dataesr/scientific-paragraphs-categorization huggingface dataset:
Publication, credit and attributions Authorship issues are common in data aggregation projects due to unclear expectations and conflicting conventions across what are often large teams of collaborators. Indeed these problems are common in large collaborative projects  (Cooke and Hilton, 2015). This can be mitigated with an expanded co-author list to include both data providers, aggregators, and reanalysis teams, but requires significant project management and organizational overhead. As always with larger team science, we highly recommend a formal authorship policy prior to beginning database compilation including what the role of each contributor is and who are in lead manuscript positions, listed as co-authors, and listed in acknowledgements. While it can be tempting in a data aggregation project to fall back on what you are legally entitled to (i.
The most permissive is CC-0 or public domain license that puts no restriction on data use. A 'By-Acknowledgment' (BY) rider requests that the original data source be acknowledged in the derivative product in some way (sometimes this acknowledgement is specified, sometimes not). A 'Non-Commercial Use' clause restricts the sale of the data for commercial purposes. Finally a 'Share-Alike' or copy-left clause says that the data may be reused if it is released under the same license. A 'CC-BY' license In all cases database creation does not have to be a single push, but is ideally part of an ongoing synthesis effort, leading to the need for database versioning. The COSORE database is an example for such an approach (Bond-Lamberty et al. , 2020). After each major change (release) the database receives a new doi and is permanently archived on a repository.
Future of soil data The hope of big data is to have any data collected at any time anywhere in the world at the tip of your fingers (see Section B). For soil science the potential for long-term (multi-decadal) understanding is particularly exciting. Long-temporal coverage of soil data could lead to a better understanding of soil carbon sequestration potential to mitigate climate change, or better management of soils for crops. How do we attain these above futures, where data reuse is equally as valued as data production? We recommend implementing a core set of measurements and processes to facilitate soil data reuse. The recommendations in section 3. 1 are aimed at researchers collecting soil data who wish to ensure the long-term value and reusability of their datasets. These recommendations are also relevant for journals and peer reviewers of soil science research as a short checklist of key details that should be reported or addressed.
For data collected on privately owned land, such as on-farm research and observations, researchers may not be at liberty to release detailed location information publicly in order to protect landowner privacy  (Richardson et al. , 2015). There are efforts to bridge data sharing and data privacy. For example, the platform under development by the International Agroinformatics Alliance will integrate secure data storage, granular data permissions, and options to register privately hosted data to facilitate data discovery and sharing while protecting privacy  (Gustafson et al. , 2017). Clearly this is an ongoing discussion that will require more research and conversations with stakeholders. The advantages of high precision geolocations are significant; and regardless of the precision, the level of uncertainty in the provided geolocation is critical and often missing in archived datasets.
Location information enables soil data to be joined with the growing number of gridded global datasets that can provide key contextual information for interpretation and modeling. While there are privacy concerns in some locations, not reporting the location of a sample collection should be the exception and not the rule, especially in publicly funded research data. In addition to location, sample depth is also critical due to the variation of soil properties and processes with depth. Unfortunately over 60% of studies fail to report measurements of sample depth (i. e. layer defining upper and lower bounds) associated with soil data  (Yost and Hartemink, 2020). This is particularly critical to advancing our understanding of deeper soil properties and functions, but also relevant for the effects of surface tillage and grazing on managed lands.
Soils are temporally dynamic and time of collection can provide key insights into decadal level changes in soils. Soils change over time owing to pedogenesis, historical land use and increasingly, global climate change  (Tugel et al. , 2005; Richter et al. , 2011; Ellis, 2011; Harden et al. , 2018). Recording the time of collection for modern datasets can produce valuable returns in future reanalysis. Older datasets, consisting of historic measurements and archived samples, are increasingly valuable to track soil responses to global change. Such datasets can provide a window into the dynamics of how soil properties change, and should be a high priority for data rescue and documentation. Decadal scale soil records provide valuable information for the study of global change and land management, and therefore sites associated with older observations should be prioritized for re-survey efforts  (Hawkins et al.
, 2013). Decadal scale datasets have their own challenges. These data are often associated with a particular researcher or group that are typically released at the retirement of the primary investigator, and likely have been reformatted multiple times across several generations of storage systems and lab staff and may even be on analog storage and not digitized. Thus, these re-survey efforts can be combined with data rescue to be an incredibly valuable data product involving digitization of paper copies, or conducting structured interviews of personnel to enrich metadata of prior observations  (Karasti et al. , 2006). In the opinion of the authors, data rescue efforts are an underutilized resource in the field. By adopting these recommendations to record geolocation, depth of sample, and collection date we can greatly increase the value of soil data, extending the measurement reusability for future analysis.
How to harmonize? We touched on several common approaches to data harmonization in this paper. Often driven by a single research question or objective, data harmonization has historically been a laborious process carried out by a single or small group of researchers for a specific project. Based on our experiences in various harmonization projects, we propose a more community-centered approach moving forward, founded on the principles of open and transparent science. Outputs from these groups should include semantic tools like ontologies and shared vocabulary lists with clear and transparent governance, as well as a new community-centered approach to the practice of data harmonization and the resulting databases.
Annotating datasets with a common vocabulary forms the theoretical backbone of all data harmonization work. Whether this is a manual copy-paste from a source data table to a common data template, or creating a thesaurus that cross-references given data columns to some internal standard name, both processes rely on a vocabulary. Classical soil glossaries and lab manuals have been printed as dictionary style references that are difficult to transform into digital resources for both copyright reasons and technical ones. This vocabulary could be a valuable community resource but would require ongoing engagement with the research community to remain accessible, relevant, and up to date. Further extending this vocabulary into an ontology, that captures the relationship between terms in addition to their definitions, could drive the next generation of data-driven machine learning.
They are open, in the sense that anyone can join or contribute and are empowered through educational activities to participate. Transparency ensures it is easy to contribute and to understand decision making processes. Diverse communities can draw on a wide range of skill sets, from experience in soil processes to knowledge representation. They are also rewarding, furthering members' careers through creation of tangible products (for example citations or grant dollars) and opportunities for scientific leadership and service. While there are several approaches to achieve this, one possible workflow for the establishment of a new harmonized database might look something like the following: 1. Open application period and selection of team 2. Purpose setting and establishing a common vocabulary 3.
One of the first challenges with an interdisciplinary team is establishing agreement on goals and methods. This requires developing a shared understanding and vocabulary (e. g. through educational activities on computational tools or soils surveys and measurements). In an academic community, shared purpose is most easily motivated by a synthesis paper or research question. Data may provide a clear shared motivation but it's uses and governance processes need to be clearly identified and revisited regularly. Sustainable creation and curation of the harmonized database is essential to create relevant data products to serve a database's specific purpose and enable future reuse to address a variety of questions. Accessing, annotating and merging the datasets is a well-established technical process once the community tools and community of practice are in place.
Curation of databases could be patterned after the manuscript review process, where domain researchers review proposed database additions to ensure the accuracy of new contributions. This review process should keep the diverse needs and practices of the soil community in mind, including soil surveyors, field/lab experimentalists, and land managers. In the end, the synthesis of existing data is not the goal: it is their application to scientific problems. In that regard, successful product development from a database can encourage growth and adoption of the data resource by others.
Conclusions Soils are the foundation for our food and fiber system as well as a significant component of the global carbon cycle. As such, information and measurements of the soil system, from hydrological conductivity to soil carbon stocks to changes in nutrient content, are a key public good for a varied group of users. However, this valuable scientific resource is currently under-utilized due to many of the issues outlined above. We suggest data use and reuse could be facilitated by addressing issues along the database construction pipeline. We outlined database creation as a common set of steps: generation, discovery, access, curation, and publication. While this pipeline can look different depending on the skill sets, timeline, and funding structure of the researchers involved, we https://doi.
org/10. 5194/bg-2021-323 Preprint. Discussion started: 15 December 2021 c Author(s) 2021. CC BY 4. 0 License. summarized common pain points throughout this process, which can reduce the accuracy and usability of a database. Data collection, synthesis, and use are inherently human endeavors, and as such, breaks in this pipeline are often driven by lack of community awareness and practices. We put forth recommendations ranging from measurement prioritization to data harmonization decisions that can help move forward community practices around soil data. We recommend that contextual information like geolocation, depth of sample, observation time, and management history all be reported with soil measurements. Soil data harmonization requires the development of new semantic tools like vocabulary lists and ontologies that are co-produced by data and soil scientists.
A1 Field-warmed soils The template-driven approach to data harmonization is exemplified by  (Crowther et al. , 2016). In this study, individual researchers who collected data of interest (in this case soil field-warming manipulations) were contacted directly and invited to collaborate in a meta-analysis. A post-doc was tasked with creating a data template, and working with those collaborators to capture a representation of their study. These data were then appended into an integrated set of data tables and analyzed. By working with researchers directly, this approach captured both published and unpublished data and ensured a nuanced interpretation of the study results. This careful one-on-one approach combined with co-authorship on a high profile journal ensured that researchers were comfortable sharing data that they might have otherwise withheld from a joint publication.
A2 ISRaD The International Soil Radiocarbon Database (ISRaD) consists of an open-source database of soil and soil-related radiocarbon data. Additionally, ISRaD provides a continually-developing library of tools for data access and manipulation based in R  (Lawrence et al. , 2020). Radiocarbon data production is an expensive and time-consuming process but provides unique information on longer-timescale in situ processes. In addition, a global pulse in atmospheric radiocarbon content in the 1960's provides unique analytical power for data collected in the intervening decades to constrain models. Initial funding was provided by the USGS Powell Center and the USDA NIFA FACT program. Currently, the Max Planck Institute for Biogeochemistry provides both ongoing funding and staffing. Since 2015, over 300 studies have been compiled, though the data collection process is active and ongoing, and user-submitted data are also welcomed.
The project utilizes a standardized Excel template for data ingestion which each contributor fills out and submits to a designated ISRaD coordinator. The core unit of a data entry is the "profile", which is a unique spatial AND temporal identifier. All data must be matched to a profile, which is in turn matched to a "site", and the uppermost level of "entry", which identifies the publication from which the data originates. Such hierarchy is best preserved through vetting with both an automated and human-led QA/QC process. Therefore, prior to ingestion, data undergo both automated quality check and expert review for metadata consistency and data quality. All database and data handling tools are built in the open-access R computational language, and an official ISRaD code library is available through the R library repository, CRAN.
A3 Coastal Carbon Research Coordination Network The Coastal Carbon Research Coordination Network (CCRCN) was formed to accelerate the pace of discovery in coastal wetland carbon science by providing the community with access to data, analysis tools, and synthesis opportunities. Funded by a National Science Foundation Research Coordination Network, the project's primary staff includes a funded research scientist as well as several part-time data technicians. Besides organizing topical working groups and communities events, one of the primary engagements of the CCRCN is the development and maintenance of a database of carbon stock and sequestration in coastal marshes, mangroves, swamps, scrub/shrub, and seagrass. Both the database and its software is hosted on GitHub  (Holmquist et al. , 2018 (Holmquist et al.
, -2021)) , and its structure and naming conventions  (Holmquist, 2018)   datasets into the database is via scripted transcription, by which curation to CCRCN standards is performed in a unique "hook" script tailored for each dataset. A suite of helper tools aids unit conversion, quality control, and spatiotemporal processing specific to soil carbon data. Datasets are joined together to construct the multi-level database, partitioned primarily by scale of observation (depth series, core/plot, site, and methods levels). An automatically-generated bibliography tracks primary citations of data contributors alongside the secondary citation of the database itself. Post synthesis QA/QC script identifies possible duplicate plot-level entries between datasets. Internally-facing visuals and reports are generated via Markdown implementation to track database growth as well as geographic/biophysical gaps in the database.
Finally, the online version of the database feeds into the backend of its primary public interface, the Coastal Carbon Atlas  (Holmquist, 2021). This R Shiny App allows anyone to explore global representation, as well as query desired according to a variety of environmental/methodological parameters, then download the data and (importantly) the corresponding citations. Parallel to synthesizing the database has been a concerted effort to generate data releases, each with its own DOI, as a service to data submitters as well as the coastal carbon community. This has included dedicated staff time towards outreach, formatting of datasets, generating metadata (based upon the Ecological Metadata Language standards) and assigning DOIs to datasets, which has so far resulted in the public release of 22 datasets so far on the Smithsonian Figshare repository.
A4 SoDaH The Soils Data Harmonization (SoDaH) and Synthesis project features a tool suite for harmonizing soil organic matter data from disparate sources into a common data model, and a database of harmonized soil organic matter data and related variables that, as of this writing, includes data from over seventy unique studies  (Wieder et al. , 2020 (Wieder et al. , , 2021)). The product of a Long-Term Ecological Research (LTER) synthesis working group (LTER Soil Orgnaic matter Working Group, a), the project brought together soil scientists with diverse backgrounds and affiliations with scientific research networks to refine and evaluate theories of soil organic matter dynamics, and to produce a soil organic matter dataset that spans a wide range of environmental and experimental conditions.
SoDaH employs a keyed-translation approach using metadata about the data combined with conversion scripts to translate contributed data tables into the common data model. Metadata are organized at multiple levels, including around the study (e. g. , study location, data provider) and data variables, which are subdivided into profile, layer, and fraction categories. Additional metadata fields facilitate the identification of experiment details and study design, allowing users to, for example, query data associated only with specific manipulations or control conditions. The harmonization script (LTER Soil Orgnaic matter Working Group, b) maps user-provided metadata and data resulting in new flat file(s) in which the variable names and units, if relevant, are standardized in the output along with appropriate quality control.
Acknowledgements. We would like to thank several anonymous survey respondents for their contributions. This work is based on materials provided by the  ESIP Lab  with support from the  National Aeronautics and Space Administration (NASA) ,  National Oceanic and Atmospheric Administration (NOAA)  and the  United States Geologic Survey (USGS). James Holmquist  was funded by the  Coastal Carbon Research Coordination Network  ( DEB-1655622 ). Natasja van Gestel was funded by the  Department of Energy's Biological and Environmental Research  ( DOE-BER) program  ( DE-SC0010632 ).
miRNA regulation allows for unprecedented versatility in the outcomes of gene expression. In complex with ARGONAUTE (AGO) effector proteins, miRNAs bind in a homology-dependent manner to target transcripts and trigger their site-specific cleavage and/or translational repression (TR)  (Yu et al. 2017). On a cellular level, these silencing mechanisms can result in a complete suppression of target accumulation or serve as a rheostat to dampen the noise inherent in gene expression  (Voinnet 2009). When mobile positional information coordinates these outcomes on a tissue level, miRNA regulation can promote the formation of spatiotemporal patterns of target gene expression, or when in the rheostat mode, it can refine domains of target activity. Moreover, stemming from their ability to move from cell to cell through plasmodesmata  (Vatén et al.
When this ratio exceeds a certain threshold, miRNA activity leads to target clearance, but once this ratio falls below the threshold, regulation switches to a rheostat mode  (Skopelitis et al. 2017; Klesen et al. 2020). Whether the miRNA-to-target ratio affects the switch at the level of TR or transcript cleavage and what factors beyond those affecting miRNA-target recognition influence these regulatory mechanisms remain unclear. To address these questions in the context of the patterning properties of small RNAs, we next studied the relationship between miRNAs and their targets. To identify miRNA targets in the maize apex, a pipeline was produced implementing the TargetFinder tool and degradome (PARE) analysis, which assesses enrichment of 5 ′ uncapped transcript ends at the predicted cleavage site over chance occurrence (Methods).
Altogether, 106 target genes were identified, 104 of which are conserved in Arabidopsis (Supplemental Data Set S3). Given the high conservation of targets, an additional 48 target genes were identified by phylogenetic analysis (Supplemental Data Set S3). For 16 miRNA families, both precursor and target transcripts were found present in the apex. For the remaining miRNA families, either one or the other was not detected in any of the tissues sampled. In line with the developmental dynamics of the SAM, the miRNA-target modules identified serve developmental processes, rather than regulating physiological or stress responses (Supplemental Data Set S3). As a first assessment of the regulatory modes and mechanisms used by miRNAs during developmental patterning, we compared the combined transcript levels of targets across the apex domains to those of the miRNA precursors (Fig.
2004; Li et al. 2014a ). In addition, parameters such as the position and sequence context of the target site have been linked to efficacy of individual miRNAtarget interactions  (Li et al. 2014a; Liu et al. 2014; Zheng et al. 2017; Agarwal et al. 2018 ). However, globally, differences in the relative frequencies of PARE signatures in our data are not explained by distinctions in these parameters (Supplemental Data Set S4), and no correlation is evident between PARE-to-transcript ratio and the complementarity "strength" of these natural targets as defined based on TargetFinder score (Fig. 6B ). Likewise, the relative number of PARE signatures is not correlated to miRNA abundance, whether considering targets individually or combined on a per miRNA basis (Supplemental Fig. S4A, B ). However, an additional important variable to consider is the contribution of miRNA-to-target ratio.
Gene expression analysis RNA-seq data for the adaxial and abaxial tissue samples were processed and analyzed as described in  Knauer et al. (2019). Briefly, trimmed reads were aligned to the B73 RefGen_V3 using GSNAP  (Wu and Nacu 2010) , and uniquely mapped reads were used for subsequent analyses allowing two or fewer mismatches every 36 bp and fewer than five bases for every 75 bp. miRNA precursor expression values were calculated for all 12 B73 shoot apex samples based on the annotated precursor models. Target gene expression values were calculated similarly or collected from  Knauer et al. (2019). Heatmaps were generated using the R package "ComplexHeatmap. " The clustering method was set to the default "hierarchical clustering. " Expression levels were normalized using the formula y(i) = [x(i)min(x)]/[max(x)min(x)] where x(i) represents gene expression in different subdomains; i indicates the different tissues types; max(x) represents the maximum expression value of a given gene across the different domains; min(x) represents the minimum expression value of a given gene across the different domains; and y(i) is the normalized value ranging from 0 to 1.