dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: The authors are grateful to Nguyen Tien Tam for his support in data accuration and representation. The authors also thank the Department of Science and Technology, the Hanoi People's Committee, for funding the research project "Assessment of the current situation and propose solutions to organize landscape architecture of PFGs in the historic city centre of Hanoi with the community participation". Thanks to the expert members from Hanoi Architectural University and the three teams of architects involved to the project. Data Availability Statement: Some data used in this study are available on request from the author.
Acknowledgments: The authors are grateful to Nguyen Tien Tam for his support in data accuration and representation. The authors also thank the Department of Science and Technology, the Hanoi People's Committee, for funding the research project "Assessment of the current situation and propose solutions to organize landscape architecture of PFGs in the historic city centre of Hanoi with the community participation". Thanks to the expert members from Hanoi Architectural University and the three teams of architects involved to the project.
Rather, the recipient country benefits from aid in terms of welfare. For a given level of the stock of global GHG emissions, if the impact of aid is sufficiently strong, the recipient country's imports and exports improve. In other words, the welfare of both countries increases which is a necessary condition for the acceptability of aid by both countries. These mechanisms translate into a positive and reciprocal mathematical relationship between the donor country's imports and exports and climate aid. Thus, the empirical analysis aims to validate this relationship and eliminate possible endogeneity biases via an IV method. In the empirical part of the paper, we provide an econometric evaluation of the predictions of our theoretical model by estimating the impact of trade on aid. We use the OECD Official Development Assistance (ODA) data relative to climate actions, and consider bilateral climate transfers from the members of OECD Development Assistance Committee (DAC) between 2002 and 2017.
We consider two types of climate aid from country 1 to country 2: mitigation aid denoted as T M and, adaptation aid denoted as T A. As concerns mitigation aid, we posit that it allows the recipient country to adopt more efficient and cleaner technology (energy saving technology). Adaptation aid is of different nature: it helps the recipient country to undertake adaptation actions reducing the adverse effect of GHG emissions on production. For example, drip irrigation, crop diversification, and cyclone shelters are adaptation devices which mitigate some of the effects of climate change. The donor funding to the recipient consists of the transfer of environmental goods manufactured using the domestically produced consumer good. 5 We can found examples of these types of projects of climate aid in the database of the OECD for bilateral climate aid that will be used in the next section for the empirical analysis.
Data We gathered data on the universe of climate aid, trade flows, and some control variables, for the period 2002-2017. Climate Aid. The OECD bilateral environmental aid database (OECD, 2020) includes bilateral environmental aid from the members of the OECD DAC. It distinguishes among four types of environmental actions corresponding to the four Rio markers: mitigation, adaptation, biodiversity, and desertification. The projects are screened and their relevance relative to the Rio markers is evaluated as "principal", "significant,"or "not targeted". We use the aggregate principal and significant mitigation and adaptation aids. Following the advice accompanying the database, we separate mitigation and adaptation transfers using project level data, and merge them to obtain total bilateral climate aid per year without duplicates.
We also screen for identical bilateral transfers registered more than once by donor countries for different purposes. The data considered in our analysis refer to 154 recipient and 27 donor countries during the period 2002 to 2017. All the donor countries are DAC members. We excluded EU institution aid transfers; also, we merged Luxembourg's and Belgium's transfers to enable a matching with our trade data. We observed no environmental aid transfers from Hungary. We corrected the aid values for inflation using the US Consumer Price Index (CPI) (base 2010) (World Bank World Development Indicators -WDI 9  ). We built a database which accounts for null transfers by rectangularizing the data at the donor, recipient, and year levels. If no transfer was made, this is recorded as a zero. The rectangularized dataset is used to check for potential selection bias (across donors and recipients) in the empirical estimation.
This second set of data includes all possible climate aid transfers for each country pair in each year. Trade. To measure trade flows, we use the CEPII BACI database  (Gaulier and Zignago, 2010 ) which gathers bilateral trade data at the product-importer-exporter-year level. We matched these data to our period of analysis (2002-2017) and our countries. We aggregate all product-specific trade flows at the bilateral-year level and correct them for inflation using the US CPI (base 2010). This allows us to measure both exports and imports at the country-pair level. Control Variables. We include in our analysis the following set of macroeconomic control variables. In line with much of the foreign aid literature, we control for the following three categories of variables  (Alesina and Dollar, 2000) : recipient needs, recipient merit, and donor interests.
To account for recipient needs, we include GDP and population (World Bank World Development Indicators -WDI), environmental vulnerability (Notre Dame Global Adaptation Initiative (ND-GAIN) indicators 10  ), and per capita GHG emissions (Our World In Data 11  ). To measure recipient merit, we include indicators of institutional quality (World Bank Worldwide Governance Indexes -WGI 12  ). We created an indicator for average institutional quality by computing the mean of the five WGI. To control for bilateral relationships, we include distance, a dummy representing past colonial ties between donor and recipient countries (taken from CEPII)  (Head et al. , 2010)  as we underline in the theoretical model, and an index of voting similarity 13  at the UN General Assembly  (Voeten and Merdzanovic, 2009).
We control also for donor GDP (World Bank WDI 14  ). In the regressions to control for omitted variables and selection bias, we use the share of green party seats in the donor country's parliament to proxy for the donor population's interest in environmental issues  (Armingeon et al. , 2020) , foreign aid (OECD 15  ), language similarity (taken from CEPII)  (Head et al. , 2010) , and a dummy for whether donor and recipient countries are signatories to a regional trade agreement (RTA) (de Sousa, 2012). Monetary variables are in USD millions corrected for inflation using the 2010 US CPI. Descriptive statistics. Table  1  provides the descriptive statistics of the dataset limited to observed climate transfers. The full dataset including the null transfers includes 66, 528 observations. Separate mitigation and adaptation aid data are provided only from 2010, therefore the observations for these two variables cover only 2010 to 2017.
Correcting for selection bias First, we show that our results are robust to correcting for sample selection. Since our baseline results are estimated on a sample which includes only positive climate aid flows, we develop a two-stage model to account for this potential selection bias -known also as a Heckman correction. In the first stage, we estimate the probability to receive climate aid using a set of models on rectangularized data which includes zero aid flows: P r(ClimateAid ijt > 0) = φ(γW it + θY ijt + αZ ij ) (27) where ClimateAid ijt is a binary variable indicating whether or not climate aid was transferred from country i to country j in the year t. In our model, provision of climate aid is determined by the existence of a foreign aid (ODA) relationship between the two countries and by the share of green party seats in the parliaments.
The base map was made with the Natural Earth R package using a CC BY license. Bodies of water were added from the RCMRD Geoportal with a CC BY license in R. All points were added and modifications were performed in R and Adobe Illustrator. The map is published for the first time in this Article. b, In a principal component analysis, eigenvector 1 correlates to variation maximized in sub-Saharan Africa, and eigenvector 2 correlates to Eurasian variation. c, Ancestry component assignment using ADMIXTURE with K = 9 clusters (selected on the basis of low cross-validation errors, high log-likelihood scores, and a low number of reference populations to not overfit; groups that maximize each components are shown on the right). Individuals with sufficient data for high-resolution analysis are plotted in approximate chronological order from left to right.
Imports at most coastal sites typically comprise less than 5% of total assemblages  2, 9. Other aspects of the material culture also show continuity with earlier settlements, including the persistence of crops, domesticated animals, craft styles and ceramics  9, 19. Linguistic evidence provides additional evidence of African roots: Kiswahili is an African Bantu language with Asian loanwords  20. However, without ancient DNA evidence, it is not possible to directly address questions of how genetic ancestry changed over time. We generated ancient DNA data from the skeletal remains of individuals found at six coastal or island towns: Mtwapa, Manda, Faza, Kilwa, Songo Mnara and Lindi. These individuals date to ad 1250-1800 but provide insight into genetic events from the tenth century ad onwards.
Dataset overview We generated 179 ancient DNA libraries from 156 distinct skeletal samples (Supplementary Data File 1 and Methods). We applied in-solution enrichment for a targeted set of about 1. 2 million single nucleotide polymorphisms (SNPs) to obtain genome-wide data passing standard measures of ancient DNA authenticity from 80 distinct individuals. The individuals were buried at seven second millennium ad sites in eastern Africa (black shapes in Fig. 1a ; individuals are listed in Extended Data Table  1  and Supplementary Data File 2; see Methods, 'Inclusion and ethics' and Supplementary Information for details of archaeological and genetic permissions and sampling). We obtained direct radiocarbon dates (Supplementary Data File 3) for 33 of the skeletons, and estimated date ranges for the other individuals on the basis of archaeological context or genetic evidence of relatedness to individuals for whom we had direct dates (Extended Data Table  1  and Supplementary Data File 2).
We hypothesized that the ancestry of Makwasinyi people might be a good proxy to represent inland African groups that may have been in contact with people from medieval towns on the northern Swahili coast in previous centuries  22. Of the 80 individuals for whom we report data, we exclude 26 in genome-wide analyses, although their data remain valuable. Of these, 18 had too few SNPs for high-resolution whole-genome analyses although they yielded useful data such as reliably determined mitochondrial sequences; 5 were first-or possibly second-degree relatives of other individuals in the dataset with higher-quality data; 2 showed evidence of contamination; and 1 was a population genetic outlier with limited data, raising the possibility of contamination (Supplementary Data File 2). Ancient DNA data from four individuals from the eastern African coast have previously been published (Supplementary Data File 5), but none have been published from a Swahili town  23.
Genetic affinities To obtain a qualitative picture of the sources of the ancestry in the ancient individuals, we performed principal component analysis (PCA) (Fig. 1b ). We used 1,286 present-day Eurasian and African individuals to compute the axes (Supplementary Data File 6). We projected the newly reported ancient individuals onto this PCA and found that they form a cline, with one end overlapping with ancient and present-day African groups and the other falling between present-day Persians and Indians. This suggests mixtures of different proportions of source populations at either end of the cline, with these sources potentially having multiple deeper ancestry components. Some coastal individuals-particularly from Songo Mnara and Lindi, do not fall on this cline-suggesting additional complexity, although our power to understand this variability is limited by the small sample sizes.
Proportions of African, Persian and Indian DNA Using qpAdm  26  , we find that most medieval and early modern individuals can only be fit by a model with at least three ancestry components that can be proxied with ancient African, present-day Iranian and present-day Indian populations (Fig. 2a , Extended Data Table  2 , Supplementary Tables 2-10 and Supplementary Information). Such a three-source model fits the pool of 48 Mtwapa individuals and the Faza individual (P value for fit = 0. 23); the pool of Manda (P = 0. 28) individuals, and at least one Songo Mnara individual (I19550) (P = 0. 38). One Kilwa individual (I8816) had a relatively high proportion of ancestry related to inland sub-Saharan Africans, and so the Indian proportion, which is the smallest contributor, falls below the threshold of definitive detection (a two-source model fits; P = 0.
27). The type of African ancestry needed to make the models fit differed between individuals from the north (Mtwapa, Faza and Manda) and south (Kilwa and Songo Mnara) of the studied region. In Kenya, the best-fitting proxy African source is the inland Makwasinyi individuals (Extended Data Table  2 ), who are themselves well-modelled as mixtures of about 80% Bantu-associated and 20% ancient eastern African Pastoral Neolithic ancestry  24  (Fig. 3a , Extended Data Table  3 , Supplementary Tables  2  and 3  and Supplementary Information). In Tanzania, the best-fitting African proxy source is Bantu-associated without evidence of a Pastoral Neolithic contribution. We use the individual buried at around ad 1600 in Lindi as a proxy Bantu-associated source for the Kilwa individual and individual I19550 from Songo Mnara (Extended Data Table  2 ).
Relating medieval to modern Swahili We assembled genome-wide data for two groups of present-day people who identify as Swahili: 89 with previously reported data  32  and 93 for which we generated new data. Most of the individuals in the previously published dataset (87 out of 89) have only a modest inheritance from people with ancestry resembling the medieval people that we sampled from nearby coastal areas (Extended Data Fig. 2a , Extended Data Table  3 , Supplementary Table  16  and Supplementary Information). We estimate that 11 ± 4% have medieval Swahili-associated ancestry; 84 ± 3% have Bantu-associated ancestry; and 6 ± 3% have Pastoral Iron Age-associated ancestry  24  (Extended Data Fig. 2a  and Extended Data Table  3 ). These patterns are mirrored in the Y chromosome haplogroups, with 95% having typically African haplogroups-in contrast to the medieval coastal individuals, for whom almost all Y haplogroups are associated with Near Eastern We compared ancestry estimates on chromosomes 1-22 and the X chromosome to infer proportions from female or male ancestors (Methods).
The previously published dataset included people from the coastal towns of Kilifi, Lamu and Mombasa in Kenya who indicated that their family had been Swahili-speaking for the past three generations. The newly published dataset included people from 13 locations along the Kenyan coast who indicated that their ancestors had lived in coastal towns and had a Swahili identity for many generations, thus enriching for more traditional upper-class Swahili people who plausibly retained more ancestry from medieval coastal individuals  30. The greater medieval coastal ancestry may also reflect isolation. In the newly published dataset from the mainland, individuals from the site of Jomvu Kuu had significantly less medieval coastal ancestry than the individuals from the other sites, all of which were from islands that were plausibly more isolated from admixture with inland groups (P = 3.
No recent Asian ancestry in inland people The Makwasinyi individuals date to the past three centuries, and are from deep in the Tsavo region, nearly 50 km from the nearest population centre. The Makwasinyi group fits as a proxy source for African ancestry in qpAdm modelling of the Mtwapa, Faza and Manda individuals, but unlike these individuals, qpAdm finds no evidence of recent Asian ancestry in the Makwasinyi group (similar to present-day non-coastal populations; Supplementary Table  17 ). Instead, Makwasinyi individuals are similar in ancestry to the modern individuals identified as Swahili in the previously reported dataset  32. They are well-modelled as 21. 3 ± 1. 2% Pastoral Neolithic-associated ancestry (from herders present in eastern African after 3000 bc) and 78. 7 ± 1. 2% Bantu-associated ancestry (from farmers present after 1000 bc) (Extended Data Fig.
The genetic evidence suggests that this arrival was accompanied by mixture, which began by ad 1000, and continued later. People of both African and Asian ancestry made major contributions, with African proportions of approximately 57% on average at Mtwapa and Faza, 32% at Manda, 67% at Songo Mnara and 74% at Kilwa (Table  1 , Extended Data Table  2  and Supplementary Table  8 ). Archaeological evidence provides important context for our genetic findings. The individuals that we analysed lived in the thirteenth to eighteenth centuries, and were excavated mostly from elite contexts. However, coastal sites from around ad 1000, when the mixture occurred, showed little evidence for distinct societal elites. Three of the sites sampled here (Mtwapa, Songo Mnara and Faza) did not exist as towns in ad 1000, and so these admixed populations moved to those towns later.
In addition, the individuals that we analysed were not fully representative of all social and economic groups in Swahili society. Nearly all of the coastal graves and tombs in this study occupied prominent positions in medieval and early modern townscapes (see Supplementary Information). With the possible exceptions of Kilwa, Lindi and Faza, we analysed elite individuals from high-profile coastal sites. However, the Swahili cultural world included many non-elite settlements, where ancestry might be systematically different  36. Our analysis of data from two samplings of present-day people who identify as Swahili with different strategies for determining this identity also reveals qualitative differences in ancestry patterns, revealing how groups identified as Swahili retain high substructure and variation today.
Ancient DNA data generation To generate the genetic data for the samples in Extended Data Table  1  and Supplementary Data File 2 (by-library metrics in Supplementary Data File 1), we used established protocols in dedicated clean rooms, involving first sampling typically 40 mg of powder from skeletal remains, then extracting DNA using methods designed to retain short, damaged fragments  37  , then building individually barcoded libraries  38  after incubation with uracil DNA glycosylase (UDG) with the goal of greatly reducing the characteristic errors typical of ancient DNA. We amplified libraries and carried out in-solution enrichment of them for about 1. 2 million SNPs 39 as well as baits targeting the mitochondrial genome  40. We sequenced the enriched products along with a small amount of unenriched library on Illumina NextSeq 500 or HiSeqX10 instruments.
Data from a total of 93 individuals passed quality control and their data are reported and analysed. We genotyped on the Human Origins SNP array 19 newly reported individuals from Madagascar, for whom samples were collected between 2007-2014 with approval by the Human Subjects' Ethics Committees of the Health Ministry of Madagascar and by committees in France (Ministry of Research, National Commission for Data Protection and Liberties and Persons Protection Committee). Individuals all gave written consent before the study. Samples came from two locations in the north and south of the country. Sampled villages were founded before 1900, and individuals were 61 ± 15 years old, with the maternal grandmother and paternal grandfather born within 50 km of the sampling location. We genotyped on the Human Origins SNP array 10 newly reported Emirati samples that were collected from Emirati nationals from the city of Al Ain, United Arab Emirates.
Extended Data Table 1 | Ancient individuals with high coverage genome-wide data newly reported in this study The lower coverage individual (but still above 15,000 SNPs) from pairs of first-degree relatives are not included in whole-genome analyses, but are reported here. Makwasinyi Family A has 4 members: I13873 (removed from analysis) has a son I13872 and a brother I13875 (who is genetically a second or third-degree relative of I13872), and all three males are second-or third-degree relatives of I17402. Manda Family A has 4 members: I7934, I7942 (removed from analysis), I7943 (not included in the table due to low sequencing coverage), and I19407 (removed from analysis) are all first-degree relatives of one another. Mtwapa Family A has 2 members who are second-or third-degree relatives, I19401 and I13611.
Mtwapa Family B has 8 members; of them, I23550 and I17409 are first-degree relatives. Second-or third-degree relatives are I17409 to I19391, I19394 to I23558, I19394 to I19391, I19391 to I19393 (not included in the table due to low sequencing coverage), I19394 to I19423, and I19394 to I19388. Mtwapa Family C has 3 members: I21475 and I19419 are first-degree relatives, and I21475 and I19384 are second-or third-degree relatives. Mtwapa Family D has 2 members who are second-or third-degree relatives, I19416 and I19417. Mtwapa Family E has 2 members who are second-or third-degree relatives, I19414 and I19415. Mtwapa Family F has 2 members who are second-or third-degree relatives, I23662 and I19398 (not included in the table due to low sequencing coverage, this individual also has a direct date included in Supplementary Data File 2).
Songo Mnara Family A has 2 members who are second or third-degree relatives, I19547 and I19549. Extended Data Table  2  | Ancient individuals with high coverage genome-wide data newly reported in this study that have significant evidence of Persian and often Indian ancestry The African proxy source for the individuals at Mtwapa, Faza, and Manda in Kenya is the Makwasinyi group newly reported in this study, and for the Kilwa individual and the Songo Mnara individual I19550 in Tanzania is the Lindi individual newly reported in this study. The Persian proxy source in all cases is the Human Origins genotyped Iranian group  54, 55. The Indian proxy source in all cases is the Human Origins genotyped Sahariya_MP group  56. Individuals for whom the qpAdm model does not fit with statistical confidence ( < P 0.