BUSCADOR RECOLECTA

Additional file 3: Supplementary Table 1. Characteristics of the study participants with information for cognition outcomes. Mean and SD are shown for continuous variables. Supplementary Table 2. Characteristics of the study participants with information for neuroimaging outcomes. Mean and SD are shown for continuous variables. Supplementary Table 3. Characteristics of the study participants with information for CSF biomarkers. Mean and SD are shown for continuous variables., Peer reviewed

Additional file 4: Supplementary Table 1. Results of the effect of genetically predicted longer telomere length on AD endophenotypes in the entire sample. Supplementary Table 2. Results of the effect of genetically predicted longer telomere length on AD endophenotypes among APOE-ɛ4 carriers. Supplementary Table 3. Results of the effect of genetically predicted longer telomere length on AD endophenotypes among APOE-ɛ4 non-carriers. Supplementary Table 4. Results of the effect of genetically predicted longer telomere length on AD endophenotypes among individuals at high genetic predisposition to Alzheimer's disease. Supplementary Table 5. Results of the effect of genetically predicted longer telomere length on AD endophenotypes among individuals at low genetic predisposition to Alzheimer's disease., Peer reviewed

Appendix A. The texts and cognitive tests Appendix B. Additional data description Appendix C. Additional analysis, All the analyses have been made with STATA 15. The data are organized in two separate datasets, one for the laboratory experiment (Study 1) and the second for the field experiment (Study2.dta), Peer reviewed

Additional file 5: Supplementary Figure 1. Leave-one-out permutation analysis plot for AD signature among individuals at high genetic predisposition to AD obtained by leaving out the SNP indicated and repeating the Inverse-Variance Weighted method with the rest of the instrumental variables. Supplementary Figure 2. Leave-one-out permutation analysis plot for Aging signature among individuals at high genetic predisposition to AD, obtained by leaving out the SNP indicated and repeating the Inverse-Variance Weighted method with the rest of the instrumental variables. Supplementary Figure 3. Leave-one-out permutation analysis plot for Aβ ratio among APOE-ɛ4 non-carriers obtained by leaving out the SNP indicated and repeating the Inverse-Variance Weighted method with the rest of the instrumental variables. Supplementary Figure 4. Leave-one-out permutation analysis plot for NfL among APOE-ɛ4 non-carriers obtained by leaving out the SNP indicated and repeating the Inverse-Variance Weighted method with the rest of the instrumental variables. Supplementary Figure 5. Leave-one-out permutation analysis plot for p-tau among individuals at high genetic predisposition to AD, obtained by leaving out the SNP indicated and repeating the Inverse-Variance Weighted method with the rest of the instrumental variables., Peer reviewed

Contents: • Online Appendix A: Course structure and screenshots of the final exam • Online Appendix B: Subject pool information • Online Appendix C: Analysis of order effect for individual questions • Online Appendix D: Instant order effects • Online Appendix E: Analysis of informativeness of exam grades • Online Appendix F: Additional figures • Online Appendix G: Exam questions • References, Peer reviewed

This dataset is a valuable component of the article titled "Monitoring Montane-Subalpine Forest Ecotone in the Pyrenees: Integrating Sequential Forest Inventories and Landsat Imagery." The dataset provides comprehensive information necessary for implementing the analysis and models described. These analyses encompass studying the variations in Abies alba Mill. and Pinus uncinata Ramond. basal area and Replacement Index over three reference years (1991, 2002, and 2015). Additionally, the study applies linear mixed-effects models, considering altitude, aspect, total basal area, year, and protection level (National Park vs. protection buffer zone) as fixed effects, and plot as a random effect. By utilizing the reflectance values from the Landsat composites of 1991, 2002, and 2015, a Support Vector Machine binary classifier can be trained using presence/absence indicators for A. alba and P. uncinata, enabling the prediction of species’ distribution throughout the entire study area. All methods are thoroughly described in the manuscript., This work was supported by the Spanish Ministry of Science and Innovation (formerly Ministry of Economy, Industry, and Competitiveness) through the FPI program (BES-2017-081606), and the AGL2016-76769-C2-1-R and PID2020-119204RB-C21 project and by the National Parks Autonomous Agency (Spanish Ministry for the Ecological Transition and the Demographic Challenge) through the project 2481S/2017 OLDFORES., DatabaseBands.xls 1989_1993_composite_stack_series.tif; 1989_1993_composite_stack_series.tfw; 2001_2005_composite_stack_series.tif; 2001_2005_composite_stack_series.tfw; 2014_2016_composite_stack_series.tif; 2014_2016_composite_stack_series.tfw, Peer reviewed

A Data Appendix A.1 Data Access and Data Sources A.1.1 INEP Microdata A.1.2 SISU Data A.1.3 Affirmative Action Quotas Data A.2 Data Description A.2.1 Student-level data A.2.2 Institution-level data A.2.3 Program-level data B Self-declared Data B.1 Variable “Public-school Student (PS)” B.2 Variable “Public-school Non-white Student (PSNW)” B.3 Variable “Public-school Low-income Student (PSLI)” C Missing Data and Sample Selection D Replicability: Results at Program Level E Affirmative Action Treatment E.1 Ethnic versus Non-Ethnic Quotas E.2 Local Supply of PS and PSNW Students E.3 Strategic High School Choice F Heterogeneity F.1 By Initial Share of Enrollments of Low-SES Students F.2 Persistence G Robustness G.1 Spillovers and SUTVA G.2 Robustness of Out-of-state Students’ Outcome H Additional Figures and Tables, Peer reviewed

72 pages. -- Tables and supplementary methods., Peer reviewed

Supplementary Table 1. List of Tara Oceans samples sequenced with a metabarcoding (18S V9) approach and with a metagenomic approach, including identifiers for sequencing reads deposited in the DDBJ/ENA/GenBank Short Read Archives (SRA). [This Table is identical in version 2.] Supplementary Table 2. Table of environmental parameters for each sample. [This Table is identical in version 2.] Supplementary Table 3. Matrix of metagenomic dissimilarity for the 0-0.22 μm size fraction. [This Table is identical in version 2.] Supplementary Table 4. Matrix of metagenomic dissimilarity for the 0.22-1.6/3 μm size fraction. [This Table is identical in version 2.] Supplementary Table 5. Matrix of metagenomic dissimilarity for the 0.8-5 μm size fraction. [This Table is identical in version 2.] Supplementary Table 6. Matrix of metagenomic dissimilarity for the 5-20 μm size fraction. [This Table is identical in version 2.] Supplementary Table 7. Matrix of metagenomic dissimilarity for the 20-180 μm size fraction. [This Table is identical in version 2.] Supplementary Table 8. Matrix of metagenomic dissimilarity for the 180-2000 μm size fraction. [This Table is identical in version 2.] Supplementary Table 9. Matrix of OTU dissimilarity for the 0-0.22 μm size fraction. [This Table is identical in version 2.] Supplementary Table 10. Matrix of OTU dissimilarity for the 0.22-1.6/3 μm size fraction. [This Table is identical in version 2.] Supplementary Table 11. Matrix of OTU dissimilarity for the 0.8-5 μm size fraction. [This Table is identical in version 2.] Supplementary Table 12. Matrix of OTU dissimilarity for the 5-20 μm size fraction. [This Table is identical in version 2.] Supplementary Table 13. Matrix of OTU dissimilarity for the 20-180 μm size fraction. [This Table is identical in version 2.] Supplementary Table 14. Matrix of OTU dissimilarity for the 180-2000 μm size fraction. [This Table is identical in version 2.] Supplementary Table 15. Matrix of minimum travel time, in years. [This Table is identical in version 2.] Supplementary Table 16. Matrix of minimum geographic distance (without traversing land), in kilometers. [This Table is identical in version 2.] Supplementary Table 17. Matrix of imaging-based dissimilarity. [This Table is identical in version 2.] Supplementary Table 18. Matrix of metagenome-assembled genome (MAG)-based dissimilarity for the 20-180 μm size fraction. [The filename of this Table was modified from version 2. The contents of the Table are identical.] Supplementary Table 19. The cophenetic correlation coefficient for different methods of clustering metagenomic dissimilarity. [This Table is identical in version 2.] Supplementary Table 20. Baker's Gamma index comparing clustering results within size fractions. [This Table is identical in version 2.] Supplementary Table 21. Rand Index for K-means and spectral clustering, and multivariate ANOVA calculated by the adonis function. [This Table is identical in version 2.] Dataset 1. Reference database (in FASTA format) used to perform taxonomic assignment of metabarcodes. The header line of each reference V9 rDNA barcode (with a > sign) contains a unique identifier derived from GenBank accession number, followed by the taxonomic path associated to the reference barcode. [This Dataset is identical in version 2.] Dataset 2. V9 rDNA abundance at the metabarcode level. md5sum = unique identifier; totab = total abundance across all samples; cid = identifier of the OTU to which the barcode belongs (see Dataset 3); pid = best percentage identity to a barcode in Dataset 1; refs = identifier(s) of the best matching barcode(s) in Dataset 1; lineage = taxononmic lineage of the best match in Dataset 1; taxogroup = high-level taxonomic grouping of the best match in Dataset 1; sequence = V9 rDNA sequence; TV9_XXX = barcode abundance by sample (see Supplementary Table 1 for sample identifiers). [This Dataset is identical in version 2.] Dataset 3. V9 rDNA abundance at the OTU (operational taxonomic unit) level. cid = identifier of the OTU; md5sum = unique identifier of the most abundant barcode in the OTU; pid, refs, lineage, taxogroup, sequence = defined as in Dataset 2; rtotab = total abundance of the most abundant barcode in the OTU; ctotab = total abundance of all barcodes in the OTU; TV9_XXX = abundance by sample of all barcodes in the OTU (see Supplementary Table 1 for sample identifiers). [This Dataset is identical in version 2.] Dataset 4. Relative abundances of metagenome-assembled genomes (MAGs) in metagenomic samples from the 20-180 μm size fraction. [This Dataset is new in version 3.], Biogeographical studies have traditionally focused on readily visible organisms, but recent technological advances are enabling analyses of the large-scale distribution of microscopic organisms, whose biogeographical patterns have long been debated. Here we assessed the global structure of plankton geography and its relation to the biological, chemical and physical context of the ocean (the 'seascape') by analyzing metagenomes of plankton communities sampled across oceans during the Tara Oceans expedition, in light of environmental data and ocean current transport. Using a consistent approach across organismal sizes that provides unprecedented resolution to measure changes in genomic composition between communities, we report a pan-ocean, size-dependent plankton biogeography overlying regional heterogeneity. We found robust evidence for a basin-scale impact of transport by ocean currents on plankton biogeography, and on a characteristic timescale of community dynamics going beyond simple seasonality or life history transitions of plankton., Peer reviewed

Explanation note: Deatails on colour measurements (methodology, processing) and generated data are given., Wing colour is an important trait for identification of butterflies and a species-specific characteristic (Bálint et al. 2012), an indicator of genetic variation (Wasik et al. 2014), and evidence of a changing population (Kertész et al. 2021). Observing fixed differences in wing colour of butterflies of different population can serve as a reliable tool for taxonomists for taxonomical identification (Bálint et al. 2010). Here we used colour measurements of dorsal wings of male Agrodiaetus to generate standardized RGB measurements of set specimens., Peer reviewed