dataesr/scientific-paragraphs-categorization huggingface dataset:
Samples were selected from across the compositional groups qualitatively identified by pXRF. The sample set amounts to a total of 48 samples, including 21 samples of copper ores, 17 slag fragments and 10 copper objects or lumps from the three metallurgical sites, which were selected for lead isotope analyses, seeking to cover the whole production process in each site. The sample is completed with four copper objects from the nearby necropolis of La Encantada I, Loma de Belmonte and Las Churuletas 1. Data from the literature from Almizaraque, Terrera Ventura and El Barranquete are also used for comparative purposes. In addition, trace element analysis was conducted for 18 archaeological ore samples from Almizaraque, Las Pilas and Santa Bárbara in order to compare them with the trace element patterns of the geological samples in the area (Tab.
We have to bear in mind as well the difficulty in defining the isotopic field of Herrerías, which cannot be differentiated from Sierra Almagrera, and from which two of the analysed samples are displaced significantly towards the zone which covers the isotopic field of Cerro Minado. In figure  10  the two 'outliers' from Herrerías are also represented, which could be indicators of the direction in which the isotopic field might expand should further analyses be conducted. Were that so, the overlap with the isotopic field of Cerro Minado could turn out to be considerable; based on the limited dataset available, the differences in the trace element patterns are limited too (with the apparent exception of the mentioned lead levels). It would be necessary to expand the ore sampling from Herrerías to achieve greater definition, although given the extensive modern exploitation of the area it will be difficult to obtain new samples for analysis.
ACKNOWLEDGMENTS The authors are grateful for the technical and human support provided by  SGIker of UPV/EHU  and European funding (ERDF and ESF). We are also in debt with  Eduardo Galán ,  Ruth Maicas  and  Carmen Cacho , curators of the  Museo Arqueológico Nacional (Madrid)  for facilitating the sampling and study of metal objects as well as with  Ignacio Soriano Llopis  for his help on the selection and sampling of the Palmela points, with Massimo Chiaradia who performed the analyses of the Palmela points at the  Department of Earth Sciences (University of Geneva, Switzerland ) and with  Óscar García Vuelta  for his pictures of some assemblages. We also appreciate and the careful work of editing and style of the TP editorial team.
* This study was suported by the following sources of funding: a  Marie Curie Intra European Fellowship  ( PN623183 ) funded within the 7 th European Community Framework Programme; two  Research and Development (R&D)  projects funded by the  Spanish Ministry of Science, Innovation and Universities : " Metal and amber: Models of raw materials circulation in Iberian Recent Prehistory " ( HAR2017-82685-R ) and " Tecnología y Sociedad. Las primeras artesanías de las comunidades neolíticas en Andalucía oriental entre el VI y el III  milenio a. n. e" (HAR 2016-78197-P); and a Research Excellence Chair 2011-2016 "Activités minières et métallurgiques: anthropisation des milieux et productions matérielles", funded by the  Centre National de la Recherche Scientifique (CNRS). a Dpto. de Cartuja s/n 18071 Granada.
Agradecimientos En este trabajo destacamos la colaboración de la Lic. María Cecilia Puigbó quien tuvo a su cargo la comunicación y coordinación entre los diferentes autores que sumaron aportes y permitieron la concreción de este artículo multidisciplinario. Se agradece a la Dra. María Laura Salinas por haber supervisado la tarea de transcripción y traducción del francés al español realizada por los autores del Instituto de Investigaciones Geohistóricas (IIGHI) y en especial a la becaria Milagros Belén Blanco por haber colaborado en la transcripción paleográfica. Se agradece la colaboración de la Prof. Celina Magalí Torres en la interpretación del material en guaraní y a Prof. María Sara Lanari Zubiaur por la lectura del material producido por la Dirección de Educación Intercultural Bilingüe dependiente del Ministerio de Educación de la Prov.
Sampling and DNA preparation We sampled all 19 species of "Brosimeae" but were unable to obtain usable sequences from B. glaziovii, B. melanopotamicum, and H. steyermarkii, with final taxon sampling consisting of 16/19 species plus both subspecies of B. alicastrum (Table  1 ). Outgroups included at least one species from all other genera within Dorstenieae (12 additional genera), one sample per tribe for the remaining six Moraceae tribes, and Trema orientale (Cannabaceae). We prepared 22 new sequencing libraries for this study, combining those samples with reads from  Zhang et al. (2019) ,  Johnson et al. (2016) , and GenBank to complete the outgroup sampling. All reads have been deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (BioProject PRJNA301299).
Sequence assembly and phylogenetic analysis We trimmed demultiplexed reads using Trimmomatic (with these parameters: "ILLUMINACLIP: TruSeq3-PE. fa:2:30:10 HEADCROP:3 LEADING:30 TRAILING:25 SLIDINGWINDOW:4:25 MINLEN:20")  (Bolger & al. , 2014)  and assembled them with HybPiper, which produces gene-by-gene, reference-guided, de novo assemblies. The default output of HybPiper is the predicted coding sequence for each gene ("exon" sequences). We used the HybPiper script "intronerate. py" to build "supercontig" sequences for each gene, consisting of exons as well as any assembled flanking non-coding sequences (intronic or intergenic). For each target gene, "exon" sequences were filtered to remove sequences less than 25% of the average length for that gene. Filtered sequences were then aligned using MAFFT  (Katoh & Standley 2013) , and columns with more than 75% gaps were removed with Trimal  (Capella-Gutiérrez et al.
1 to 1000. The best value (i. e. , with the lowest chi-square) of 2. 6 was then applied as the smoothing parameter in divergence time estimation. We used MCMCTree as implemented in the PAML v4. 9 package  (Yang, 2007)  to estimate the divergence times with a Bayesian relaxed clock, using the topology from the bestknown tree obtained from the RAxML analysis of the concatenated supermatrix. Two steps are needed for estimating divergence times by the approximate likelihood approach in MCMCTree. We first estimated the gradient and Hessian of branch length, and then used them to estimate the divergence times. A rough mean of several parameters were estimated by baseml in PAML v4. 9 at first. Then we set the parameters in MCMCTree according to the results from baseml. We launched the Baysian analysis with a chain length of 22 million generations with the first 10% of the chain length discarded as burnin, sampling a total of 10,000 generations at a frequency of once every 2,000 generations.
To confirm convergence, two independent runs with the same setting were conducted. To test the influence of the prior on the estimate, we used another prior setting (program defaults), followed the same process as described above, then ran the chain for 16. 5 million generations, sampling a total of 10,000 generations at a frequency of once every 1,500 generations. An additional run was conducted for each setting but without data to confirm that the posterior was different from the prior. The convergence of the runs were checked using Tracer v1. 7  (Rambaut & al. , 2018). After confirming all the effective sample size (ESS) were over 100, we combined the results of the two independent runs for each prior setting. The entire dataset set was treated as a single partition to avoid extremely long running times.
Divergence time estimation The stem-and crown-group ages of Brosimum s. l. were estimated to 22. 09-35. 35 Ma (early Miocene) and 18. 49-29. 62 Ma (Oligocene to early Miocene), respectively. Estimation from two different prior settings in MCMCTree showed similar results (Table  2 , Figure  S1 ). The ages estimated with a PL approach were consistent with the results from MCMCTree (Figure  S2c ) as well. Estimates from control runs without input data differed from results based on our dataset, suggesting the estimates were not determined by the priors.
Author contributions EMG led the writing of the manuscript, did field, lab, and herbarium work, and conducted all analyses except the divergence time estimation. LA did herbarium sampling and lab work for the ingroup. QZ did herbarium sampling and lab work for the outgroups and conducted the divergence time analyses. HS acquired funding for outgroup sequencing and supervised overall work on Dorstenieae. AM provided overall guidance on Brosimum was responsible for collecting the morphological data including reviewing herbarium specimens and assembling the character matrix. NJCZ initiated the project, provided overall supervision, and acquired funding for ingroup sequencing. All authors commented on and contributed to the manuscript. Figure  2. Left: maximum-likelihood tree based on a supermatrix of all sequences ("exon" dataset), showing bootstrap support, with previously accepted names.
Right: ASTRAL species tree based on 333 gene trees ("exon" dataset), showing bootstrap/local posterior probability, with revised names. Disagreeing branches appear in red. The "supercontig" topologies were identical (and therefore not shown). Scyphosyce pandurata q q q q q q q q q q q q q q q q q q q q q q q q Pleistocene Pliocene Miocene Oligocene Eocene Quaternary Neogene Paleogene • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • Brosimum guianense M5258 • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • Brosimum guianense M5258.
Acknowledgements This work was supported by the  United States National Science Foundation  (DEB awards  0919119  and  1501373  and  DBI award   1711391 ), a  China Scholarship Council (CSC) PhD grant  (grant No. 201506140077 ) to Q. Z. , and a research grant from the  International Association for Plant Taxonomy (IAPT)  to Q. Z. We thank the  Pritzker Laboratory for Molecular Systematics at the Field Museum of Natural History (K. Feldheim)  for the use of sequencing facilities, the  Kampong (National Tropical Botanical Garden ) for access to living collections,  Louis Ronse De Craene (E)  for advice on the use of anatomical terms,  L. Pokorny  for proofreading the Spanish abstract, and the following herbaria for access to specimens for examination and DNA extraction: F (C. Niezgoda), IBSC (Chung K.
Agradecimientos A los habitantes de los páramos colombianos y venezolanos, ya que sin ellos este estudio no hubiera sido posible. También a las instituciones, asociaciones y académicos entrevistados. A Jean-Marc Antoine y Alexandra Angéliaume-Descamps. Se agradece el apoyo financiero parcial del proyecto ECOS Nord (Evaluation-orientation de la Coopération Scientifique). Gracias a Efraín Porto Tapiquén por su ayuda en la elaboración de los mapas, así como a Héctor Alfonso Martínez Castillo por su ayuda en la traducción. Finalmente, a los tres revisores anónimos por sus útiles sugerencias.
La determinación de fases cristalinas se realizó por medio de difracción X de polvo en el Laboratorio de Estudios Cristalográficos (Granada), en un difractómetro de alta resolución Bruker D8 Advance con radiación monocromática Cu Kα1, monocromador primario de Ge(111) y un detector PSD Lynxeye. Los difractogramas se analizaron con el programa Malvern HighScore Plus (PANalytical) usando la Crystallographic Open Database.
6°S,70. 1°W) in Central Chile, which has the longest time series of mass balance records in the Southern Hemisphere  (DGA, 2009; Masiokas et al. , 2016; Peña et al. , 1987; Zemp et al. , 2019). However, its negative mass balance from 1955 is greater compared with other glaciers of the same watershed  (Ayala et al. , 2020; Farías-Barahona et al. , 2019 , 2020b). In order to identify the glacier similarities, we present a glacier cluster classification across the Chilean Andes using topo-climatic variables from the glacier national inventory of Chile and TerraClimate datasets, for 23,974 glaciers during the period 1980-2019  (Abatzoglou et al. , 2018; DGA, 2015a). Goal in which the gridded data allows documenting the climate in poorly monitored regions  (Condom et al. , 2020; Manz et al. , 2016; Schumacher et al.
b) Climate variables from the TerraClimate dataset The climatic variables were extracted from the TerraClimate (TC) product. TC comprises a global climate dataset based on reanalysis data since 1958, with a 4 km grid size of spatial resolution at monthly time series. This dataset was validated with the Global Historical Climatology Network using 3,230 stations for temperature (r = 0. 95; MAE 0. 32°C) and 6,102 stations for precipitation (r = 0. 90; MAE 9. 1%)  (Abatzoglou et al. , 2018). The variables such as mean, maximum, minimum temperatures, precipitation and solar radiation were processed. From these five variables, we calculated annual (five variables) and monthly (sixty variables) values for the period 1980-2019, obtained sixty-five climate variables in total. The annual values were estimated considering the mean of the annual sum (precipitation) or the mean of the annual mean (temperature and solar radiation), while the monthly values were calculated with the monthly mean in forty years.
Acknowledgments We thank the  Dirección General de Aguas de Chile  for providing the glacier national inventory version 2015. The TerraClimate products were obtained freely at http://www. climatologylab. org/terraclimate. html. This work was funded by the  National Agency for Research and Development (ANID) /  Scholarship Program/ DOCTORADO   BECAS CHILE/2019 -72200174  and the  Secretaría Nacional de Educación Superior, Ciencia, Tecnología e Innovación , SENESCYT/ PhD scholarship. This study was conducted in the framework of the  International Joint Laboratory GREAT-ICE , a joint initiative of the  IRD and universities and institutions in Bolivia, Peru, Ecuador, and Colombia.
Para determinar la edad de los depósitos sedimentares, las muestras seleccionadas fueron secadas en un horno a 50 °C por algunos días hasta obtener peso constante  (Andrejko et al. , 1983). Seguido, las muestras fueron enviadas al Laboratorio de Medida de Carbono 14 (LMC14-Francia), donde fueron analizadas en un Espectrómetro de Masas con Acelerador de Partículas (AMS) para la estimación del isótopo 14 C. Las edades presentadas en este trabajo fueron debidamente calibradas, los rangos de edad calibrados se calcularon utilizando CALIB 7. 10 (programa Calib 7. 10 disponible en internet (http://calib. org/calib/calib. html) e Intel Cal 13. 14c  (Hua et al. , 2013; Reimer et al. , 2013). El modelo cronológico fue obtenido con el uso del paquete Clam 2. 2 en el software R. Las edades obtenidas fueron expresadas en años AC y DC (Antes de Cristo y Después de Cristo, respectivamente).
| Data and context Two data sources were used to monitor the French manufacturing employment year after year over a twenty-year To alleviate this problem, we decided to conduct analysis on a breakdown of the manufacturing activity into 24 divisions (equivalent to the divisions of the NACE rev. 2, see Appendix A), rather than on more detailed breakdowns. We also focused on the general patterns observed over the whole period, rather than on particular years. At the end, we did not see any significant impacts from revisions to the NAF or change in sources that could call our results into question. Figure  1  presents the French Hexagon manufacturing employment growth rate at the LAU 1 (Figure  1(a) ), the NUTS 3 (Figure  1(b) ) and the NUTS 2 (Figure  1(c )) spatial scales, as well average value and Tukey fences for regional growth rates.
43E -2 ) 20 (1. 57E -2 ) 21 (1. 06E -2 ) Competitive effect 14 (3. 08E -3 ) 19 (1. 26E -2 ) 17 (1. 65E -2 ) 18 (8. 09E -3 ) Industry-mix effect 8 (1. 07E -3 ) 3 (4. 96E -3 ) 3 (7. 60E -3 ) 4 (3. 10E -3 ) Emergence effect 5 (2. 79E -3 ) 4 (4. 18E -3 ) 4 (7. 72E -3 ) 4 (4. 05E -3 ) Outlier effect 4 (9. 20 E -4 ) 2 (5. 27E -3 ) 2 (8. 21E -3 ) 4 (2. 66E -3 ) Notes: All significant Moran's I have positive values; p-values ≤ 0. 05 are considered statistically significant; p-values (two-tailed) are calculated for the randomization null hypothesis' test; R package lctools has been used. 22-year study period. Table  1  presents summarized results regarding the number of years with a significant global autocorrelation and the values of the significant indices. 12 Second, Anselin's LISA (local indicator of spatial association) is commonly used as a local equivalent of Moran's index  (Anselin, 1995).
period. For years 2008 to 2015, we used the CLAP database of the National Institute of Statistics and Economic Studies (INSEE),6 which reports the number of salaried job positions (except for defence and household employers) on 31 December of each year at the municipal level, distinguishing among the 732 different activities of the French statistical classification of economic activities (NAF rev. 2). For years 1993 to 2008, we used the database of the former French agency for unemployment insurance system (Assédic), which provides a similar information, but excluding state employees and agricultural sectors. Based on these datasets, we computed gross and sectoral annual growth rates at three spatial scales (LAU 1, NUTS 3, NUTS 2) for the years 1994 to 2015. These rates were systematically calculated with annual data coming from the same dataset: the annual growth rate of 2008 based on the number of Assédic jobs in 2007 and 2008, and the annual growth rate in 2009 based on the number of CLAP jobs in 2008 and 2009.
For this, we use data on CO 2 emissions derived from household-polluting activities aggregated at the regional level for the 20 regions of Italy, to furnish accurate estimates of household income elasticities for environmental degradation. For the data source, we use the NAMEA (National Accounting Matrix including Environmental Accounts) data set provided by the Italian National Institute of Statistics (ISTAT). The latter is one of the few data sets, to the best of our knowledge, that discriminate data on pollution by emission source, that is, emissions from industrial activities and emissions from polluting activities of individuals, thus ruling out all emissions that are not derived from household activities. 6 he rest of the paper is organized as follows. Section 2 summarizes relevant literature review and discusses why the Italian scenario represents an interesting case study for the analysis of the EEC under a regional framework.
Data For our analysis, we constructed a balanced panel data 11 comprehensive of longitudinal observations of household data aggregated at the regional level for the 20 Italian regions, spanning from 1995 to 2008. 12 For the data on CO 2 emissions, we relied on the data sets from NAMEA and ISPRA (Institute for Environmental Protection and Research). The NAMEA data set is composed of two related data sets provided by ISTAT, which report, respectively, the CO 2 emissions from 1995 to 2008 at the nationwide level and the levels of CO 2 emissions derived from both household and economic activities aggregated at the regional level for 2005. The ISPRA data set contains detailed information on the SNAP (Simplified Nomenclature for Air Pollution) classification of emissions per sector and process at the regional and provincial levels.
This data set is used by ISTAT to obtain the shares of CO 2 emissions (measured in metric tons) derived from both household and economic activities at the regional level. We replicated the ISTAT methodology using NAMEA and ISPRA data sets to compute the levels of CO 2 emissions derived from household activities aggregated at the regional level for the remaining years. 13 Subsequently, we derived the average level 11 Overall, the use of panel data presents notable advantages over cross-sectional and time series data, such as the possibility to control for individual heterogeneity and to increase the degrees of freedom. 12 As discussed by  Bimonte and Stabile (2017) , this particular time frame allows us to reduce the potential issue of structural breaks, because it excludes the periods of the Italian economic boom and of social reforms (which originated, respectively, in the early 1950s and 1970s) and the financial crisis (which started to spread in 2008).
13 The ISTAT methodology to compute gas emissions from household activities aggregated at the regional level is extremely exhaustive. Specifically, the quantities of CO 2 emissions are derived from the ISPRA data set and broken down by process according to the SNAP classification. The latter are then attributed to households at the aggregate regional level following two steps. First, a qualitative association between each process of the SNAP classification and household activities is performed; household-polluting activities are thus classified into three different items: ''transportation'' (including household emissions derived from the purchase of fuel for private transportation), ''heating'' (including household emissions derived from the usage of heating systems, electricity consumption, and any other energy-related activity), and ''other'' (including household emissions derived from all the other carbon-emitting activities and/or technologies; e.
Such estimations are based on available income data aggregated at very small spatial scale (i. e. census tracts, municipalities, etc. ). Depending on the country, such information either comes from administrative records, such as tax records, or from surveys. Sources of data, income definition and building block units of analysis are reported in Annex 1 (Table  A. 1). 8  We rely on several measures. First, we use the Gini coefficient, as the most commonly used indicator of income distribution. Second, as our dataset includes information on the levels of income for different percentiles along the income distribution (except for Mexico), we complement with percentile ratios, as common in the inequality literature. The 90/10 percentile ratio compares the income of a rich individual (i. e. , in 90 th percentile of the income distribution) with that of a poor individual (i.
Together with the inequality measures, our data set includes, for every city, information on size, GDP per capita, labour participation and the share of population over 65 years. All these indicators are publicly available from the OECD Metropolitan Database, and the full list of variables and their respective definition is reported in Table  A. 2 in the Appendix. 10 In addition, in some regressions we also control for the industrial structure of FUAs. However, at this spatial scale, comparable data at sectoral level is not available for all FUAs. Where data was missing, we considered the industrial structure of the smallest region  9  Our 153 FUAs are all the FUAs above 500,000 inhabitants that the OECD has identified as such and for which a number of statistical indicators are made available.
13 Clearly, Latin American cities are the ones with the highest inequality indices (as the top 10 unequal cities were always Mexican, the list includes other non-Mexican cities), while Nordic and Austrian cities are the ones with the lowest values. The largest differences among cities of the same country are found in Canada. Large diversity is also present among US cities. In any case, what can be seen in Figure  1  is that the range in inequality levels is much larger when one uses an international dataset like ours, rather than using a sample for an individual country, as previously done in the literature. 11 We approximated the industrial structure at FUA level with that of the region in which the FUA is located for about half of the FUAs in our sample. For European countries, we considered the NUTS-3 geographical breakdown.