BUSCADOR RECOLECTA

Global Extra-tropical Circulation Database based on the Jenkinson-Collison Classification calculated with 6-hourly mean sea-level pressure fields from several reanalysis datasets. This dataset is the result of an extension of the Jenkinson-Collison circulation type classification to the entire globe, including a modification of its original formulation for the southern hemisphere., Peer reviewed

Document S1. Methods S1. Document S2. Figures S1–S4., Peer reviewed

In this document are included the raw and treated scientific data associated with the publication: A. Lancelot, M. E. Meger, E. Guerreiro Gómez, T. Sierra, J. J. Wilker, Adhesion of Catechol-Functionalized Linear-Dendritic Block Copolymers: Dendritic Effect, Self-Assembly, and Bioadhesion. Adv. Funct. Mater. 2024, 2413398. https://doi.org/10.1002/adfm.202413398, Adhesion data: - Adhesion data point file in csv file - Adhesion curve in pdf file - Calculations for adhesion mean value and standard deviation, Chemical characterization: - FTIR spectrum in csv file - 1H NMR spectrum in mnova file - 1H NMR spectrum in pdf file - SEC data point file in csv file, DSC curves: - DSC curves and derivatives in xlsx file, Microscopy images: - 5 representative cryoSEM images of the hydrogel in tif file - 5 representative cryoTEM images of the aggregates in tif file - 3 representative SEM images of the aggregates in tif file, Viscosity curves: - viscosity curves in xlxs file, Peer reviewed

Links to other publicly accessible locations of the data: https://www.videpi.com/ (in original scanned paper format).-- https://www.videpi.com/videpi/cessati/sismica_titoli.asp, The dataset include five seismic reflection profiles, collected for commercial exploration during the '80s, which have been selected across "The Alto Tiberina Near Fault Observatory area (NFO TABOO)". The source data is currently available in the Italian repository VIDEPI (videpi.com) in pdf format, scanned from the original paper copies of the seismic profiles. Such format makes the data often difficult to be Found and Accesses, as well as making them not Inter-operable and Re-usable, as recommended by FAIR policy. This policy characterizes the EPOS infrastructure (https://www.epos-eu.org/dataportal), funder of such a digitalization pilot project accomplished by UniPG, which is also collaborating with the CSIC and other institutions in the framework of EPOS to make active seismic profiles publicy available in this portal. The new digitized data will enable the users to re-use them to enhance their quality and interpretability, as well as to better integrated them in the geological studies related to the NFO TABOO (https://taboo.ingv.it/)., European Plate Observing System (EPOS), Title: "Un nuovo data-base integrato e fruibile di dati geologici e geofisici di sottosuolo per i Near Fault Observatories italiani: il caso pilota “TABOO”. Obiettivo 2 - Piano Esecutivo 2024 della JRU EPOS ITALIA., PER-7EXT-83_gpkg.zip (Geopackage files EPSG:32633, profile track and shot points location) PER-7EXT-83.tif PER-7EXT-83.SGY (digitized seismic profile) PER-7EXT-83_aux (auxiliary data: acquisition and processing information) PER-7EXT-83_ancillary.zip (ancillary data: elevation and datum correction) PER-8-83_gpkg.zip (Geopackage files EPSG:32633, profile track and shot points location) PER-8-83.tif PER-8-83.SGY (digitized seismic profile) PER-8-83_aux (auxiliary data: acquisition and processing information) Per-8-83_ancillary.zip (ancillary data: elevation and datum correction) PER-9-83_gpkg.zip (Geopackage files EPSG:32633, profile track and shot points location) PER-9-83.tif PER-9-83.SGY (digitized seismic profile) PER-9-83_aux (auxiliary data: acquisition and processing information) Per-9-83_ancillary.zip (ancillary data: elevation and datum correction) PG-301-81V-stack_gpkg.zip (Geopackage files EPSG:32633, profile track and shot points location) PG-301-81v.tif PG-301-81V.SGY (digitized seismic profile) PG-301-81V-stack_aux (auxiliary data: acquisition and processing information) AN-334-84-stack_gpkg-zip (Geopackage files EPSG:32633, profile track and shot points location) AN-334-84.tif AN-334-84-stack_aux (auxiliary data: acquisition and processing information) AN-334-84.SGY (digitized seismic profile), Peer reviewed

[Description of the data and file structure] 01_Conventional_vs_INIS_Qualitative.xlsx and 01_Conventional_vs_INIS_Quantitative.xlsx contain the values of Interaction turnover and its components estimated in 20 natural communities in Mallorca Island (Gómez-Martínez et al. 2022, Lázaro et al. 2022), following the Conventional or the INIS approach, and using Qualitative or Quantitative data. Description of each variable within the file.-- 02_SamplingEffort_Interannual.xlsx contains the values of Interaction turnover and its components between plant-pollinator interaction networks of 2016 and 2017 in Son Bosc, Mallorca Island (Lázaro et al. 2020), with an increasing sampling effort estimated by accumulating sampling days. Description of each variable within the file.-- 02_SamplingEffort_Seasonal.xlsx contains the values of Interaction turnover and its components between plant-pollinator interaction networks of different sampling days in Son Bosc, Mallorca Island (Lázaro et al. 2020), with an increasing sampling effort estimated by accumulating minutes of sampling. Description of each variable within the file.-- 03_SamplingCompleteness.xlsx contains the rewiring events of species (pollinators and plants) in 2016 and 2017 in Son Bosc, Mallorca Island (Lázaro et al. 2020) following the Conventional and the INIS approach, and the sampling completeness of interactions for each species. Description of each variable within the file., Understanding temporal dynamics in ecological networks is crucial to predicting their capability to cope with global changes. Despite this, proper quantification of network dynamics still remains a challenge. Temporal dynamics are typically studied using data of interaction networks over time, through the evaluation of interaction turnover and its two components: changes related to species turnover (species gains and losses) or rewiring (switching partners among the set of species shared over time). However, with this approach based exclusively on network data, dynamics are computed similarly for species that are truly missing from the community at a given temporal period, and for species occurring in the community but that do not interact with any other. This might lead to an underestimation of the real extent of rewiring, while overestimating the species turnover component of interaction turnover. We used data on 20 plant-pollinator communities to calculate interaction turnover components accounting also for the species that occurred in the communities at different temporal periods but did not appear in some of the temporal interaction networks (non-interacting species), and then compared these estimates with conventional ones. Besides, we used empirical data and simulations to evaluate the extent to which dynamics estimates were affected by sampling effort when including and excluding non-interacting species. As expected, disregarding the non-interacting species that occur in the communities at different temporal periods leads to the underestimation of rewiring and the overestimation of species turnover as components of interaction turnover. Effect size was moderate when independent pollinator data were included, and large when including plants or both trophic levels. Simulations indicated that, in general, considering the non-interacting species reduced biases at the time of identifying changes due to the different interaction turnover components. Accounting for non-interacting species was particularly important to reduce bias when sampling effort was low and when dynamics were calculated seasonally. Despite sampling effort effects, phenology was the main determinant of species’ rewiring frequencies. Our approach contributes to reducing biases and improving the estimates of interaction flexibility in networks, which are necessary to comprehend the response of communities in the face of global change., Spanish Ministry of Economy and Competitiveness, Spanish Research Agency & European Social Funds: PRE2018-083185. Spanish Ministry of Science, Innovation and Universities, Spanish Research Agency, European Social Funds (ESF invests in your future) & University of the Balearic Islands: RYC-2015-19034. Spanish Ministry of Science and Innovation, Feder Funds & Spanish Research Agency: CGL2017-89254-R. Spanish Ministry of Science and Innovation & Spanish Research Agency: MCIN/AEI/10.13039/501100011033., Peer reviewed

Under a Creative Commons license CC-BY 3.0, https://creativecommons.org/licenses/by/3.0/, Tables: Table S1. Experimental campaign and detailed GC characterization of the light fraction.--Table S2. Experimental campaign and detailed GC characterization of the heavy fraction.-- Tale S3. BTEX concentrations reported in the literature from the pyrolysis of ELTs.-- Table S4. Elemental and calorific analyses of the resulting heavy fraction.-- Table S5. GC/MS results of the resulting heavy fraction.-- Figures: Fig. S1. Steady-state temperature profile of the column (run # 1).-- Fig. S2. Steady-state temperature profile of the column (runs 2 and 7).-- Fig. S3. Steady-state temperature profile of the column (runs 3 and 8).-- Fig. S4. Steady-state temperature profile of the column (runs 4 and 9).-- Fig. S5. Steady-state temperature profile of the column (runs 5 and 10).-- References, This work is part of the BLACKCYCLE project (For the circular economy of tyre domain: recycling end of life tyres into secondary raw materials or tyres and other product applications) which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 869625. The authors would also like to thank the Regional Government of Aragon (DGA) for the support provided under the research groups support program and CSIC for the interdisciplinary thematic platforms SusPlast (Sustainable Plastics) and SosEcoCir (Sustainability and Circular Economy)., Peer reviewed

Figure S1: FESEM images of the as-synthesized TiO2 nanorods; Figure S2: screenshots of the motion of TiO2/NiFe nanomotors under different light off/on cycles; Figure S3: magnetic experimental set up; Figure S4: MSD and diffusion coefficients of TiO2/NiFe nanomotors with different alloy thickness; Figure S5: representative individual trajectories and the corresponding average MSD for the nanomotors in the dark under SMF and WMF; Figure S6: PL spectrum of TiO2 with a Rutile crystalline phase; Figure S7: representative schematic illustration of the experimental setup used for PL and EPR measurements., Video S1: motion of TiO2/NiFe nanomotors under light off/on conditions. Video S2: motion of TiO2/NiFe nanomotors at different light intensities. Video S3: motion of TiO2/NiFe nanomotors under long-term irradiation. Video S4: motion of TiO2/NiFe nanomotors under acidic and basic conditions. Video S5: motion of TiO2/NiFe nanomotors under light in combination with magnetic fields., Peer reviewed

Figures: -S1 Fatband analysis for P4W12O44 showing the contribution of the W t2g and eg orbitals. -S2 Comparison of the crystal structures of η-Mo4O11 (a) and γ-Mo4O11 (b). -S3 Lindhard response function calculated for P4W12O44 in the (a*,b*) reciprocal plane at 300 K. Note the three sets of differently oriented diffuse lines discussed in the text together with the large square-like enhancement of the intensity of the response at their crossing points (compare these results for 300 K with those at 10 K in Fig. 4. -S4 Fermi surface for P4W12O44 where the different qi nesting vectors discussed in the text have been outlined. -S5 Comparison of the distribution of electrons in the partially filled bands of γ-Mo4O11 and m= 6 MPTBp where n(ef) refers to the number of electrons at the Fermi level. The different W (left) and Mo (right) sites are labeled. -S6 Lindhard response function calculated for η-Mo4O11 in the (a*,b*) reciprocal plane at 10 K. -S7 Thermal dependence of scans along the diagonal direction a*+b* of the Lindhard function for η-Mo4O11. -S8 Thermal dependence of the intra-chain (a) and inter-chain (b) electron-hole coherence length of the horizontal and diagonal chains in η-Mo4O11. These thermal dependences are very similar to those for γ-Mo4O11 in Fig. 13. -S9 Fermi surface viewed along the c* direction for the m= 4 MPTBp. Note the splitting of the twin FSs due to an enhanced inter-layer coupling in the m= 4 MPTBp with respect to that in Fig. 3c for the m= 6 MPTBp., Peer reviewed

Detailed synthesis and methods, zeta-potential measurements, FESEM images, and PXRD diffractograms., Peer reviewed

The scripts and data presented here are part of the article "Birds optimize fruit size consumed near their geographic range limits"., Animals can adjust their diet to maximize energy or nutritional intake. For example, birds often target fruits that match their beak size as these can be consumed more efficiently. We hypothesize that pressure to optimize diet — measured as matching between fruit and beak size — increases under stressful environments, such as those determining species’ range edges. Using fruit-consumption and trait information for 97 frugivorous bird and 831 plant species across six continents, we demonstrate that birds feed more frequently on closely size-matched fruits near their geographic range limits. This pattern was particularly strong for highly frugivorous birds, whereas opportunistic frugivores showed no such tendency. These findings highlight how frugivore interactions might respond to stressful conditions and reveal that trait matching may not predict resource use consistently., Funding: University of Canterbury, Doctoral Scholarship Royal Society Te Apārangi: UOC1705, The Marsden Fund New Zealand’s Biological Heritage, National Science Challenge Fundação de Amparo à Pesquisa do Estado de São Paulo: 2014/01986-0 Fundação de Amparo à Pesquisa do Estado de São Paulo: 2015/15172-7 Fundação de Amparo à Pesquisa do Estado de São Paulo: 2016/18355-8 Fundação de Amparo à Pesquisa do Estado de São Paulo: 2004/00810-3 Fundação de Amparo à Pesquisa do Estado de São Paulo: 2008/10154-7 Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro: E-26/200.610/2022 Fundação de Amparo à Pesquisa do Estado da Bahia: 0525/2016 Fundação de Amparo à Pesquisa do Estado da Bahia: 0525/2016 Universidade Estadual de Santa Cruz: 00220.1100.1644/10-2018 Rufford Foundation: 22426, Small Grants for Nature Conservation Rufford Foundation: 22426–1, Small Grants for Nature Conservation Rufford Foundation: 11042-1, Small Grants for Nature Conservation Fundação para a Ciência e Tecnologia: CEECIND/02064/2017 Fundação para a Ciência e Tecnologia: UID/BIA/04004/2020 European Research Council: 787638, European Union’s Horizon 2020 Research and Innovation program Swiss National Science Foundation: 173342 Brazilian Research Council: 540481/01-7 Brazilian Research Council: 304742/2019-8 Brazilian Research Council: 300970/2015-3 Consejo Nacional de Investigaciones Científicas y Técnicas: PIP 592 ARC SRIEAS: SR200100005 Deutsche Forschungsgemeinschaft: PAK 825/1 Deutsche Forschungsgemeinschaft: FOR 2730 Deutsche Forschungsgemeinschaft: FOR 1246 Deutsche Forschungsgemeinschaft: HE2041/20-1 Deutsche Forschungsgemeinschaft: PAK 825/1 Deutsche Forschungsgemeinschaft: FOR 2730 Deutsche Forschungsgemeinschaft: FOR 1246 Deutsche Forschungsgemeinschaft: HE2041/20-1 Earthwatch Institute and Conservation International Fundação para a Ciência e Tecnologia: 10.54499/LA/P/0092/2020 Fundação para a Ciência e Tecnologia: 10.54499/CEECINST/00152/2018/CP1570/CT0014, Peer reviewed