dataesr/scientific-paragraphs-categorization huggingface dataset:
Collecting dam data We collected information on 124 dams in the basin from published dam databases  (4, 5, 37). The model required some key parameters for each dam site: (i) location; (ii) mean annual inflow (m 3 /year); (iii) total storage, i. e. , sum of dead and live storage (m 3 ); (iv) annual generation (GWh/year); and (v) status and commercial operation data (COD). For sites with any of these parameters missing, we supplemented the tabulated data via interpolation from other dam sites or from global geospatial data, as described in Supplementary Method 1 and figs. S1 and S2. The database notably omits smaller hydropower dams and many irrigation dams, especially in Thailand, and their potential impacts  (38). However, the database, available as a supplementary Excel file (data S1) presents the largest available set of dams with consistent availability of relevant data.
Acknowledgments Funding: R. J. P. S. was supported by the grant from the  UC Berkeley Institute of International Studies  for the interdisciplinary faculty seminar "Water Management: Past and  Future Adaptations ," and a grant of  WWF. This research was partially supported through the  EU Horizon 2020 Project AMBER  (grant agreement no. 689682 ). The manuscript greatly benefitted from comments and discussions with  M. Goichot  ( WWF Greater Mekong ) and  R. Soncini-Sessa  ( Politecnico di Milano ). Author contributions:  R. J. P. S. , S. B. , and A. C. designed the experiments. R. J. P. S. executed the experiments and developed visualizations. R. J. P. S. , S. B. , A. C. , J. J. O. , and.
Plain Language Summary Standardizing the way data are described and shared is key to accelerating the progress of science. Building on recent advances in paleoceanography and paleoclimatology, we present the first community-led reporting standard for such datasets. The Paleoclimate Community reporTing Standard (PaCTS) provides guidelines as to which information should be included when reporting data from various paleoclimate archives, as well as themes common to many fields, like uncertainty and other site-specific information. The ultimate goal of this effort is to (1) make these datasets more re-usable over the long term, and (2) provide a roadmap for implementing and revising the standard, as the field of paleoclimatology and its practitioners both evolve. The requirements are driven by the differing needs of data producers and the data consumers, who often have different goals in mind.
Introduction Paleoclimatology is a highly integrative discipline, often requiring the comparison of multiple data sets and model simulations to reach fundamental insights about the climate system. Currently, such syntheses are hampered by the time and effort required to transform the data into a usable format for each application. This task, called data wrangling, is estimated to consume up to 80% of researcher time in some scientific fields  (Dasu & Johnson, 2003) , an estimate commensurate with the experience of many paleoclimatologists, particularly at the early-career stage. Wrangling involves not only identifying missing values or outliers in the time series but also searching multiple databases for the scattered records, contacting the original investigators for the missing data and metadata, and organizing the data into a machine-readable format.
Further, this wrangling requires an understanding of each data set's originating field and its unspoken practices and so cannot be easily automated or outsourced to unskilled labor or software. There is therefore an acute need for standardizing paleoclimate data sets. Indeed, standardization accelerates scientific progress, particularly in the era of Big Data, where data should be Findable, Accessible, Interoperable, and Reusable (FAIR;  Wilkinson et al. , 2016). Standardization is critical to many scientific endeavors: efficiently querying databases, analyzing the data and visualizing the results; removing participation barriers for early-careers scientists or people outside the field; reducing unintended errors in data management; and ensuring appropriate credit of the original authors. While the paleoclimate community has made great strides in this direction (e.
g. ,  Williams et al. , 2018) , much work remains. The recent adoption of the FAIR data principles  (Wilkinson et al. , 2016)  by the American Geophysical Union  (Stall et al. , 2017)  elevates the urgency of defining what data and metadata should be archived, and how. This article proposes a community-recommended set of preliminary reporting standards and an open platform to determine which metadata are important for public archival, with an eye toward maximizing the long-term value of hard-earned paleoclimate observations and ensuring optimal reuse. The need for standardization in paleoclimate research is beyond vocabulary agreement. Consider the editorial of  Wolff (2007) , which tackled the ambiguous definition of time in the paleoclimate community. The notation before present (BP) has become a de facto standard in the community, although "present" means different things to different people.
It is often taken as Common Era (CE) 1950 (especially within the radiocarbon community), undefined, or defined as some other date (e. g. ,  CE 2000) , or the year the study was performed/published. For studies spanning several million years with age uncertainties in excess of 1,000 years, a 50-year difference is immaterial. However, for studies working at higher resolution (e. g. , decadal to subannual), concentrating on recent millennia, this difference is consequential. Thus, an agreement over the precise meaning of the term present turns out to be critical to many uses of these data sets. The same can be said of many other metadata properties, underscoring the need for common practices in paleoclimate data reporting. Given this acute need for standardization, the National Science Foundation EarthCube-funded LinkedEarth project nucleated a discussion on data reporting practices.
For this system to function, one needs (1) a set of compartments and drawers to house the information, (2) labels to identify and classify the contents of the drawers, and (3) a disciplined adherence to the classification system. This entails including essential information required for application and reuse of the cards and the information they contain. In other words, every user follows similar guidelines to generate, use, and file the cards; otherwise, the classification falls apart and the cards may as well be stored in a random pile. This article focuses on the last requirement, namely, the creation of standards for reporting paleodata and metadata. It builds upon recent efforts to address the first two points. On the first point, the Linked PaleoData format  (LiPD; McKay & Emile-Geay, 2016)  and derived vocabulary agreements to describe paleoclimate data (the LinkedEarth Ontology;  Emile-Geay et al.
, 2019)  provide a data container for paleoclimate data (section 2), which is currently used in a range of data analysis software  (Bradley et al. , 2018; Khider et al. , 2018; McKay et al. , 2018). On the second point, the National Oceanic and Atmospheric Administration (NOAA) World Data Service for Paleoclimatology (WDS-Paleo) has created a set of standard names to document paleoclimate variables, the Paleoenvironmental Standard Terms (PaST) Thesaurus  (National Oceanographic and Atmospheric Administration, 2018). This article's aim is twofold: First, to provide a snapshot of the first version of the Paleoclimate Community reporTing Standard (PaCTS), as of 2019, with the understanding that this standard will eventually evolve, and second, to document the process of community elicitation of such guidelines, so as to provide maximum transparency on why and how these decisions were made.
The LinkedEarth Framework: An Online Approach to Standard Development The LinkedEarth project established an online  (Gil et al. , 2017)  that enables the curation of metadata for publicly accessible data sets by experts and fosters the development of terminology agreements and standards for paleoclimate metadata. Our approach builds on two synergistic elements: (1) the LinkedEarth Ontology  (Emile-Geay et al. , 2019) , which provides an unambiguous structure and terminology to describe the metadata of a paleoclimate data set, and (2) the LinkedEarth Platform  (Gil et al. , 2017) , which enables the collaborative authoring of highly structured metadata about paleoclimate data sets using the terms in the LinkedEarth Ontology. The LinkedEarth Ontology represents vocabulary agreements to describe paleoclimate metadata.
A feature of ontologies is that they allow the creation of machine-readable metadata, that is, data descriptions that can be queried programmatically by machines to retrieve data sets of interest. Thanks to the ontology, machines can navigate through metadata and discover data that otherwise would be hidden to them. LinkedEarth relies on semantic web technologies to represent ontologies, specifically the Web Ontology Language (OWL) standard of the World Wide Web Consortium (W3C; W3C OWL Working Group, 2012). More details are provided in  Emile-Geay et al. (2019). The LinkedEarth Platform allows users to (1) describe paleoclimate data sets using the terms available in the LinkedEarth Ontology and (2) propose new terms if they cannot find an appropriate one in the ontology. The LinkedEarth Platform is a sociotechnical system, and as such, it provides technology infrastructure coupled with social processes that support terminology and standards convergence.
Utilities in Matlab, Python, and R  (Heiser et al. , 2018)  allow users to interact with the files (specifically, to read, write, query, or filter data sets matching specified conditions). In many ways, LiPD is intended to be the netCDF of paleoclimate observational data. However, although both LiPD and the LinkedEarth Ontology provide a standard way to describe a paleoclimate data set, they say little about what information should be stored to ensure reuse. The endorsement of netCDF by a 10. 1029/2019PA003632.
Because PaCTS is focused on paleoclimate, we reused the relevant subset of the TRiDaS standard. A discussion regarding paleoceanographic data standards was started during the Paleoclimate Model Intercomparison Project (PMIP) Ocean Workshop 2013-Understanding Changes Since the Last Glacial Maximum (hereafter, PMIP LGM) in Corvallis, Oregon, in December 2013. Given the expertise of the working group members, the discussion focused on marine sedimentary archives and was summarized into a document, which is available on the LinkedEarth Platform  (Kucera et al. , 2013). Their recommendations served as the foundation for a preliminary reporting standard for records based on marine sedimentary archives. Although the group identified recommended properties to be included with marine data sets, they did not propose a complete vocabulary nor a subset of required properties for acceptance in a database.
Paleoceanography and Paleoclimatology the Holocene epoch  (Khider et al. , 2016). In the current research ecosystem, a typical workflow would consist of querying several databases to find suitable records, extract the data, consult the original publication(s) for additional metadata (e. g. , author's definition of present), reformat the data into a coherent format for analysis, apply spectral analysis to examine the frequency content of the records, perform some statistical analysis of the results, and visualize them. In an ideal world, the query, preferably from a single database, should (1) find records that span the Holocene, (2) find the subset of those that primarily reflect SST, and (3) find the subset of that subset with a specified resolution (e. g. , finer than 200 years) to have at least five data points per 1,000-year cycle (a permissive assumption for this sort of work).
Simple though it may seem, this query requires the following (meta)data: (1) a measure of age (time) and minimum and maximum values of the time series; (2) an estimate of SST, as an inferred variable, and/or Mg/Ca, U k' 37 , TEX 86 , or microfossil assemblages as measured variables from which SST can be inferred; and (3) temporal resolution, calculated from the data. Other types of basic queries include: searching for a particular publication, using either the DOI, title, journal, or authors; and searching by the type of archives. Defining the search parameters for these complex queries on the LinkedEarth platform  (Khider & Garijo, 2018)  sparked the discussion for the needed properties. A standard helps not only with the menial task of searching for records in a database. Such a standard can also assist with doing the science per se, by ensuring that the required information is present in the data set.
For instance, making a simple map of all the records in a database by archive types (Figure  1a  of PAGES2k Consortium, 2017) requires each data set to report latitude, longitude, and the archive type. More complex data analysis requires more information: to investigate the effect of age uncertainties (e. g. , with the Bchron  (Haslett & Parnell, 2008)  or BACON  (Blaauw & Christen, 2011)  packages, or to establish new depth-age models  (Blois et al. , 2011; Giesecke et al. , 2014) , one needs the raw radiocarbon measurements, their measurement uncertainties, and associated depth in the archive.
In addition to the properties identified, a data set DOI and a data set license would also promote data reuse. LinkedEarth is not set up to mint DOIs directly, but they can be obtained through other platforms such as PANGAEA, Dryad, or FigShare. The registry of research data repositories, re3data, gives information on whether a repository provides persistent identifiers. The Creative Commons (CC-BY) license is recommended for paleoclimate data since under this license, other researchers are free to share and adapt materials while giving appropriate credit to the original contributor of the resource.
Speleothems When constructing their database  (Atsawawaranunt et al. , 2018) , the SISAL WG identified 23 properties specific to speleothem records. The SISAL database only focuses on stable isotopes in speleothems, and these properties only apply to this proxy system. These properties can be further subdivided into four categories describing the cave and modern cave conditions, the physical sample, and information about the sample data. For new data sets, 11 properties were considered essential and 12 recommended. For legacy data sets, only 2 properties were considered essential and 21 were marked as recommended  (Figures 5, 6, and 11). Although evidence for equilibrium (e. g. , the Hendy test;  Hendy, 1971 , or monitoring data that supports equilibrium precipitation of calcite) was narrowly voted as essential for new data sets and recommended for legacy data sets, three respondents (two on Twitter and one on the survey) expressed concerns about the value of this property as it rarely shows up in monitoring data and the Hendy test has been abused by the paleoclimate community.
Paleoceanography and Paleoclimatology from the proposed tree-ring isotope databank  (Csank, 2009). TRiDaS was chosen as a starting point as it was designed as a standard to represent dendrochronological data across its many subdisciplines, including dendroclimatology. TRiDaS therefore includes many (optional) properties as essential or recommended that are not applicable to data sets collected for paleoclimate reconstructions. For new data sets, 26 properties were considered essential, 7 recommended, and 2 desired. For legacy data sets, 19 properties were voted on as essential, 9 as recommended, and 7 as desired  (Figures 5, 6, and 12). Several researchers were confused about the terms used in TRiDaS, suggesting that the standard may be too broad for most paleoclimate applications and should be further refined if it is to be widely adopted.
The reason for this confusion may be because TRiDaS was initiated by the cultural dendrochronology community (e. g. , dendroarcheology, art, and building history) in a response to the more pressing need for standardized metadata in these disciplines. Despite attempts to engage all subdisciplines of dendrochronology in the development of TRiDaS, the cultural aspects of the standard were more fully implemented due to the greater participation of users from these areas of research. Nevertheless, a subset of the fields defined in TRiDaS was used as a starting point for discussion for PaCTS v1. 0. Many fields within TRiDaS are already addressed in the cross-archive metadata and were disregarded, leaving only dendro-specific fields. These were then supplemented by fields for tree-ring isotope data taken from the tree-ring isotope databank proposed by  Csank (2009).
Documentary Archives Historical documents differ quite significantly from the other archive types presented in PaCTS v1. 0. Documentary data are extracted from written sources (books, chronicles, newspaper, etc. ), and each of these sources in the data set needs a reference to the publication metadata (in addition to the scientific publication of the data in a journal). The raw data most comparable to measurements on other archives are quotes; that is, text strings in any language cited from the source from which location, time, and event are extracted. Every single data point in the set can thereby have a different location and a variety of parameters describing the event  (Glaser, 1996). The time step can be, but is not necessarily, periodic. The quote might contain information regarding the temperature in a city, precipitation conditions, and the resulting water level in a river, as well as statements concerning harvest amount and quality of a certain crop.
Consider the example of radiocarbon dating. Each radiocarbon value is associated with an uncertainty that is often reported in a separate column of the measurement table. This radiocarbon-age uncertainty is then translated (via a calibration curve) into a calendar age uncertainty that is also stored in a separate column. In both of these cases, the uncertainty is a variable that can be described with the same richness as other columns in the data table. Furthermore, probabilistic age modeling software such as Bchron  (Haslett & Parnell, 2008)  and BACON  (Blaauw & Christen, 2011)  for radiocarbon, HMM-Match  (Lin et al. , 2014)  for stratigraphic alignments, and the Banded Age Model  (Comboul et al. , 2014)  return possible age distributions around the calendar age value and age model ensembles for each depth in the paleorecord.
Discussion This paper describes the first effort by the global paleoclimate community to define standards for digitally archiving paleoclimate data sets. Such standards aim to make publicly archived paleoclimate data more reusable by clearly describing them with comprehensive metadata. In combination with the LinkedEarth Ontology, these standards also help meet the interoperability principle by using a formal, accessible, shared, and broadly applicable language for knowledge representation. If the data sets are properly described using microdata (e. g. , Schema. org), they are also findable. Together, these standards bring such data sets closer to compliance with FAIR principles. The standards arose through collective discussions, both in person and online, and via an innovative social platform  (Gil et al.
We have also found PAGES-sponsored workshops to be excellent opportunities to discuss data stewardship considerations, of which reporting standards are an important aspect. At the very least, an annual session at an international meeting would be useful for the community to touch base and take stock of progress and challenges, but more frequent interactions will be desirable until adoption reaches a critical threshold (e. g. , 80% of submissions to public repositories like WDS-Paleo or PANGAEA). Assuming that such meetings will take place over the next few years in many corners of the community, there is still a need for more sustained forms of communication. The virtual working groups on the LinkedEarth platform are where many of our discussions took place, and they remain available to complement the in-person discussions.
, 2013) , as the information and noise content of each piece of data needs to be accurately quantified to avoid biasing a model  (Schneidman-Duhovny et al. , 2014). Here, we introduce a Bayesian approach  (Rieping et al. , 2005)  to model the structure of a macromolecular system by optimally combining cryo-EM data with other input information. Bayesian inference and maximum-likelihood methods are not novel to the cryo-EM field  (Scheres, 2012b; Sigworth et al. , 2010) , as they were initially introduced for aligning structurally homogenous sets of 2D images  (Sigworth, 1998) , and they are now widely used by software packages such as RELION  (Scheres, 2012a)  for single-particle reconstruction. In our approach, we use Bayesian inference in a way similar to that discussed in a recent paper  (Habeck, 2017) , i.
By accounting for both data noise and correlation, this approach enables an effective use of cryo-EM density maps in integrative structural modeling. In the following, we first outline our modeling approach and then benchmark its accuracy using synthetic low-resolution data of several protein/DNA complexes. Finally, we apply our approach to the integrative modeling of the GroEL/ES complex, as well as the RNA polymerase II and the exosome complexes, in which we combine cryo-EM with chemical cross-linking/mass spectrometry (XL-MS) data. This method is implemented in the opensource Integrative Modeling Platform (IMP) package (http://integrativemodeling. org) (Russel et al. , 2012) , thus enabling integrative structure determination of biological systems based on a variety of experimental data, including FRET and NMR spectroscopies, XL-MS, small angle X-ray scattering, and various proteomics data.
Modeling of the GroEL/ES complex The ADP-bound GroEL/ES is a 21-subunit molecular chaperone that assists protein folding in bacteria. We used cryo-EM data at 23. 5 Å resolution (EMDB code 1046)  (Ranson et al. , 2001)  and the crystallographic structures of the subunits (PDB code 1AON)  (Xu et al. , 1997)  (Fig. S4 ). The 100 best-scoring models grouped into 3 clusters, which were mainly different in the orientation of the GroEL-trans subunit (Fig. 5  and Table  S2 ). All three clusters presented a misrotation of the GroES subunit, which was due to the small size of the subunit and the low resolution of the map (de  Vries and Zacharias, 2012; Habeck, 2017; Kawabata, 2008). The rmsd of the best scoring model (Fig. S4 ) with respect to the reference structure was 9. 0 Å, with p(10) of 0. 98 and data-model CC of 0.
Integrative modeling of the RNA polymerase II The yeast RNA polymerase II is a 12-subunit complex that catalyzes DNA transcription to synthesize mRNA strands  (Armache et al. , 2005). To model this complex, we used the structures of all its subunits as determined in the RNA polymerase II X-ray structure (PDB code 1WCM)  (Armache et al. , 2005). We incorporated a low-resolution cryo-EM map of the RNA polymerase II-Iwr1 complex (EMDB code 1883)  (Czeko et al. , 2011)  and two XL-MS datasets (Fig. 6 , Fig. S5 , and Fig. S6 ). 997 of the 1000 best scoring models grouped in the first cluster. The rmsd of the best scoring model with respect to the reference structure was 32. 9 Å, with a p(20) of 0. 80 and a datamodel CC of 0. 52. The major contribution for the inaccuracy was the misplacement of subunit Rpb8.
There was a total of 9 violated cross-links (3. 5% of the total dataset), which involved the following subunits: Rpb1-Rpb1 (2 cross-links) Rpb1-Rpb2 (2 cross-links), Rpb1-Rpb4 (1 cross-link), Rpb1-Rpb6 (1 cross-link), and Rpb2-Rpb2 (3 cross-links) (Fig. 6E ).