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.

sheet1 raw data sheet 2 base line sheet3 subgroup raw data sheet4 results of statistical analysis

This study compared the effects of cold water immersion (CWI) and hot water immersion (HWI) on muscle recovery following a muscle-damaging exercise protocol in women. Thirty healthy women (23.3 ± 2.9 years) were randomly assigned to either the CWI, HWI, or control (CON) groups. Participants completed a standardised exercise protocol (5 x 20 drop-jumps), followed by a 10 min recovery intervention (CWI, HWI, or CON) immediately and 120 min post-exercise. Physiological responses, including muscle oxygen saturation (SmO2), core and skin temperature, and heart rate, were assessed at baseline, immediately post-exercise, after the first recovery intervention (postInt), and during 30 min follow-up. Recovery was evaluated through maximal voluntary isometric contraction, muscle swelling, muscle soreness ratings, and serum creatine kinase at baseline, 24, 48, and 72 h post-exercise. A mixed-effects model was used to account for repeated measures over time. Results showed lower SmO2 values in the CWI compared to the HWI group at 20 min (Δ-6.76%, CI: −0.27 to −13.25, p = 0.038) and 30 min (Δ-9.86%, CI: −3.37 to −16.35, p = 0.001), and compared to CON at 30 min (Δ-7.28%, CI: −13.77 to −0.79, p = 0.022). Core temperature was significantly higher in the HWI than the CWI group (postInt and 30 min), while it was significantly lower in the CWI group than CON (30 min). CWI caused a substantial decrease in skin temperature compared to HWI and CON between postInt and 30 min follow-up (all p < 0.001). Skin temperature was higher in the HWI group compared to CON at postInt and throughout 30 min follow-up (all p < 0.001). No significant differences in recovery markers were observed between CWI and HWI groups, although HWI led to slightly higher creatine kinase levels (24 h and 72 h) and greater muscle swelling (24 h) compared to CON. Despite distinct acute physiological responses to CWI and HWI, neither improved subjective or objective recovery outcomes during the 72 h follow-up compared to CON in women following a muscle-damaging exercise protocol.Trial registration numberNCT04902924 (ClinicalTrials.gov), SNCTP000004468 (Swiss National Clinical Trial Portal).

Raw data, analysis tools and other usefull information from arXiv:2104.06814

R code to reproduce the construction of social networks from the raw data, bout analysis, data checking, and randomization-based null models, ;R code to reproduce dyadic and nodal regression analyses; uses the processed data from the aninet R package

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)

This dataset presents the quantitative raw data that was collected under the H2020 RRI2SCALE project for the D1.4 - “Large scale regional citizen surveys report”. The dataset includes the answers that were provided by almost 8,000 participants from 4 pilot European regions (Kriti, Vestland, Galicia, and Overijssel) regarding the general public's views, concerns, and moral issues about the current and future trajectories of their RTD&I ecosystem. The original survey questionnaire was created by White Research SRL and disseminated to the regions through supporting pilot partners. Data collection took place from June 2020 to September 2020 through 4 different waves – one for each region. Based on the conclusion of a consortium vote during the kick-off meeting, it was decided that instead of resource-intensive methods that would render data collection unduly expensive, to fill in the quotas responses were collected through online panels by survey companies that were used for each region. For the statistical analysis of the data and the conclusions drawn from the analysis, you can access the "Large scale regional citizen surveys report" (D1.4).

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

Routine to reproduce Figs 1D–1F, 2B, 2C, 3, 4 and 5 and S1–S6, panel B in S7, and S8 Figs. (ZIP)

File List data.txt Description The "data.txt" file is a tab-delimited text file containing the raw data used in this meta-analysis. Column definitions: interaction ID: unique numeric identification number for each pairwise resource-consumer interaction system: the location of study latitude: latitude of system in degree-minute-seconds longitude: longitude of system in degree-minute-seconds ecosystem type: the broad habitat category (i.e. aquatic, including marine and freshwater subtypes, or terrestrial) ecosystem subtype: the specific habitat category (e.g. temperate forest or freshwater) study type: observational or experimental event: the specific occurrence of a primary resource pulse in time pulse duration (d): the length of time that resource availability was more than 10% greater than the baseline condition in days response duration (d): the length of time that consumer densities or recruitment were more than 10% greater than the baseline condition in days resource: short description of the resource identity consumer: short description of the consumer identity trophic level of resource: the integer trophic level of the dominant pulsed resource (as described in text) consumer trophic level: the integer trophic level of the consumer (as described in text) consumer trophic position: autotrophy or heterotrophy consumer trophic distance: the minimum number of trophic levels between the focal consumer and the primary pulsed resource R baseline, Rb: resource availability in the baseline state R pulse, Rp: maximum resource availability in the pulsed state R units: units of resource availability R ratio: Rp/Rb ln (R ratio): ln(Rp/Rb), resource pulse magnitude C baseline, Cb: consumer density or recruitment in the baseline state C pulse, Cp: maximum consumer density or recruitment in the pulsed state C units: units of consumer density C ratio: Cp/Cb ln(C ratio): ln(Cp/Cb), consumer response magnitude ln(C ratio/R ratio): ln[(Cp/Cb)/(Rp/Rb)], relative response magnitude, measures the magnitude of consumer responses relative to their resource pulses estimated consumer body mass (g): the average mass of the consumer at reproduction estimated consumer generation time (d): the average interval of time between the birth, germination or division of the consumer and the birth, germination or division of their offspring in days response lag (d): the length of time between the observed peak of resource availability and the observed peak consumer density in days consumer response mechanism: the primary mode of numerical response (i.e. reproductive, aggregative, or combined reproductive and aggregative) reference(s): key literature citations, see manuscript notes: additional notes

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/.

Raw data used for analysis

Data analysis raw data in a PDF file

Raw data of survival analysis

Raw dataset and analysis for Figure 4E:Figure 4: AAV9-GFP transduction in kidneys of 8, 10, 12-week-old wild type and Alport mice injected with saline or the high dose (1.65 x 1014 vg/kg). E) The quantification of GFP-positive podocytes (%) is displayed for the two phenotypes WT (left) and Alport mice (right), injected with either saline (light grey bars) or high dose of AVV9-GFP (dark grey bars) (n=2-4 mice per group and per condition). Data are presented as mean ± SEM. P value

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.

Dataset

This dataset was created by Wahab_A

Contents

Dataset

This dataset was created by Siddhesh Shelke

Contents

Raw data files of cell viability assay, gene and protein analysis, statistical analysis and SEM images are uploaded as supporting information.

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