Data and code for figures and statistics reported in Midolo, G., De Frenne, P., Hölzel, N. & Wellstein, C. (2019). Global patterns of intraspecific leaf trait responses to elevation.For a complete list reference of studies included in the meta-analysis, see the Supporting Information of our article.See the "csv_file_description" file for detailed information on dataset description and the R code to analyze the data.For any question, please contact me at: gabriele.midolo@natec.unibz.it

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).

A summary of the raw data. Visit https://dataone.org/datasets/sha256%3Ad2b14d6a9da46e707296080c0c4a17242ca7b713e14be24a256c85693535a891 for complete metadata about this dataset.

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

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.

The data for every graph in both the main text and supplementary material is listed within individual sheets. Sheets are labeled by the Figure number and panel. (XLSX)

The Journal of Physiology and British Journal of Pharmacology jointly published an editorial series in 2011 to improve standards in statistical reporting and data analysis. It is not known whether reporting practices changed in response to the editorial advice. We conducted a cross-sectional analysis of reporting practices in a random sample of research papers published in these journals before (n = 202) and after (n = 199) publication of the editorial advice. Descriptive data are presented. There was no evidence that reporting practices improved following publication of the editorial advice. Overall, 76-84% of papers with written measures that summarized data variability used standard errors of the mean, and 90-96% of papers did not report exact p-values for primary analyses and post-hoc tests. 76-84% of papers that plotted measures to summarize data variability used standard errors of the mean, and only 2-4% of papers plotted raw data used to calculate variability. Of papers that reported p-values between 0.05 and 0.1, 56-63% interpreted these as trends or statistically significant. Implied or gross spin was noted incidentally in papers before (n = 10) and after (n = 9) the editorial advice was published. Overall, poor statistical reporting, inadequate data presentation and spin were present before and after the editorial advice was published. While the scientific community continues to implement strategies for improving reporting practices, our results indicate stronger incentives or enforcements are needed.

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).

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).

NA. This dataset is not publicly accessible because: The data used in this manuscript were obtained under Data Use Agreements with the NCS Vanguard Data and Sample Archive and Access System and the NICHD Data and Specimen Hub (DASH). Because of the requirements of the DUA, we are unable to provide raw data; thus, the summary data are provided that are included in the manuscript. It can be accessed through the following means: The manuscript contains tables of the summary statistics. For the original data, users must have an approved DUA with NICHD DASH. Format: Word file of tables with summary statistics for maternal blood Pb, urine Pb, Pb surface wipe loading and Pb vacuum bag dust. This dataset is associated with the following publication: Stanek, L., N. Grokhowsky, B. George, and K. Thomas. Assessing lead exposure in U.S. pregnant women using biological and residential measurements. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, (905): 167135, (2023).

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.

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."

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/.

Research Data and analysis for manuscript "Spectral tuning in mammalian melanopsins" by McDowell et al. (2025) Molecular Biology and Evolution https://doi.org/10.1093/molbev/msaf158

Sheet 1 (Raw-Data): The raw data of the study is provided, presenting the tagging results for the used measures described in the paper. For each subject, it includes multiple columns: A. a sequential student ID B an ID that defines a random group label and the notation C. the used notation: user Story or use Cases D. the case they were assigned to: IFA, Sim, or Hos E. the subject's exam grade (total points out of 100). Empty cells mean that the subject did not take the first exam F. a categorical representation of the grade L/M/H, where H is greater or equal to 80, M is between 65 included and 80 excluded, L otherwise G. the total number of classes in the student's conceptual model H. the total number of relationships in the student's conceptual model I. the total number of classes in the expert's conceptual model J. the total number of relationships in the expert's conceptual model K-O. the total number of encountered situations of alignment, wrong representation, system-oriented, omitted, missing (see tagging scheme below) P. the researchers' judgement on how well the derivation process explanation was explained by the student: well explained (a systematic mapping that can be easily reproduced), partially explained (vague indication of the mapping ), or not present.

Tagging scheme:
Aligned (AL) - A concept is represented as a class in both models, either

with the same name or using synonyms or clearly linkable names; Wrongly represented (WR) - A class in the domain expert model is incorrectly represented in the student model, either (i) via an attribute, method, or relationship rather than class, or (ii) using a generic term (e.g., user'' instead ofurban planner''); System-oriented (SO) - A class in CM-Stud that denotes a technical implementation aspect, e.g., access control. Classes that represent legacy system or the system under design (portal, simulator) are legitimate; Omitted (OM) - A class in CM-Expert that does not appear in any way in CM-Stud; Missing (MI) - A class in CM-Stud that does not appear in any way in CM-Expert.

All the calculations and information provided in the following sheets

originate from that raw data.

Sheet 2 (Descriptive-Stats): Shows a summary of statistics from the data collection,

including the number of subjects per case, per notation, per process derivation rigor category, and per exam grade category.

Sheet 3 (Size-Ratio):

The number of classes within the student model divided by the number of classes within the expert model is calculated (describing the size ratio). We provide box plots to allow a visual comparison of the shape of the distribution, its central value, and its variability for each group (by case, notation, process, and exam grade) . The primary focus in this study is on the number of classes. However, we also provided the size ratio for the number of relationships between student and expert model.

Sheet 4 (Overall):

Provides an overview of all subjects regarding the encountered situations, completeness, and correctness, respectively. Correctness is defined as the ratio of classes in a student model that is fully aligned with the classes in the corresponding expert model. It is calculated by dividing the number of aligned concepts (AL) by the sum of the number of aligned concepts (AL), omitted concepts (OM), system-oriented concepts (SO), and wrong representations (WR). Completeness on the other hand, is defined as the ratio of classes in a student model that are correctly or incorrectly represented over the number of classes in the expert model. Completeness is calculated by dividing the sum of aligned concepts (AL) and wrong representations (WR) by the sum of the number of aligned concepts (AL), wrong representations (WR) and omitted concepts (OM). The overview is complemented with general diverging stacked bar charts that illustrate correctness and completeness.

For sheet 4 as well as for the following four sheets, diverging stacked bar

charts are provided to visualize the effect of each of the independent and mediated variables. The charts are based on the relative numbers of encountered situations for each student. In addition, a "Buffer" is calculated witch solely serves the purpose of constructing the diverging stacked bar charts in Excel. Finally, at the bottom of each sheet, the significance (T-test) and effect size (Hedges' g) for both completeness and correctness are provided. Hedges' g was calculated with an online tool: https://www.psychometrica.de/effect_size.html. The independent and moderating variables can be found as follows:

Sheet 5 (By-Notation):

Model correctness and model completeness is compared by notation - UC, US.

Sheet 6 (By-Case):

Model correctness and model completeness is compared by case - SIM, HOS, IFA.

Sheet 7 (By-Process):

Model correctness and model completeness is compared by how well the derivation process is explained - well explained, partially explained, not present.

Sheet 8 (By-Grade):

Model correctness and model completeness is compared by the exam grades, converted to categorical values High, Low , and Medium.

Name: Data used to rate the relevance of each dimension necessary for a Holistic Environmental Policy Assessment.

Summary: This dataset contains answers from a panel of experts and the public to rate the relevance of each dimension on a scale of 0 (Nor relevant at all) to 100 (Extremely relevant).

License: CC-BY-SA

Acknowledge: These data have been collected in the framework of the DECIPHER project. This project has received funding from the European Union’s Horizon Europe programme under grant agreement No. 101056898.

Disclaimer: Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.

Collection Date: 2024-1 / 2024-04

Publication Date: 22/04/2025

DOI: 10.5281/zenodo.13909413

Other repositories: -

Author: University of Deusto

Objective of collection: This data was originally collected to prioritise the dimensions to be further used for Environmental Policy Assessment and IAMs enlarged scope.

Description:

Data Files (CSV)

decipher-public.csv : Public participants' general survey results in the framework of the Decipher project, including socio demographic characteristics and overall perception of each dimension necessary for a Holistic Environmental Policy Assessment.

decipher-risk.csv : Contains individual survey responses regarding prioritisation of dimensions in risk situations. Includes demographic and opinion data from a targeted sample.

decipher-experts.csv : Experts’ opinions collected on risk topics through surveys in the framework of Decipher Project, targeting professionals in relevant fields.

decipher-modelers.csv: Answers given by the developers of models about the characteristics of the models and dimensions covered by them.

prolific_export_risk.csv : Exported survey data from Prolific, focusing specifically on ratings in risk situations. Includes response times, demographic details, and survey metadata.

prolific_export_public_{1,2}.csv : Public survey exports from Prolific, gathering prioritisation of dimensions necessary for environmental policy assessment.

curated.csv : Final cleaned and harmonized dataset combining multiple survey sources. Designed for direct statistical analysis with standardized variable names.

Scripts files (R)

decipher-modelers.R: Script to assess the answers given modelers about the characteristics of the models.

joint.R: Script to clean and joint the RAW answers from the different surveys to retrieve overall perception of each dimension necessary for a Holistic Environmental Policy Assessment.

Report Files

decipher-modelers.pdf: Diagram with the result of the

full-Country.html : Full interactive report showing dimension prioritisation broken down by participant country.

full-Gender.html : Visualization report displaying differences in dimension prioritisation by gender.

full-Education.html : Detailed breakdown of dimension prioritisation results based on education level.

full-Work.html : Report focusing on participant occupational categories and associated dimension prioritisation.

full-Income.html : Analysis report showing how income level correlates with dimension prioritisation.

full-PS.html : Report analyzing Political Sensitivity scores across all participants.

full-type.html : Visualization report comparing participant dimensions prioritisation (public vs experts) in normal and risk situations.

full-joint-Country.html : Joint analysis report integrating multiple dimensions of country-based dimension prioritisation in normal and risk situations. Combines demographic and response patterns.

full-joint-Gender.html : Combined gender-based analysis across datasets, exploring intersections of demographic factors and dimensions prioritisation in normal and risk situations.

full-joint-Education.html : Education-focused report merging various datasets to show consistent or divergent patterns of dimensions prioritisation in normal and risk awareness.

full-joint-Work.html : Cross-dataset analysis of occupational groups and their dimensions prioritisation in normal and risk situation

full-joint-Income.html : Income-stratified joint analysis, merging public and expert datasets to find common trends and significant differences during dimensions prioritisation in normal and risks situations.

full-joint-PS.html : Comprehensive Political Sensitivity score report from merged datasets, highlighting general patterns and subgroup variations in normal and risk situations.

5 star: ⭐⭐⭐

Preprocessing steps: The data has been re-coded and cleaned using the scripts provided.

Reuse: NA

Update policy: No more updates are planned.

Ethics and legal aspects: Names of the persons involved have been removed.

Technical aspects:

Other:

**Student Mental Health Survey: Scaled Data on IT Students' Academic and Emotional Well-being ** **Overview **This dataset contains survey responses from IT students, focusing on academic stress, mental health, and lifestyle factors. It includes two files that capture different stages of data preparation to suit various analytical needs.

Files Included MentalHealthSurvey.csv:

Description: Contains the original survey data with raw categorical and numerical variables. Usefulness: Ideal for initial data exploration and understanding the unprocessed patterns before any data transformation. MentalHealthSurvey_Cleaned.csv:

Description: This file contains cleaned and preprocessed data with scaled numerical variables. The data was scaled using standard scaling techniques, which adjust the values so that each variable has a mean of 0 and a standard deviation of 1. Why Scaling is Useful: Scaling ensures that all numerical variables contribute equally to statistical models, particularly in factor analysis, where varying scales can skew the results. Scaled data improves model performance, stability, and interpretability, making it especially valuable for advanced analyses like predictive modeling and machine learning. Applications Initial Data Exploration: Use the raw data to explore variable distributions, correlations, and identify potential data quality issues. Advanced Analysis: The cleaned and scaled data is optimal for statistical analysis, helping to uncover meaningful patterns and insights into the factors affecting students' mental health and academic performance. Both files offer a complete view of the dataset, from raw data exploration to scaled data ready for rigorous analysis.

Using N-body simulations, we test and show the sesquinary catastrophe as a mechanism to reconcile Deimos' dynamically excited past with its cooler present. This work answers an important question about the origin of Deimos.

Raw data used for analysis

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