BUSCADOR RECOLECTA

Supplementary Figure S1. Flow chart of inclusion/exclusion criteria for the studied autopsy sample. Supplementary Figure S2. Classification of individual FDG-PET scans based on spatial correlation with pathology-specific patterns. Supplementary Figure S3. Illustration of an alternative stratification approach based on a Gaussian Mixture Model analysis of the ∆r distribution. Supplementary Figure S4. Individual FDG-PET profiles of the two cases with hippocampal sclerosis in the comorbid AD+LATE-NC group. Supplementary Figure S5. FDG-PET patterns of the stratified atypical and non-hypometabolic patient groups in the in-vivo cohort. Supplementary Figure S6. Group-specific trajectories of longitudinal cognitive decline. Supplementary Figure S7. Associations with LATE-NC- vs AD-like FDG-PET pattern expression on a continuous scale. Supplementary Figure S8. Individual hypometabolic profiles of LATE-NC cases. Supplementary Figure S9. IMT ratio in relation to age. Supplementary Figure S10. FDG-PET pattern of age-matched AD group. Supplementary Figure S11. LATE-NC-typical pattern using an age-matched control group. Supplementary Figure S12. FDG-PET patterns of pathologic groups without LATE-NC/AD and other pathologies. Supplementary Table S1. Harvard-Oxford atlas ROIs used for screening for relevant hypometabolism. Supplementary Table S2. Characteristics of LATE-NC-like and AD-like patient groups stratified according to a GMM-based approach. Supplementary Table S3. Characteristics of patients stratified according to the inferior-to-medial temporal metabolism ratio. Supplementary Table S4. Contingency table of group assignments based on pattern matching compared to the inferior-to-medial temporal ratio., TDP-43 assessment and individual staging. ADNI memory and executive function composite scores. Possible neuropathologic correlates of a LATE-NC-like FDG-PET pattern., Peer reviewed

Electronic supplementary information (ESI) available: (1) Neutron reflectivity fitting procedure, (2) NR fitting demonstration of PLL between the ESs, (3) surface pressure and ellipsometry stability measurements, and (4) verification of surface pressure values at high compression ratios., Peer reviewed

3 files. -- Dataset 1: This dataset contains measurements on the atypical song that were analyzed in the manuscript. -- Dataset 2: This dataset contains measurements on nestlings from the Cranberry Lake white-throated sparrow population from 1998-2014, and includes the focal males (FROP; 1541-43272)., Here we provide data for a manuscript in which we describe the atypical song of a male white-throated sparrow (Zonotrichia albicollis). We observed this male over multiple breeding seasons at our Cranberry Lake study site (Adirondack Mountains; New York; 44.15N, 74.78W). We recorded the male singing, and also made observations regarding his failure to obtain reproductive success. In addition, as the male was banded as a nestling, we were able to compare his morphometric measurements at the time to the population average. Our observations of this unique individual support a connection between developmental stress, atypical song, and fitness outcomes., EXPLANATION OF DATASET_WTSP_Atypical_song.docx, FROP_Song_analysis.csv, NestlingLevel1998_2014.csv, Peer reviewed

6. CalRotesPlot_Rainfall-AirTemp-RH-SWC-5min_2021-07_2022-07.xlsx The precipitation data (mm), air temperature data (ºC), relative humidity data (%) and Soil Water Content data (cm3.cm-3) at the Cal Rotes Plot (5 min time step) for the period 07-2021-07-2022., Peer reviewed

Supplemental Data Supplemental Video 1 Supplemental Video 2, Peer reviewed

39 pages. -- Section A: Description of the study area and the sample. -- Table A.1: Annual mean hydrochemical conditions from 2010 to 2020 (data source: LUNG). -- Table A.2: Total tagged pike (per lagoon and sex). -- Figures A.1-A.2. -- Table A.3: of the sample after data filtering and UD estimations. -- Section B: Receiver range estimations. -- Section C: Descriptive statistics. -- Section D: Analysis on weight (including code). -- Table D.1: Stepwise model selection on core area. -- Table D.2: Stepwise model selection on extended range. -- Effects and confidence intervals. -- Table D.3: Stepwise model selection on range.ratio. --Section E: Analysis on length. -- Table E.3: Stepwise model selection on range.ratio. -- Section F: Example Range plots. -- Section G: Monthly effects., Peer reviewed

Document S1. Figures S1–S8, Movie S1 and Supplemental Experimental Procedures., Peer reviewed

Peer reviewed

22 p. -- This PDF file includes: Figs. S1 to S7. -- Tables S1 to S2. -- 6 sections., Peer reviewed

The dataset is made up of 4 files: (1) map.geojson: a GEOJSON file that describes the digital representation of the experimental field where the tests were carried out; (2) mission.geojson: a GEOJSON file describing the geographic coordinates of the planned mission between Building T and Field 3; (3) robot_path.geojson: a GEOJSON file describing the geographic coordinates of the robot performing the mission; (4) RobotLog.txt: a TXT file with the log generated by the robot during the execution of the mission., This database presents the results of the tests developed in the WeLASER experimental farm, setup in the facilities of the Centre for Automation and Robotics (UPM-CSIC). These experiments consisted of autonomous navigation of the WeLASER robot following a complete mission within the farm. This dataset contains the digital representation of the farm, as well as the geographic coordinates that represent the mission. Furthermore, the geographical coordinates that the robot followed during the execution of the mission are also presented, in addition to the log generated by the robot where the different actions and states that the robot performed at each instant of time are were recorded. This for the purpose of promoting transparency, reproducibility, and the sharing of our research findings., This dataset is part of a the WeLASER project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101000256., Peer reviewed