BUSCADOR RECOLECTA

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 data: Table S2. Electrophysiological Properties of ACC PMd-targeting and AMY-targeting pyramidal neurons. Supplementary Figure S1. Dendritic reconstructions of tracer-labeled neurons. Representative 3D reconstructions of AMY-targeting and PMd -targeting projection neurons in layers 3 and 5. Supplementary Figure S2. Effects of firing frequency adaption to synchrony of distinct ACC networks. Supplementary Figure S3. Representative plots of simulated low and high stochastic drive (IsFigure S4. Simulated output of networks with and without inhibition and recurrent excitation to pyramidal neurons. Ctoch)., Peer reviewed

These files contain the programs and the public data for the journal article: "Centralized Admissions, Affirmative Action and Access of Low-income Students to Higher Education", American Economic Journal: Economic Policy., Barcelona Graduate School of Economics (Severo Ochoa Program for Centers of Excellence in RD - SEV2015-0563, CEX2019-000915-S); Institute for Economic Analysis (2017 SGR 1359 Secretaria d’Universitats i Recerca-Generalitat de Catalunya), With funding from the Spanish government through the "Severo Ochoa Centre of Excellence" accreditation (CEX2019-000915-S)., Peer reviewed

This dataset contains a single Excel file listing all information of the fungal culture collection generated in the Mycospitalomics Project. This culture collection, preserved at IRNAS-CSIC (Seville), includes 504 fungal isolates from diverse environmental samples (air, surface and dust) collected in 2022-2023 from three study hospitals: Virgen del Rocio hospital in Seville (37°21′42″N 5°58′50″O), La Fe hospital in Valencia (39°26′37″N 0°22′32″O) and Severo Ochoa hospital in Leganés, Madrid (40°19′14″N 3°46′09″O)., Funded Ministerio de Ciencia e Innovación (España); Agencia Estatal de Investigación (España) and European Commission, No

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

The Bayesian analysis and NJ analysis based on the concatenated matrix COI+ITS2. The NJ analysis was performed using with Tamura3-parameter+G (Kumar et al., 2018) as the optimal model. The Bayesian analysis was performed using the program MrBayes 3.2 (Ronquist et al. 2012) with default settings. See main text, Methods section, for detailed methodology description., Peer reviewed

8 pages. -- The file includes 3 tables and 5 figures. -- Table S1. Mean and standard error of the concentrations (mg of tartaric acid equivalents/g DW of plant) and percentages of specialized compounds and nutritional traits. -- Table S2. Results of Kruskal Wallis and Dunn tests on C and N of adult seagrass leaves and flowers (n=6), and seedling leaves (n=10). SD indicates seedling leaves, Finflorescences, L1 young leaves and L3 old leaves. In Dunn test, parentheses indicate that these tissues are not statistically different to the ones next to them. -- Table S3 Loading values of PCA from figure 2. -- Figure S1. Concentration (mg chicoric acid equivalents/g DW of plant) of total phenolic compounds expressed as the set of specific phenolic compounds from the different tissues analyzed; Inflorescences (F), young leaves (L1) and old leaves (L3) from non-reproductive plants, and young leaves (RL1) and old leaves (RL3) from reproductive plants, and seedling leaves (SD). Compounds labelled as 1 (chicoric acid), 2 (caffeoylferuloyltartaric acid), 5 (di-p-coumaroyltartaric acid,), 4 (coutaric acid,), and 3 (p-coumaroylcaffeoyltartaric acid). Error bars indicate standard error of total content of phenolic compounds. For F, L1, L3, RL1, RL3 n=6 for SD n=10. -- Figure S2. Pairwise correlation matrix of total phenolic compounds (TPC), C/N, C, N, 1 (chicoric acid), 2 (caffeoylferuloyltartaric acid), 3 (p-coumaroycaffeoyltartaric acid), 4 (coutaric acid), and 5 (di-p-coumaroyltartaric acid) from all analyzed tissues (inflorescences, young leaves, old leaves and leaves from seedlings). Numbers indicate Pearson correlation coefficients. -- Figure S3. Scheme of (A) adult Posidonia oceanica in reproductive stage and (B) seedling showing the different parts.-- Figure S4. Extracted ion chromatograms for compounds (1-5) and their MS/MS spectra. -- Figure S5. Diagnostic MS/MS fragmentation for compounds (1-5), Peer reviewed

Supplemental Table 1. Detailed biological and demographic features of patients. Supplemental Table 2: Composition of the EuroFlow B-CLPD panel Supplemental Table 3. Overview on the data analysis strategy within the scope of the main study Supplemental Table 4. Canonical coefficients for CA1 and CA2. Significance of contribution of individual parameters to the canonical axes CA1 and CA2 by differential diagnosis. V Supplemental Table 5. SD lines utilized as decision criterion per pair-wise differential diagnosis Supplemental Table 6. Medians (10th – 90th 309 percentile) of medFIs and of BT ratio, 3respectively, by parameter and entity (see Figure 3 for corresponding box plots) Supplemental Table 7. Monte Carlo cross-validation results Supplemental Table 8. Cases rejected prior to study inclusion Supplemental Table 9: Markers representing predominantly background signal (BS) by entity, Peer reviewed