dataesr/scientific-paragraphs-categorization huggingface dataset:
Description of the LAGOS-NE LOCUS v1. 01 data module The LAGOS-NE LOCUS module includes data on the physical location, some features, and unique identifiers for all lakes in the study area ≥1 ha, which means this data file has information on 141 378 lakes. Note that, because we detected errors in the digitization of lakes between 1 and 4 ha, we have chosen to define our census population of lakes as only those ≥4 ha, but we still make data available for lakes smaller than 4 ha when available in this and the LAGOS-NE LIMNO data module. However, we recommend caution in analyses, interpretation, and inference for lakes <4 ha in this database that depend on NHD's spatial representation and detection of water bodies. The data in this module include lake unique identifiers, perimeter, area, latitude and longitude (which are typically the centroid of the lake or a central point that is within the lake boundary), GNIS name, and the zone IDs that the lake is located within (e.
Definition of lakes We defined lakes previously in Soranno et al. [17]  as follows. A "lake" in LAGOS-NE is a perennial body of relatively still water. We include lakes and reservoirs that range from being completely natural to highly modified: lake basins can be entirely natural, modified natural (i. e. , a water control structure on a natural lake), or a fully impounded stream or river (i. e. , a reservoir). We explicitly exclude sewage treatment ponds, aquaculture ponds, and detention ponds that are known to contain basins that are entirely artificial and were built for high-intensity human use. In addition, due to their unusual nature and size, we do not include the 5 Laurentian Great Lakes in our database. This definition of "lake" for LAGOS-NE has been developed only for the purpose of this database and its applications (e.
Lakes near and beyond the state borders For some of our analyses, we delineated boundaries in other ways than political boundaries that were more ecologically relevant, which resulted in the inclusion of some lakes outside of the exact 17-state border. This fact allowed us to include more in situ data collected by state and citizen sampling programs that do not always follow strict state borders and may include lakes that are outside of state lines. Although most of these border lakes have hydrologic (i. e. , lake connectivity measures) and topographic (i. e. , lake watershed delineations) calculations or water quality data, some measures of ecological context may be missing. For example, for lakes in Canada, we were not able to estimate any data that relied on national data sets that stopped at the Canadian border; one exception is the NHD, which extends into Canada to retain hydrologic boundaries.
Data sources of the LAGOS-NE LOCUS module Detailed information on data sources are found in Additional file 5 in Soranno et al. [17]. Briefly, the data source for lakes and streams in the 17-state area was the NHD [21]. The hydrologic boundaries (i. e. , for 3 of the spatial classifications, HUC12, HUC8, HUC4) came from the Watershed Boundary Dataset (WBD)  [23]. In addition, we used the digital raster data set of elevation for watershed delineation from the National Elevation Dataset  [24]. All download dates for these data sources are provided in Additional file 5 in the above citation.
Data-integration methods of the LAGOS-NE LOCUS module All methods to create this module are described in Soranno et al. [17]. The most challenging and time-consuming part of building this module was connecting the sampling locations from the lake water quality data sets (each contained different types of unique identifiers, and sometimes only lake names) to a georeferenced location in the NHD. When data providers included the lake latitude and longitude, we were able to mostly automate the procedure. Nevertheless, even when coordinates were available, there were many cases where the latitude and longitude did not intersect with the NHD lake polygon boundary, requiring manual interpretation.
Quality Control of the LAGOS-NE LOCUS module The full description of error analysis for this module is described in Soranno et al. [17]. However, here we briefly describe our efforts to determine the minimum area of a lake that we could confidently represent using the NHD (further details located in Additional file 9 in Soranno et al. [17] ). Although the NHD is a national data set, it is updated and edited regionally (often at the state level) by local practitioners familiar with each study region. As a result, there are regional differences in the resolution and digitization of water bodies, particularly for small water bodies, making it difficult to quantify or document even nominal error rates, or rather, the minimum lake size that is well-represented in the NHD. It has been documented previously that the NHD may not successfully identify small water bodies due to a variety of reasons including the resolution of the original underlying data of the NHD database, errors in digitization, and hydrologic changes since the time of map creation (e.
Description of the LAGOS-NE LIMNO v1. 087. 1 Data Module The LAGOS-NE LIMNO module includes in situ measurements of lake water quality. We included variables that are most commonly measured by state agencies and researchers for studying eutrophication (water quality data and metadata, including chlorophyll a, Secchi depth, water color, DOC, total and dissolved phosphorus [P], nitrogen [N], and carbon [C]) (Fig. 2 ). For each water quality data value, we also include metadata as additional columns in the exported data table (metadata including analytical method, data qualifier from the program, detection limit [when available], and the LAGOS-NE censor code) (Fig. 2 ), including the analytical methods, qualifiers with data flags from the original program (qual, which is not standardized for LAGOS-NE), detection limits (if available), and standardized censor codes from our quality control procedures (censorcode, standardized for LAGOS-NE).
Data sources of the LAGOS-NE LIMNO module We acquired individual water quality data sets for LAGOS-NE LIMNO by contacting individuals at each of the 17 state and 5 tribal agencies. These contacts helped us to identify the state agency-collected data set required by the Clean Water Act that was most likely to be in the public domain. In this way, we were able to acquire at least 1 (and typically more) data set from each of the 17 states. Because state and tribal agencies vary in sampling approach and intensity (see below for details), we sought to supplement these data sets with other known sources of water quality data, including university researchers, federal agencies, and non-profit groups, to integrate into the LAGOS-NE LIMNO module. The full list of data sources acquired is in Soranno.
Data integration methods of the LAGOS-NE LIMNO module All methods to create this module are described in Soranno et al. [17]. Briefly, for each data set acquired, we authored LAGOS-NE metadata in EML to aid in data provenance (included in this paper). We also incorporated key metadata features (e. g. , methods used, censor codes, if applicable), and sampling program information) into the database so that future users could easily identify these important attributes. Because each data set was unique in structure, file format, and naming conventions, we manually processed each data set and its metadata so that they could be translated into the standard LAGOS-NE vocabulary and data model. Although labor-intensive, we created customized R scripts to process and load each data set separately (included in this data paper).
For all versions of LAGOS-NE LIMNO , phases I and II are conducted on the entire cumulative data set to leverage as large of a sample size as possible to detect problem values. In other words, because many of the QAQC analyses outlined here make use of all information from an individual lake or variable, incorporating new data may result in a better assessment of the data than when there are fewer data. Thus, for each new version of LAGOS-NE LIMNO , new decisions are made about egregious values. In this data paper, we describe the procedures for assessing all major versions of LAGOS-NE LIMNO , but we present the results only for this version of LAGOS-NE LIMNO (v1. 087. 1). Because there are few accepted practices for conducting such quality control on a large, integrated database, we created our own procedures for phases I and II by creating tests to identify egregious values that leverage a large, integrated database with multiple measures of water quality and well-established expected relationships among variables.
The database that we used to identify egregious values was based on data in the full LAGOS-NE LIMNO database for samples taken from all lake depths provided by the source data sets (note, our data exports in this data paper are only for epilimnetic or surface samples). While the quality control procedures that we implemented here were designed to help resolve the large and egregious errors in a combined data set such as this, there are likely additional extreme values in the database due to the size and heterogeneity of the data. Users may want to check for additional issues in the data values specific to their intended analyses.
Data in the LAGOS-NE LIMNO module All data in LAGOS-NE LIMNO v1. 087. 1 are from samples that we identified as being collected from either the lake surface or the epilimnion (the well-mixed surface layer of a thermally stratified lake during the period of stratification). Because we did not have lake temperature data to quantify the exact epilimnion depth in all lakes, we used information from the source data sets to either determine epilimnion depth or to select data from only the top water layers. Although we received data from different depths in lakes, the majority of the samples were from the surface or epilimnion. The database includes samples from any season of the year. However, most of the published analyses to date have focused on the summer stratified period. Lakes are not sampled the same way by all individuals, groups, or agencies; there are differences in the variables measured, the frequency and timing of sampling, and the proportion of lakes sampled.
Data integration methods of the LAGOS-NE GEO module All methods to create this module are described in Additional files 5, 7, 8, 13, and 14 in Soranno et al. [17]. Briefly, we calculated the metrics for this module that describe the ecological context surrounding lakes by developing project-specific GIS tools in the ArcGIS environment, which are referred to as the LAGOS GIS Toolbox  [33]. The toolbox outputs multiple individual data tables of calculated values organized by the above 3 data themes that are then imported into LAGOS-NE GEO for different spatial classifications, including values calculated at the level of the individual lake, 100-meter and 500-meter buffers around each lake, the lake IWS, states and counties, hydrologic units, and ecological drainage units (an ecoregion spatial classification).
The unique identifiers for this data module are the zone IDs for each spatial classification for which we calculate these metrics. In other words, we calculate land use around a lake in each of the zones of the many spatial classifications in LAGOS-NE. However, the data are exported into individual tables by spatial classification. Therefore, there are different numbers of rows in each table; for example, there are 51 101 rows for the land use metrics calculated for the 100-meter lake buffer because there are 51 101 lakes that have a 100-meter buffer area, but only 17 rows for the land use metrics calculated for the state spatial classification.
Quality control of the LAGOS-NE GEO module The full description of error analysis for this module is described in Additional file 14 in Soranno et al. [17]. The quality control procedures for this module included procedures to identify possible errors or improbable values as a result of the extensive automated GIS data processing that creates the LAGOS-NE GEO data tables and to correct those problems. We assumed that the     original data layers had already gone through extensive quality control by the originators of the data sets. We defined errors and improbable values to be: (i) values that did not make ecological sense; (ii) values that were well beyond what has been observed in previous studies; (iii) values that are not technically feasible; or (iv) null values that indicate an absence of data, when in fact data exist based on the input data coverages.
Research to Date Using LAGOS-NE Prior versions of this database have supported numerous peerreviewed publications to date. In particular, LAGOS-NE is ideally suited for studying the local to regional controls of water quality through both space and time because of the large number of lakes with in situ water-quality measurements and their wide gradients of ecological context. The lake census data set also makes it possible to quantify the types of biases present in the data set to assess the potential influence of uneven sampling efforts on results across both space and time. Below, we describe the types of research questions that have been and are being addressed using LAGOS-NE, organized according to 3 main topics related to studying water quality across space and time in thousands of lakes.
Methods and database development for macrosystems ecology Several of our lines of research have required the development of novel methods and the application of existing methods in novel ways. Much of the impetus for this work on methods and database development has been driven by 2 needs. The first was to further develop the database-i. e. , creating derived and predicted data as a new data product that is publicly accessible (e. g. ,  [28] ). The second was to better understand the spatial and temporal distribution of data contained in LAGOS-NE and to further our understanding of important ecological attributes of lakes across multiple spatial scales. These 2 needs are not mutually exclusive-analyses that have helped contribute data to LAGOS-NE have also addressed important ecological questions.
Three data gaps were identified early during database development, including (i) a lack of lake depth information (lake depth drives many in-lake processes), (ii) the need to develop a flexible method for creating ecological regions from multithemed mapped data, which are often used in macroscale research to account for broad-scale patterns and processes, and (iii) the need for developing ways to measure freshwater connectivity to account for the transport and processing of materials in lakes at broad scales. For the first gap, Oliver et al. [28]  used a linear mixed model to predict lake depth for lakes where in situ measurements were lacking, allowing the relationship between surface area and lake depth to vary by region because of the strong regional differences in this relationship. Predictions in some regions were far better than other regions, potentially due to differences in underlying geomorphology.
A further challenge in large, integrated databases such as LAGOS-NE is the well-known problem with data derived from analytical methods related to the issue of detection limits  [36]. Stow et al. (personal communication) studied the in situ concentrations that were too low to be quantified by standard analytical practices-measurements that are termed left-censored or below a detection limit of an analytical method. Unfortunately, detection limits were only sometimes reported (although we do include those data in LAGOS-NE LIMNO where available). In some cases, low values were flagged as being censored, with an explanation as to the reason for censoring the data value, but in other cases the reason for censoring was not clear. In some instances, patterns in the data suggested that ad hoc substitutions for censored observations may have occurred without clear documentation.
However, to date, there have been no studies to examine these issues comprehensively across broad scales and to examine which drivers are most strongly related to eutrophication status in lakes. LAGOS-NE is very well suited to answer these types of questions. For example, nearly 3000 lakes were examined for trends in nutrients and chlorophyll from 1990 to 2013 using LAGOS-NE  [45]. Across all lakes, nitrogen has declined, and phosphorus and chlorophyll have not changed. Nitrogen and stoichiometric changes in lakes were related to atmospheric deposition of nitrogen, providing key insight into large-scale nutrient transport and policies such as the Clean Air Act. Using only citizen science data in a subset of the LAGOS-NE database, Lottig et al. [46]  showed results that suggested little evidence for major declines or improvements in water quality.
Using LAGOS-NE for Future Research, Management, and Policy To facilitate the potential future use of LAGOS-NE, we have thoroughly documented the database and its methods  [17] , and here we share LAGOS-NE data with the broader research community. In this data paper, we include a wide range of research products, including the water quality and ecological context data, the GIS coverages underlying much of the analyses on freshwaters, and an R package that facilitates use of LAGOS-NE  [47]. This package includes functions to retrieve, store, and interact with the LAGOS-NE database, which works across many different operating systems. The package should increase the ease with which users of the database are able to access the data and documentation while maintaining a reproducible workflow. Key motives for constructing this database included interest in examining lake nutrients and productivity at multiple spatial and temporal scales, fostering broad-scale aquatic ecology and macrosystems research in an open-science platform, and providing new understanding and resources for management and policy-makers.
In addition, we have collaborated with boundary organizations and decision-makers. For example, under development is a dashboard of the ecosystem services provided by lakes for use by land managers. In addition, we have helped the state of Michigan determine lake-specific nutrient standards. Our hope is that this database and the associated support tools and documentation serve as a powerful resource and a foundation for future research and decisionmaking by a broad community of scientists, policy-makers, and natural resource managers. Indeed, our success and experience with database construction and research have inspired us to expand the spatial extent for LAGOS-NE. We have begun to build LAGOS-US, which will include similar data as LAGOS-NE but will be for the continental United States.
Challenges and Recommendations for Creating Large, Integrated, and Heterogeneous Databases We found that the largest challenge when creating this database was integrating many small heterogeneous data sets that had few common standards. Although creating such large, integrated data sets using fully automated procedures may happen someday, it appears that we are nowhere near such automation today. Until standards in metadata documentation and robust ontologies are created and widely adopted when creating local or regional data sets, future efforts to integrate these into larger databases will have to rely on close collaborations among domain experts and ecoinformatics professionals, extensive manual interpretation of individual data sets, and funds sufficient to implement these labor-intensive approaches  [16].
The economic value of water quality data in an integrated database This extensive effort was supported by a US National Science Foundation grant that totaled $2. 4 million, along with resources from other projects. Our team ranged in size from 14-20 individuals across the 6 years of the project, with many members compiling and integrating data, authoring metadata, creating new data products, and implementing quality control procedures, resulting in a tremendous number of person-hours. However, when one considers the cost of the data collection for the water quality data in the first place, the expense of this postprocessing integration work is not as large as it sounds. Sprague et al. [16]  suggest that a single sample (estimated for collecting nutrient or chemistry data from streams) ranged in cost from $2000 to $6000 per sample.
If we assume similar rates for lake sampling, but lower the cost as some aspects of lake sampling may be cheaper than stream sampling and multiply that cost (estimated as $1000-$4000 US) by the total number of records of nutrient or chemical samples in LAGOS-NE (n = 589 909), then the combined estimate to collect the water quality data found in LAGOS-NE is in the range of $0. 5-2. 4 billion US. It cost us between 0. 10% and 0. 40% of the cost to sample the data in the first place to harmonize these half a million records and to build an ecological context database for them. This relatively small investment in preserving, documenting, and harmonizing these valuable data sets creates the needed infrastructure for new broad-scale research, management, education, and outreach uses.
Strategies for broad-scale data integration efforts One challenge is to prioritize research areas and to identify the types of data sets that may benefit from a similar type of integration. State, federal, tribal, and citizen science water quality data sets were an excellent source of quality data for integration and conducting broad-scale research on aquatic systems. There are likely other such data sources that would benefit from being integrated as we have done here. We recommend the following strategies to make the best use of future data integration efforts. (1) The database integration effort should be driven by key underlying research questions or goals and grounded in a strong conceptual foundation of the important features to include. In our case, the principles of landscape limnology  [12, [18] [19] [20]  guided the development of LAGOS-NE, which helped us to prioritize geospatial and lake features for inclusion in the database because the addition of any data type or data set cost time and money.
(2) For databases with more than 1 major data type, it is very helpful to build the database in modular form, each with its own versioning system, specific data integration methods, and quality control procedures. This strategy was not a primary goal at the outset of our project, but it emerged somewhat organically through the life of the project. We now recognize the many benefits that the modularity brings to the database, including making it much easier to be dynamic rather than static by providing a platform for the addition of new data, new types of data, and new modules in the future (such as for biological data or data from high-frequency sensors). (3) The entire process should be grounded in an open-science framework. Knowing that the database, design, and methods were to be shared and made usable by future users influenced our decisions throughout the process and made documentation a high priority throughout.
Although we are making the full database available now, before this point, we supported open science by publishing subsets of LAGOS-NE data that were used in individual publications (e. g. ,  [48, 49] ). (  4 ) Creation of LAGOS-NE required a strong focus on team science, and in particular the roles of and incentives for earlycareer researchers in such efforts. This type of research cannot be conducted in a single-investigator mode, but requires a highly collaborative and effective team-based model (e. g. ,  [50] [51] [52] ). We explicitly considered strategies for ensuring that early-career team members get credit for their contributions  [53] , and we recommend providing team members with opportunities for leadership, project management, personnel management, and intellectual growth. For example, they can be part of major decisions and can lead smaller efforts throughout the project, as well as be given power to shape team policies and practices.