dataesr/scientific-paragraphs-categorization huggingface dataset:
While numerous regional-scale syntheses exist [e. g. 33, 34] , even world prehistory or global history textbooks generally do not attempt to integrate land use information using a consistent format for all periods and regions. Significant efforts to aggregate and synthesize archaeological and historical information in publicly available databases exist for North America (DINAA-Digital Index of North American Archaeology; CARD-Canadian Archaeological Radiocarbon Database) and Europe (e. g. , The Cultural Evolution of Neolithic Europe EUROEVOL,  [35] ) but these are typically limited in spatial and temporal scope. These important databases of site-level records are not available for all world regions. A recent effort at global-scale synthesis from archaeological data  [29]  using a preliminary version  [2]  of the classification presented here established the value of such efforts, but did not produce a finegrained, empirically consistent data set, nor did it attempt to integrate historical and archaeological evidence of past land use with information from other proxies such as pollen records  [36].
Classification structures derived from remote sensing often employ terminology applicable to both land use and land cover, such as 'cropland' or 'forest  ' [e. g. 10, 48]. However, forests are a land cover category which can be used in many ways such as recreation, hunting, foraging, or forms of agroforestry. Thus, to understand how past land use affected forests, we need independent evidence for both the land use (in the form of archaeological or historical data) and the land cover (from paleoecological data such as pollen analysis). Ethnographic descriptions of traditional land use systems are an important basis for understanding past land use, but they are not sufficient. There are past land use systems that lack modern or ethnographic analogues. Further, the global scope of LandCover6k requires collapsing regionally and temporally specific vocabulary into fewer and more generic categories.
Bridging the gap between the data-rich but classification-diverse world of historical scholarship and the ALCC community is important if the latter are to benefit from the long history of scholarship on past land use. Our classification of agricultural types shares some features with the work of Widgren and colleagues  [52, 53;  see also  54, 55]  who produced global maps of the dominant agricultural systems between CE 1000 and 1800, but it also differs in significant ways. Widgren and colleagues used a limited number of categories since the aim was to produce global maps of dominant land use systems. LandCover6k instead produces a database, which means that it can include more information than might be legible on a map. We also opted to analytically separate systems of production into components.
For example, the co-occurrence of domesticated plants and animals in an agrarian system is often defined as 'mixed farming'  [52] , but there is little agreement on how much emphasis on livestock (and of what kind) is needed to qualify a system as mixed. Numerical thresholds (e. g. "50% of the income produced on farm should come from livestock") are not something archaeologists can calculate with any degree of certainty [though see  [56] [57] [58]. We therefore code livestock-keeping separately from the cultivation of domestic plants in the database, with the understanding that these often co-occur. Our data structure allows us to track these correlations without needing to define them a priori. We also built on prior classifications of hunting and gathering and pastoralism  [59] [60] [61]  but in a way that is adapted to the possibilities and limitations of archaeological evidence and accommodates a database structure that includes numerous additional variables.
Khasanov's  [62]  categorization of pastoralism, for example, is a useful framework to break down livestock management practices according to levels of mobility; spatial and temporal patterns of movement between resource zones are clearly a significant factor in the potential land cover impact of pastoralism. However, archaeological data cannot necessarily identify levels of mobility in the past  [63] ; where this is possible, it requires datasets that scholars have only just begun to collect  [64, 65]. Past land use is inferred from multiple forms of archaeological and historical data  [66]. Pastoral practices with or without concomitant forms of agriculture, for example, may be identifiable on the basis of settlement sizes, distributions, and duration  [65, 67, 68] ; faunal remains [showing exploitation of wild taxa, husbandry practices of domesticates, including the use of secondary products such as milk, traction, and manure; e.
The LandCover6k land use classification and variables The LandCover6k land use classification scheme has five principal features. First, it is scaleand source-independent. Second, it uses uniform terminology for all world regions and periods, terminology that was agreed upon in LandCover6k workshops involving diverse participants. Third, it is hierarchical and flexible, incorporating both categories of varying specificity and variables that are relevant across land use systems. The categories and variables are defined with the limitations of archaeological data in mind, specifically the variability in data coverage and data quality across regions and time periods. Variables also include assessments of data coverage and quality. Fourth, it relies upon expert assessment of dominant forms of land use in an 8 x 8 km area at a particular point in time (see "Implementation of the Classification in a Geospatial Database," below).
Fifth, the classification takes the perspective of land rather than people. We explain and justify each of these features below. According to the Food and Agriculture Organization of the United Nations (FAO), land cover classifications should be "scale-independent, meaning that the classes at all levels of the system should be applicable at any scale or level of detail; and source-independent, implying that it is independent of the means used to collect information"  [100]. Following these principles, our database classifies global land use according to predefined categories at varying levels of detail. Within the hierarchical structure of the classification (discussed further below), all categories are scale-independent and independent of the different data sources used. Source-independence is especially important for classifications of human land use practices since there are so many ways to study them, as described above.
As noted, recording variables as well as categories allows us to make some land use categories more generic. For example, the term 'agropastoralism' is widely used to describe land use systems that integrate both farming and animal husbandry, especially those with 'high' levels of dependence on domesticates such as ovicaprids and/or bovids. However, the term is not used consistently around the world and there is no agreed-upon standard for what balance of farming and herding constitutes 'agropastoralism' and what is simply 'agriculture' with some domestic animals. Accordingly, we coded for animal (and plant) domesticates as variables distinct from the classification of agricultural practices, allowing for a higher degree of variability to be recorded. The variables recorded across land use systems include assessments of data coverage and quality in order to facilitate identification of times and places needing additional research.
Human land use is often heterogeneous, integrating multiple forms and strategies of production  [49] ; our scheme is structured to capture some of that diversity, but of necessity requires that decisions about dominant land use practices be made, sacrificing detail for scale, especially in the higher levels of classification. Our use of a relatively fine-grained 8 x 8 km spatial grid (discussed below in "Implementation of the Classification in a Geospatial Database") is also meant to allow land use mosaics to be more faithfully represented than they are at coarser spatial resolutions. In some cases, archaeological and historical data are capable of making even finer spatial distinctions than can be captured by the grid, but in other cases the data are not yet adequate to this task. Our analytical practices are thus a compromise-without sacrificing the primary goal of producing empirically-grounded land use maps for climate modelers, we also worked to develop recording strategies that would be of value to archaeologists, historians, human geographers, environmentalists, and others.
Our database does not take these distinctions into account, focusing instead on the aggregate effects of land use practices in one time and place. Although the focus of the classification is necessarily on land rather than people, we recognize that all types of societies up to the present engage in some use of wild resources, often alongside the use of agricultural and/or pastoral resources, and have built this fact into the definition of our land use categories. Our hunting-gathering-foraging-fishing (HGFF) class only applies if it is the dominant form of land use in a particular area. We preserve this category for land inhabited by people for whom wild resources were/are their principal economic resource  [104, 105]. No global map exists of specialized HGFF land use in the past. By adding proxies into our database, such as settlement mode and fire, we can better understand the role of HGFF land use on land cover throughout time.
Another way to visualize the classification is as a nested series of categories. Schematic figures of each LU1 and its nested subcategories, if any, are included here in the main text to provide illustrations of some of the basic distinctions between them [Figs  2 3 4 5 6 7 ]. The classification system has been through various iterations, and the version presented here is the full, final version of the concept note previously published by our working group  [2]. While the rationale behind the categories remains the same and we hope to have preserved the allowances for regional examples expressed by the LandCover6k membership, the current system has been modified in accordance with issues raised during testing of the database itself.
Implementation of the classification in a geospatial database The LandCover6k project is committed to producing a global land use database for several time slices requested by the climate modelers in joint meetings: 12 kya, 6 kya, 4 kya, and CE 1500. Some continental-scale subgroups are also working on additional time slices. Towards this goal, we have implemented the global land use classification scheme described above and in the (S2 File) in a GIS database, the structure of which is discussed in SI-2. The classification system has been set in the geodatabase in the form of drop-down menus containing all of the valid options for each level of the land use classification hierarchy (LU1, LU2, LU3) and all of the valid options for each of the variables and data assessments relevant across land-use systems.
In order to ensure global coverage and maximum standardization of our land-use maps, regional groups will enter data for 8 x 8 km squares in a vector data format (polygon GIS feature class). The size of spatial units represents a compromise between the needs of the modeling, pollen, and archaeological communities involved in the LandCover6K project. While the LandCover6k pollen group is producing land cover rasters with a resolution of 1 degree [e. g. 108], we elected to use a much smaller grid. The 8 x 8 km grid squares are already much larger than archaeologists and historians are typically comfortable with (as they often spend their whole careers studying individual locales), but we had to compromise in the interest of achieving global maps in a reasonable time frame. The smallest grids used by the ALCC modelers are 5 minutes (1/12 of a degree) so we chose a grid that closely matches this (Fig  8 ).
The task of the regional land use groups will be to enter classifications and to code variables for land that falls within the existing polygon squares. Each regional subgroup within LandCover6k works with an identical copy of the database to record assessments for squares and time periods falling within the range of their collective expertise. A blank version of the geodatabase with the 8 x 8 km global grid is included in the supplementary material. Because the nature and quality of archaeological data vary by region, different groups develop and pursue their own intermediate strategies of land-use mapping to populate the LandCover6k database. For example, some regional groups are able to rely on pre-existing site databases, while others must first generate them or perform land-use assessments in a more general way.
Some groups pursue a strategy of having collaborators draw land use polygons on paper maps or in Google Earth before using the spatial intersection between these polygons and the squares of the global grid to insert classifications into the database. Each regional group selects, assembles, and uses its own body of 1) background environmental information, 2) spatial archaeological data, and 3) decision rules on which to base their classification. Background environmental information commonly includes raster and vector datasets representing elevation, slope, hydrological basins, soil types, soil depth, past rainfall patterns, and past coastlines for the time slice in question. Regional-group-provided base maps are particularly important for dealing with the important issue of sea level change, which affects which sets of squares will require land-use assessments at each time period.
Spatial archaeological data commonly include site locations with information on paleobotanical and zooarchaeological identifications (if available), radiocarbon dates, paleoenvironmental reconstructions, and maps of land use from published regional archaeological syntheses. Decision rules on which to apply classifications include, but are not limited to, spatial assessments such as buffers representing hinterlands around known sites, elevations above which people did not live or practice a certain type of land use, and paleoenvironmental zones associated with a particular type of land use. The generalized processes of translating regional archaeological data into a spatial format using the classification scheme and the accompanying geospatial database are set out in the flowchart [  Fig 9].
It is unfeasible to record metadata and references within the spatial database because the database is structured around individual 8 x 8 km squares and most references and data decisions apply to broader areas. Additionally, the recording of references in the spatial database would provide limited space for reviewing arguments in existing literature and does not directly facilitate publication of the resulting maps. Instead, regional groups record references and data decisions in an essay format. Regional groups prepare one metadata essay for their LU2-"swidden/shifting cultivation" is shown by forest clearance and cultivation of small crops in that space (upper right). LU2-"wet cultivation" (lower left) includes LU3 categories: LU3-"rice paddy/taro pond fields" represented by rice paddies, LU3-"raised fields/chinampas" shown by chinampa agriculture and LU3-"wetland cultivation" shown by cereal and pulse cultivation on river floodplains.
It seems likely that future research will demonstrate that significant Late Chalcolithic rainfed agricultural settlement extended over most of the major plains in Iraqi Kurdistan, and thus our classification includes them, even though not all have yet been investigated. Zooarchaeological and paleobotanical data is virtually non-existent for the south for this period because almost no Early Uruk sites have been excavated and political conflict in the region over the last several decades has hindered the application of scientific approaches to sites of all periods. Zooarchaeological and paleobotanical studies in northern Mesopotamia typically focus on the later phases of the Late Chalcolithic period (beginning c. 3700 BCE), when Uruk populations and material culture spread into the north from the south.
In the absence of empirical data for southern marsh lifeways at the time, we rely on ethnographic analogy to suggest that communities living within marsh environments practiced some pastoralism and perhaps flood-recession agriculture, but primarily relied on fishing, birding, hunting, and gathering for subsistence  [139]. Mesopotamia classification. Using the LU classification outlined here, in the area of Southern Mesopotamia (south of Baghdad), the land use for the 8 km pixels surrounding Early Uruk sites and hypothesized late fifth-early fourth millennium BCE river courses have been assigned as LU1 agriculture with LU2 herbaceous/ground crops and canals/channels for water modification. Residents of this region kept domestic sheep/goat, cattle, and pig and grew wheat, barley, pulses, and date palms; these are recorded in the crop and animal variables.
For the purposes of this paper, our mapping example ends here. Ceramic production was well-established in both northern and southern Mesopotamia by this time (having begun during the Late Neolithic or Pottery Neolithic, after c. 6000 BCE in the north and c. 5400 BCE in the south), and was accomplished on a large scale in cities and towns. Accordingly, ceramic production is recorded in the pyrotechnology variables. Arabia land use data. In Arabia, we have relied on data synthesized by Magee  [140] , McCorriston and Martin (for southwest Arabia)  [141] , and Petraglia et al. (for northern Arabia)  [142]. The available data are much more unevenly distributed than in Mesopotamia, and the land mass is much larger and more variable. The land use mapping therefore often relies on evidence from individual sites and a concise general assessment is more difficult to produce.
Further to the north, in the portions of north Arabia that fall within Jordan and Syria, there is abundant survey data for "late prehistoric" periods that suffers from the same problems noted for the Arabian interior and the Nafud. Sites and features of the Neolithic/Chalcolithic/ Early Bronze Age are not precisely dated, but likely mostly date to periods when climatic conditions were more optimal, and few or no radiocarbon dates fall in the 6 kya range. However, there is general consensus among scholars that there was still some low level of continued human occupation of these areas  [168]. Betts and Martin  [169]  have suggested that Chalcolithic sites exist in the harra (basalt desert) and hamad (limestone desert) of Jordan but that these have frequently gone unrecognized in earlier surveys due to a lack of good diagnostic material.
Conclusion There is a critical need for data-based global assessments of past human land use. Past land use practices transformed land cover in complex and variable ways and with differing degrees of intensity. Although we know that regional-scale transformations in vegetation and even landforms were sometimes very dramatic, it is not yet clear how significant the aggregate of the many local records of landscape transformation documented by archaeologists, paleoecologists, and historians might be on a global scale. Archaeology and other historical disciplines have generated vast quantities of information over the last century or more, but until now these data have not been made commensurate, nor have they been aggregated at a global scale. Data harmonization of this sort requires a common analytical language, shared categories, and shared data formats, requirements addressed by the development of this classification and the accompanying database.
As discussed above, the classification, land use variables, and database reported here are the outcome of an extensive process of consultation and co-design  [8]  between climate modelers, paleoecologists, and archaeologists. Although purpose-built for the objective of improving climate models  [37] , this database also has significant potential to inform historical research. Indeed, in some world regions, the syntheses we are building are the first and often the most systematic effort to integrate existing large but scattered and inconsistent archaeological data sets. Classifying and documenting past land use practices is only one step in understanding the impact of our species on the earth system. While data on land use practices such as plowing, large-scale burning, or flooded-field farming that directly affect chemical cycles, such as carbon and methane, may be immediately relevant to climate models, the impacts of land use histories on anthropogenic land cover change (ALCC) are importantly mediated by climate, prior conditions, and other factors.
We therefore adopt multiple approaches to improving ALCC estimates for the past. First, we aim to link pollen-based vegetation reconstructions with archaeologically-based land use data  [37]. Pollen based reconstructions, though produced at lower resolution, provide an important test of the land use database  [8]. Second, we aim to improve existing ALCC models, which differ significantly from one another  [15]. This work is already underway. Harrison et al. [8]  outlines a protocol for evaluating the land use and land cover models created from the various archaeological data used in ALCC modeling. It suggests ways that the reconstruction created from archaeological data can be implemented into global land use and land cover scenarios, and how these should be evaluated using independent pollen-based reconstructions of land cover and climate.
As part of this, the improved models can then be used in paleoclimate simulations. Within the PMIP (Palaeoclimate Modelling Intercomparison Project) the improved models are being utilized to quantify the magnitude of anthropogenic impacts on climate through time, and in doing so the Land-Cover6k project is playing a vital role in improving the realism of Holocene climate simulations  [8]. While there are multiple approaches ongoing to improve ALCC models  [29] , LandCover6k is novel in important ways. The structure of our database ensures that data is recorded in relatively fine-scale, comparable spatial units. The classification scheme resulted from an extended workshop process in which archaeologists came together to develop a shared understanding that is built into the detailed descriptions and definitions we have provided.
This facilitated the consistent use of classification terms and the assessment of variables in comparable ways. Finally, the classification scheme, database, and resulting products are explicitly designed to both facilitate the work of modelers and to serve as a resource for archaeologists. Existing efforts to quantify ALCC rely on estimates of past population to inform projections of potential land cover impact  [9, 11] , and a recent contribution compiled published regional estimates of per capita land use for cropland and pasture as an additional parameter for population-based models  [10]. While population is clearly an important factor in the area of land needed to meet resource requirements, there are multiple strategies for obtaining specific resources and thus many different vegetation-change outcomes that may result.
Further, population effects are mediated by varied forms and levels of consumption  [195]. Demography with an assumption of constant per capita cropland use ignores factors such as surplus production, waste, and agricultural intensification  [2]. Existing ALCC models rely on algorithms to, for example, distribute population evenly across the landscape or to distribute past population through linear extrapolation from modern demographic patterns; both of these approaches miss well-known locational preferences of groups practicing different forms of land use as well as ways these locational preferences have changed through time with technological, environmental, and cultural change. Our datasets can thus provide empirically-based corrections and constraints on ALCC models, enhancing their value to earth system modelers.