The raw data on behavior and physical fitness. The behavior for sampling worker before joining WE is on sheet behavior 31 and 62 Then, we show all data for behavior and physical fitness.

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.

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/.

The work contains the following underlying dataa. The in vivo pharmacokinetic data for the CAR, PM and the CAR-SCS (F8)b. The ANOVA data generated by the Design Expert softwarec. FTIR raw data for (i) plain CAR (ii) Mannitol (iii) PM and (iv) CAR-SCS (F8)d. DSC raw data for (i) plain CAR (ii) Mannitol (iii) PM and (iv) CAR-SCS (F8)e. XRD raw data for (i) plain CAR (ii) Mannitol (iii) PM and (iv) CAR-SCS (F8)

The research primarily investigates the challenges associated with electrically-driven spin resonance in controlling semiconductor spin qubits, particularly when scaling up to larger systems. The study introduces and evaluates a coherent bichromatic Rabi control method for quantum dot hole spin qubits, aiming to provide a spatially-selective approach for extensive qubit arrays. The findings are supported through a theoretical framework, emphasizing the significance of interdot motion in bichromatic driving. This research is experimental and theoretical in nature. The data was collected with a digitiser through RF-reflectometry by measuring the charge response of a single-hole transistor.

Raw data of survival analysis

The dataset contains data collected as part of the Ancient Adhesives project under the European Union’s Horizon 2020 research and innovation programme Grant Agreement No. 678 804151 (Grant holder G.H.J.L.).

It is being made public to act as supplementary data for a publication and for other researchers to use this data in their own work.

The data in this dataset were collected at TUDelft, University of Cantabria, and Museum of Prehistory and Archaeology of Cantabria in 2023.

This dataset contains:

1. Raw data for SEM-EDX of 11 archaeological objects named: MOR2; MOR4; MOR9; MOR12; MOR13; MOR17; MOR22; MOR35; MOR42; MOR50: MOR59. The file format is .pdf
2. Raw data for micro-Raman of 6 archaeological objects named MOR2; MOR4; MOR9; MOR13; MOR22; MOR59. Raw-data for micro-Raman of 5 experimentally recreated adhesive samples: pine tar, birch tar, pine resin, beeswax, pine resin+beeswax, Ppine resin+beeswax+hematite. The analysis was performed with 532 and 785 laser lines. The file formats are .txt and .csv
3. Raw data for ATR-FTIR of 7 archaeological objects and 1 sediment sample named: MOR11; MOR38; MOR40a; MOR43; MOR44; MOR46; MOR47; MORSediment. The file format is .csv
4. Raw data for GC-MS of 23 archaeological objects (including sub-samples) and 2 sediment samples named: 230404 MOR 1.1.sirslt; 230404 MOR 5.1.sirslt; 230404 MOR 14.1.sirslt; 230404 MOR 24.1.sirslt; 230404 MOR 26.1.sirslt; 230404 MOR 28.1.sirslt; 230404 MOR 32.1.sirslt; 230404 MOR 43.1.sirslt; 230404 MOR 49.1.sirslt; 230404 MOR 57.1.sirslt; 230404 MOR 58.1.sirslt; 230421 MOR 5a.s.sirslt; 230421 MOR 11.1.sirslt; 230421 MOR 40a.3.sirslt; 230421 MOR 45.1.sirslt; 230421 MOR 47.2.sirslt; 230421 MOR 61.1.sirslt; 230421 MOR62.2.sirslt; 20220718 MOR25_1.sirslt; 20220718 MOR25_2.sirslt; 20220718 MOR38_1.sirslt; 20220718 MOR38_2.sirslt; 20220718 MOR40a_1.sirslt; 20220718 MOR40a_2.sirslt; 20220718 MOR47_1.sirslt; 20230316 MOR_41.1.sirslt; 20230316 MOR_44.1.sirslt; 20230316 MOR_46.1.sirslt; 20230316 MOR_60.1.sirslt; 20231024_MOR_SED 10.sirslt. Each.sirslt file contains the files necessary to open and manipulate the data using the original software Agilent OpenLab 2.5. Within each file there is a .DX file (for opening with Agilent OpenLab 2.5) and accompanying .ACAML, .DX, .MFX, .BIN, .RX, .PMX, and .AMX files. In addition, a .xlsx file (Morin GC-MS results.xlsx) is provided. Each sheet contains the complete GC-MS data exported for samples analyzed as well as the MS data and automated molecular data against the National Institute of Standards and Technology (NIST) library.

The acronym MOR stands for Morín Cave, a cave in Cantabria (Spain) where the objects were found.

The data included in this dataset has been organized per method. For each specimen, more than one point was measured as indicated in the file name. Only the measurements with interpretable results are made available.

The file name includes the unique ID of the object + the analytical technique + the number of the scan. For example: MOR11_ATR_loc1

Liquid-chromatography-mass spectrometry (LC-MS) raw data sets from various instruments delivered in their native instrument format. 31 files in all. 7.5 GB data.

Raw Data. analysis tools etc

The dataset contains data collected as part of the Ancient Adhesives project under the European Union’s Horizon 2020 research and innovation programme Grant Agreement No. 678 804151 (Grant holder G.H.J.L.). It is being made public to act as supplementary data for a publication and for other researchers to use this data in their own work.

The dataset includes eight .zip with the raw GC-MS data and one .Xlsx files containing the processed information used in the manuscript "A multi-analytical approach reveals flexible compound adhesive technology at Steenbokfontein Cave, Western Cape ". Each .zip file contains the files necessary to open and manipulate the data using the original software Agilent OpenLab 2.5.

The program collects data for analysis of traffic safety crashes to identify problems, and evaluate countermeasures leading to reducing injuries and property damage resulting from motor vehicle crashes. The FARS dataset contains descriptions, in standard format, of each fatal crash reported. To qualify for inclusion, a crash must involve a motor vehicle traveling a traffic-way customarily open to the public and resulting in the death of a person (occupant of a vehicle or a non-motorist) within 30 days of the crash. Each crash has more than 100 coded data elements that characterize the crash, the vehicles, and the people involved. The specific data elements may be changed slightly each year to conform to the changing user needs, vehicle characteristics and highway safety emphasis areas. The type of information that FARS, a major application, processes is therefore motor vehicle crash data.

X-ray diffraction data stored as flat files in a zip archive.

The dataset includes the results of DNA concentrations, barcode 16S information for DNA sequencing, and data analysis. This dataset is associated with the following publication: Jeon, Y., l. li, M. Bhatia, H. Ryu, J. SantoDomingo, J. Brown, J. Goetz, and y. seo. Impacts of severe harmful algal blooms on bacterial communities in full-scale biological filtration systems for drinking water treatment. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 927: e171301, (2024).

Raw data of the specimens in Insect Collection of Central South University of Forestry and Technology.

Raw data, averaged data, and data analysis code.

Data analysis raw data in a PDF file

These LC-MS and LC-MS/MS raw data were collected for purposes of an interlaboratory study evaluating the multi-attribute method (MAM). Tryptic digests of native NISTmAb (the "Reference" and the "Unknown" samples), degraded NISTmAb (the "pH Stress" sample) and NISTmAb spiked with 15 heavy-labeled synthetic peptides (the "Spike" sample) were sent to each participating laboratory. One injection of each digest was acquired in MS-only mode, while a second injection was acquired in MS/MS mode. Three injections of a mixture of 15 heavy-labeled synthetic peptides ("Calibration" sample) were also analyzed as a means of evaluating instrument performance. Although all data were collected using the same C18 column and LC method, the instrumentation used by each laboratory differed. Additional details regarding the samples, their preparation, the LC method used, and an evaluation of the results pertaining to the new peak detection aspect of MAM can be found in "New Peak Detection Performance Metrics from the MAM Consortium Interlaboratory Study" (https://pubs.acs.org/doi/10.1021/jasms.0c00415). Evaluation of the results pertaining to the attribute analytics aspect of MAM can be found in "Interlaboratory Attribute Analytics Metrics from the MAM Consortium Round Robin Study" (link to be provided). Raw data that was optionally submitted by sixteen participating laboratories are provided here. To preserve the integrity of the data, the files are provided in their original vendor format. Please note that Linux and Mac users may require the use of 7Zip (https://www.7-zip.org/) to extract zipped folders, rather than the extraction tool provided by their operating system. For any difficulty with downloading or extracting data files, please e-mail the contact listed above.

Dataset

This dataset was created by Mohsin Raza

Released under Data files © Original Authors

Contents

Raw Data for Forecast of the Trend in Sales Data of a Confectionery Baking Industry Using Exponential Smoothing and Moving Average Models

Noble gas raw data for the Hawaiian islands of Big Island, Maui, Oahu, Lanai, and Kauai. Based on results from prior phases of the Hawaii Play Fairway Analysis, this project targeted 66 wells on the islands of Hawaii, Maui, Lanai, Oahu, and Kauai for sampling of dissolved noble gases, trace metals, common ions, and the stable isotopes 2H and 18O. Ultimately, 23 of the 66 well targets were sampled. Noble gas data from this study is supplemented with data shared by the United States Geologic Survey for the summit of Kilauea, and by the geothermal energy company Ormat Technologies Inc. for their geothermal power plant Puna Geothermal Venture on the Lower East Rift of Kilauea, and for their exploration of Kona and Hualalai on Hawaii, as well as the Southwest Rift of Haleakala on Maui. The noble gas helium is used as an indicator of geothermal heat when excess 3He and/or 4He is present when compared to the atmospheric ratio of those isotopes (R/Ra). R/Ra is minimally affected by dilution and transport, allowing even those wells not perfectly situated over a geothermal system to indicate a geothermal anomaly. R/Ra anomalies are present on every island in this study. There is a strong correlation between R/Ra anomalies and proximity to rift zones and calderas. The Hawaii Play Fairway project was funded by the U.S. Department of Energy Geothermal Technologies Office (award DE-EE0006729). For more information, see Colin Ferguson's Master of Science thesis "Exploration for Blind Geothermal Resources in the State of Hawaii Utilizing Dissolved Noble Gasses in Well Waters."