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opencc-by-4.0Apr 2021View details →

A Weakly-Labeled Stance Dataset during the 2019 South American Protests

Research across different disciplines has documented the expanding polarization in social media. However, much of it focused on the US political system or its culturally controversial topics. In this work, we explore polarization on Twitter in a different context, namely the protest that paralyzed several countries in the South American region in 2019. By leveraging users’ endorsement of politicians' tweets and hashtag campaigns with defined stances towards the government of each country (for or against), we construct a weakly labeled stance dataset with hundreds of thousands of users. Moreover, through the synergistic usage of network-focused methods applied on news sharing patterns and language-focused methods, we validate our labeling methodology by showing that these stances partition the users into meaningful communities. That is, we show that polarization in users' news sharing patterns was consistent with their stances towards the government and that polarization in their language mainly manifested along ideological, political, or protest-related lines.

opencc-by-4.0Jul 2024View details →

Monthly TM5-4DVar CO2 fluxes based on GOSAT and in situ measurements for the South American Temperate region from 2009 to 2018

The data set contains monthly CO2 land-atmosphere exchange fluxes (Net Biome Productivity, NBP) for the South American Temperate (SAT) region, as defined by TRANSCOM, from 2009 to 2018. The fluxes are calculated using the atmospheric inversion TM5-4DVar (Basu et al., 2013), as described in Metz et al. (2023), assimilating in situ and/or Greenhouse Gases Observing Satellite (GOSAT) measurements. If the data is used for publications, please contact sanam.vardag@uni-heidelberg.de to discuss potential co-authorship and technical details. The following data sets are included: TM5-4DVar_ACOS_SAT: Monthly NBP fluxes for the whole South American Temperate region estimated by assimilating GOSAT/ACOSv9 XCO2 data and in situ CO2 concentration measurements together. TM5-4DVar_RT_SAT: Monthly NBP fluxes for the whole South American Temperate region estimated by assimilating GOSAT/RemoTeCv2.4.0 XCO2 data and in situ CO2 concentration measurements together. TM5-4DVar_GOSAT_MeanAcosRt_SAT: Mean of the monthly NBP fluxes of TM5-4DVar_ACOS_SAT and TM5-4DVar_RT_SAT. TM5-4DVar_IS_SAT: Monthly NBP fluxes for the whole South American Temperate region estimated by assimilating only in situ CO2 concentration measurements. TM5-4DVar_prior_SAT: Monthly NBP fluxes for the whole South American Temperate region used as prior in the atmospheric inversion TM5-4DVar. TM5-4DVar_GOSAT_MeanAcosRt_arideast: Like TM5-4DVar_GOSAT_MeanAcosRt_SAT but only for the arid regions in the eastern SAT region. TM5-4DVar_GOSAT_MeanAcosRt_aridwest: Like TM5-4DVar_GOSAT_MeanAcosRt_SAT but only for the arid regions in the western SAT region. TM5-4DVar_GOSAT_MeanAcosRt_humid: Like TM5-4DVar_GOSAT_MeanAcosRt_SAT but only for the humid regions in the SAT region. All data sets have the following variables: MonthDate: date (YYYY-MM-DD) of the middle of the individual month Month: MM Year: YYYY NBP_flux_monthly_TgC_per_subregion: NBP flux as total monthly flux over the whole individual region (SAT, SAT humid, SAT arid east, west) in TgC/month. NBP fluxes are calculated as Net Ecosystem Exchange fluxes + fire emissions. For more details about the atmospheric inversion and the used measurement data, please see Metz et al., 2023.   Basu, S., Guerlet, S., Butz, A., Houweling, S., Hasekamp, O., Aben, I., et al. (2013). Global CO 2 fluxes estimated from GOSAT retrievals of total column CO 2. Atmospheric Chemistry and Physics, 13(17), 8695–8717, 2013.  Metz, E.-M., Vardag, S.N.,  Basu, S., Jung, M., Ahrens, B., El-Madany, T., Sitch, S., Arora, V.  K., Briggs, P. R. , Friedlingstein, P., Goll, D.S., Jain, A.K.,  Kato, E., Lombardozzi, D., Nabel,J .E. M. S., Poulter, B., Séférian, R., Tian, H., Wiltshire, A., Yuan, W., Yue, X., Zaehle, S.,  Deutscher, N.M.,  Griffith, D.W.T., Butz, A. Soil respiration–driven CO2 pulses dominate Australia’s flux variability. Science, 379, 1332-1335, https://doi.org/10.1126/science.add7833, 2023.

opencc-by-4.0Oct 2021View details →

Dataset for the manuscript "Are remote sensing evapotranspiration models reliable 2 across South American ecoregions?" published in WRR

Metadata of ‘Are remote sensing evapotranspiration models reliable across South American ecoregions?’   This document describes the file formatting and data used to run and evaluate the evapotranspiration models in this study. Because forcing data varies among models, each input file contains a different set of meteorological data placed within a folder named after the corresponding model.   File format and time stamps Data files are CSV formatted with timestamps in the first column of the file. The following timestamps are used: <ul> <li>GLEAM: Year (YYYY); Day of Year (DDD)</li> <li>PT-JPL: Year (YYYY); Month (MM); Day (DD)</li> <li>PM-MOD: Year (YYYY); Month (MM); Day (DD)</li> <li>PM-VI: Date (MM/DD/YYYY)</li> </ul>   Missing data Missing data are reported using ‘NaN’ as a replacement flag. Data for all days in a leap year are reported.    Data format The column headers Name, Description and Units are adopted used in the data files to describe the following variables:: <ul> <li>ETo, Penman-Monteith FAO-56 reference evapotranspiration (mm day-1);</li> <li>ETobs, Observed evapotranspiration (mm day-1);</li> <li>Rn, Surface Net Radiation (w m-2);</li> <li>Rg, Daylight shortwave Incoming Radiation (w m-2);</li> <li>Rgs_out, Shortwave Radiation - outgoing (w m-2);</li> <li>G, Soil heat flux (w m-2);</li> <li>P, Rainfall (mm day-1);</li> <li>T, Surface Air Temperature (ºC);</li> <li>Tmax, Maximum Temperature (ºC);</li> <li>Tmin, Minimum Temperature (ºC);</li> <li>Tday, Daytime Temperature (ºC);</li> <li>TminDay, Daytime Minimum Temperature (ºC);</li> <li>TminNight, Nighttime Minimum Temperature (ºC);</li> <li>Patm, Atmospheric Air Pressure (Pa);</li> <li>ea, Actual Vapor Pressure (kPa);</li> <li>es, Saturation Vapor Pressure (kPa);</li> <li>VPD, Vapor Pressure Deficit (kPa);</li> <li>eaDay, Daytime Actual Vapor Pressure (kPa);</li> <li>eaNight, Nighttime Actual Vapor Pressure (kPa);</li> <li>RH, Air Relative Humidity;</li> <li>RHDayTime, Daytime Air Relative Humidity;</li> <li>RHNightTime, Nighttime Air Relative Humidity;</li> <li>LAI, Leaf Area Index (m² m-²);</li> <li>SWC, Soil Water Content (mm m-1).</li> </ul>   Forcing data per model Each model requires a different set of forcing data, as follows: <ul> <li>GLEAM: Rn, P, T, Rgs_out;</li> <li>PT-JPL: Tmax, Rn, RH (or ea);</li> <li>PM-MOD: Rg, Tday, TminDay, TminNight, RHDayTime, RHNighttime, eaDay, eaNight;</li> <li>PM-VI: ETo.</li> </ul>   Tower sites (IDs) and co-authors/PIs: <ul> <li>SDF: J. P. Quezada and M. Galleguillos;</li> <li>TF1 and TF2: L. Kutzbach and D. Holl;</li> <li>GRO and SLU: G. Posse;</li> <li>BAL and MCC: M. Gassman and C. Perez;</li> <li>PDG, EUC and USR: O. Cabral;</li> <li>FM and SIN: J.S. Nogueira and T. Range;</li> <li>CAA: M. Moura;</li> <li>CST: A. C. D. Antonino;</li> <li>SJO: E. S. Souza and J. R. S. Lima;</li> <li>ESEC: B. Bezerra.</li> </ul>

opencc-by-4.0Feb 2022View details →

HISAR - Hydrologic Indices of South American Rivers

This is a preview of the HISAR dataset (Hydrologic Indices of South American Rivers).  The HISAR dataset is freely available for non-commercial use. The files provided are (i) drainage line shapefile with river reaches as represented by the MGB model and 73 attributes corresponding to hydrologic indices derived from simulated time series; (ii) gauge points shapefile with 73 attributes corresponding to hydrologic indices derived from observed time series; (iii) maps with hydrologic indices, (iv) maps with information of the error of some indices and (v) the scripts used to calculate the indices. This database provides a spatial view of the variability of the river flow regime characteristics. The line shapefile has 33,749 river reaches with an average length of 15 km and drainage area > 1000 km². The ESRI shapefile also has the attributes of drainage area (Upst_Area_ in km²), length (Ltr_Km_ in km), UC (corresponding catchment attribute from hydrological modelling), HYear_min (starting month of the hydrological year of minimum flow) and HYear_max (starting month of the hydrological year of maximum flow). A value of -9999999 is used as a symbol of ‘no data’. Some river reaches do not have all hydrologic indices calculated, due to series of streamflows that could not meet specific criteria. For instance, the baseflow recession constant was automatically calculated using at least five consecutive days of decreasing streamflow, all of which below the Q90 (streamflow value that is exceeded 90% of the time), and this condition was not found in all cases. The gauge points shapefile has 1329 points with 73 attributes corresponding to hydrologic indices derived from observed time series. The drainage area of the gauges ranging from 1,000 to 4,703,503 km2. The ESRI shapefile also has the attributes of code, name, latitude (lat), longitude (long), drainage area (Upst_Area_ in km²), Country were the gauge point are located, HYear_min (starting month of the hydrological year of minimum flow)  and HYear_max (starting month of the hydrological year of maximum flow). A value of -9999999 is used as a symbol of ‘no data’. For more information about HISAR dataset see the journal article DOI: in preparation.

opencc-by-4.0Aug 2022View details →

Data from the article "Plastome sequencing of South American Podocarpus species reveals low rearrangement rates despite ancient Gondwanan disjunctions"

Input data, intermediate and final analysis output files associated to the manuscript "Plastome sequencing of South American Podocarpus species reveals low rearrangement rates despite ancient Gondwanan disjunctions" We sequenced the plastomes of four South American species of Podocarpus from Patagonia, southern Yungas, and Brazilian subtropical forests: P. nubigenus, P. parlatorei, P. salignus and P. selowii. We compared their plastomes to those published from Brazil, Africa, New Zealand, and Southeast Asia, along with representatives from other genera within Podocarpaceae as outgroups. The four newly sequenced plastomes ranged in size between 133,791 bp and 133,991 bp. Gene content and order among chloroplasts from South American, African and Asian Podocarpus were conserved and different from the plastome of P. totara, from New Zealand. Most genes showed substitution patterns consistent with a conservative selective regime. Phylogenies inferred from either complete sequences or protein coding regions were mostly congruent with previous studies, but showed earlier branching of P. salignus, P. totara and P. sellowii.

opencc-by-4.0Jan 2020View details →

Fig. 2 in Taxonomic revision of the South American subgenus Canthon (Peltecanthon) Pereira, 1953 (Coleoptera: Scarabaeidae: Scarabaeinae: Deltochilini)

Fig. 2. Canthon (Peltecanthon) staigi (Pereira, 1953), ♂, Rio de Janeiro, Restinga da Marambaia (CEMT). A. Parameres lateral view. B. Parameres dorsal view. C. Internal sac ventral view. D. Internal sac dorsal view. Abbreviations: see Material and methods.

opencc-by-4.0Jan 2020View details →

Fig. 7 in Taxonomic revision of the South American subgenus Canthon (Peltecanthon) Pereira, 1953 (Coleoptera: Scarabaeidae: Scarabaeinae: Deltochilini)

Fig. 7. Canthon (Peltecanthon) terciae sp. nov., paratype, ♂, Rio Grande do Norte, Baia Formosa, Mata Estrela (CEMT). A. Dorsal habitus. B. Ventral surface of meso- and metafemora. C. Transverse carina in the hypomeron. D. Carina in the margin between pygidium and propygidium. E. Male protibial spur. F. Paratype, ♀, Rio Grande do Norte, Baia Formosa, Mata Estrela (CEMT), protibial spur.

opencc-by-4.0Jan 2020View details →

Fig. 1 in Taxonomic revision of the South American subgenus Canthon (Peltecanthon) Pereira, 1953 (Coleoptera: Scarabaeidae: Scarabaeinae: Deltochilini)

Fig. 1. Canthon (Peltecanthon) staigi (Pereira, 1953), ♂, São Paulo, Bertioga (CEMT). A. Dorsal habitus. B. Ventral surface of meso- and metafemora. C. Transverse carina in the hypomeron. D. Carina in the margin between pygidium and propygidium. E. Male protibial spur. F. Female protibial spur, São Paulo, Caraguatatuba, P.E. Serra do Mar (CEMT).

Fig. 29 in Systematics of the enigmatic South American Streblopus Van Lansberge, 1874 dung beetles and their transatlantic origin: a case study on the role of dispersal events in the biogeographical history of the Scarabaeinae (Coleoptera: Scarabaeidae)

Fig. 29. Current distribution of Streblopus van Lansberge, 1874 and the Old World groups with which it is believed to be more closely related plotted on an Upper Cretaceous palaeomap (~ 80 million years ago). Based particularly on the hypothesis in Tarasov & Génier (2015) that Streblopus is part of a clade otherwise composed uniquely of dung beetle lineages either exclusively distributed in Africa (Circellium, Chalconotus and Gyronotus) or with a distribution largely centred on that continent (Scarabaeini), and on the dating of the origin of the Scarabaeini as 71 million years ago (Gunter et al. 2016), we propose that the lineage that would eventually lead to Streblopus branched off from those groups in Africa some time between 95 and 71 million years ago, and that one of its descendent lineages (the only one living today) dispersed from its original continent to South America during the late Upper Cretaceous or the early Cenozoic. Since Africa and South America have not been connected by land since the Lower Cretaceous, the only way the ancestor of Streblopus could have reached South America was through transoceanic dispersal across the early South Atlantic. That dispersal probably happened by rafting on floating pieces of plants or other debris, as probably occurred with a large number of other organisms. Palaeomap modified from Scotese (2016); distribution area based on Balthasar (1963), Scholtz & Howden (1987), Davis et al. (2008) and our own results.

Fig. 14. Profemora. A‒B. Streblopus opatroides van Lansberge, 1874. A in Systematics of the enigmatic South American Streblopus Van Lansberge, 1874 dung beetles and their transatlantic origin: a case study on the role of dispersal events in the biogeographical history of the Scarabaeinae (Coleoptera: Scarabaeidae)

Fig. 14. Profemora. A‒B. Streblopus opatroides van Lansberge, 1874. A. ♂. B. ♀. C‒D. S. punctatus (Balthasar, 1938). C. ♂. D. ♀. Note the differences between the species and sexes in relation to the overall shape of the profemora and the presence of spurs on the anterior edge in males.

Fig. 12. Pronotum. A. Streblopus opatroides van Lansberge, 1874. B. S in Systematics of the enigmatic South American Streblopus Van Lansberge, 1874 dung beetles and their transatlantic origin: a case study on the role of dispersal events in the biogeographical history of the Scarabaeinae (Coleoptera: Scarabaeidae)

Fig. 12. Pronotum. A. Streblopus opatroides van Lansberge, 1874. B. S. punctatus (Balthasar, 1938). Note the differences in the umbilicate punctation and colour between the species.

Fig. 1. Streblopus opatroides van Lansberge, 1874. A‒B. Ordinary specimens, dorsal view. A in Systematics of the enigmatic South American Streblopus Van Lansberge, 1874 dung beetles and their transatlantic origin: a case study on the role of dispersal events in the biogeographical history of the Scarabaeinae (Coleoptera: Scarabaeidae)

Fig. 1. Streblopus opatroides van Lansberge, 1874. A‒B. Ordinary specimens, dorsal view. A. ♂. B. ♀. C‒D. Lectotype, ♂. C. Dorsal view. D. Attached labels.

Fig. 3 in Systematics of the enigmatic South American Streblopus Van Lansberge, 1874 dung beetles and their transatlantic origin: a case study on the role of dispersal events in the biogeographical history of the Scarabaeinae (Coleoptera: Scarabaeidae)

Fig. 3. Streblopus punctatus (Balthasar, 1938). A‒B. Holotype, ♀. A. Dorsal view. B. Attached labels. C‒D. Ordinary specimens, dorsal view. C. ♂. D. ♀.

Figures 67–82. Pentilia spp. 67-72 in South American Coccinellidae (Coleoptera), Part XXI: systematic revision of South American Pentilia Mulsant (Cryptognathini)

Figures 67–82. Pentilia spp. 67-72) Pentilia elena. 67) Habitus. 68) Frons. 69) Penis guide ventral. 70) Penis guide lateral. 71) Base of penis. 72) Spermathecal capsule. 73-76) Pentilia krystal. 73) Habitus. 74) Frons. 75) Penis guide ventral. 76) Penis guide lateral. 77-82) Pentilia lora. 77) Habitus. 78) Frons. 79) Penis guide ventral. 80) Penis guide lateral. 81) Apical 1/2 of penis. 82) Spermathecal capsule.

Figures 18–34. Pentilia spp. 18-23 in South American Coccinellidae (Coleoptera), Part XXI: systematic revision of South American Pentilia Mulsant (Cryptognathini)

Figures 18–34. Pentilia spp. 18-23) Pentilia nichole. 18) Habitus. 19) Frons. 20) Penis guide ventral. 21) Penis guide lateral. 22) Penis. 23) Spermathecal capsule 24-29) Pentilia jody. 24) Habitus. 25) Frons. 26) Penis guide ventral. 27) Penis guide lateral. 28) Penis. 29-34) Pentilia kendra. 29) Habitus. 30) Frons. 31) Penis guide ventral. 32) Penis guide lateral. 33) Penis. 34) Spermathecal capsule.

Figures 116–118. Pentilia spp. 116 in South American Coccinellidae (Coleoptera), Part XXI: systematic revision of South American Pentilia Mulsant (Cryptognathini)

Figures 116–118. Pentilia spp. 116) Frons. 117) Epipleura ventral. 118) Prosternum and head ventral.

Figures 1–17. Pentilia spp. 1-6 in South American Coccinellidae (Coleoptera), Part XXI: systematic revision of South American Pentilia Mulsant (Cryptognathini)

Figures 1–17. Pentilia spp. 1-6) Pentilia sadie. 1) Habitus. 2) Frons. 3) Penis guide ventral. 4) Penis guide lateral. 5) Penis. 6) Spermathecal capsule. 7-12) Pentilia bernadette. 7) Habitus. 8) Frons. 9) Penis guide ventral. 10) Penis guide lateral. 11) Penis. 12) Spermathecal capsule. 13-17) Pentilia traci. 13) Habitus. 14) Frons. 15) Penis guide ventral. 16) Penis guide lateral. 17) Penis.

opencc-by-4.0Jun 2019View details →

Figures 1–20. Cryptognatha spp. 1–7 in South American Coccinellidae (Coleoptera), Part XIX: Overview of Cryptognathini and systematic revision of South American Cryptognatha Mulsant

Figures 1–20. Cryptognatha spp. 1–7) Cryptognatha auriculata. 1) Habitus. 2) Frons. 3) Proleg. 4) Phallobase ventral. 5) Phallobase lateral. 6) Penis. 7) Genital plates. 8–14) Cryptognatha nodiceps. 8) Habitus. 9) Frons. 10) Proleg. 11) Phallobase ventral. 12) Phallobase lateral. 13) Penis. 14) Spermathecal capsule. 15–20) Cryptognatha gemellata. 15) Habitus. 16) Frons. 17) Spermathecal capsule. 18) Phallobase ventral. 19) Phallobase lateral. 20) Penis.