumatic stress disorder (PTSD) based on the criteria of the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders. A first study investigating the psychometric properties and factorial structure of the German version of the CAPS-5 was conducted using data from previously-collected data to check the inclusion criteria for PTSD. The current study aimed to verify the robustness of the psychometric properties of the German CAPS-5 via validating it within a routine clinical context could help underlie the robustnessMethods: Overall, 288 participants were recruited. A multi-trait/multi-method design was used to analyze the validity of the German CAPS-5. Furthermore, the internal consistency, test-retest reliability, inter-rater reliability, and diagnostic accuracy of the German CAPS-5 were investigated. Finally, a cut-off score for the German CAPS-5 was calculated using ROC analyses.Results: The study showed good to excellent internal consistency, test-retest reliability, interrater reliability, construct validity, and diagnostic accuracy of the German CAPS-5. Finally, the study revealed a cut-off score for the German CAPS-5 sum score of ≥ 40.Discussion: The German CAPS-5 was found to be a structured diagnostic interview with good to excellent psychometric properties. The results revealed good convergent validity of the German CAPS-5, but more studies are needed into the divergent validity of the German CAPS-5.Trial registration: Trial ID: DRKS00015325 (https://www.drks.de): raw data

Set containing a table, a film and 48 photos illustrating the implementation of the project.

Raw data for a conceptual replication of the Steele & Hayes (1991) match-to-sample procedure.