dataesr/scientific-paragraphs-categorization huggingface dataset:
However, because of their large data volume, they are not amenable to long-term or permanent archiving. Nevertheless, long-term archiving of model-related information in some form that makes datasets reproducible is required but not yet done routinely. We suggest the following as a best practice: 1. Metadata should be permanently archived even for operational datasets (as mentioned above, this is required for a dataset to qualify as FAIR). 2. Essential subsets of operational datasets should be permanently archived, although it may not be immediately clear what these subsets should encompass. At a minimum, data subsets that would are required to support conclusions in publications should be archived. Expectations will likely emerge through a community process. 3. Model code should be permanently archived (e.
In this Chapter, an updated metadata template (Version 2. 0) is presented to accommodate the documentation of data coming out of marine carbon dioxide removal (mCDR) research (Table  2 ). It specifically allows users to indicate the type of the OAE study, and indicate whether its treatment type is for future ocean acidification conditions or for ocean alkalinity enhancement experiments. It also has a new element for the name of the model. For the "people" sections, the address field is split up into road address, city, state/province, zip code, and country for better machine-readability. The original title and abstract are replaced with "dataset title" and "dataset description", respectively, to make them distinguishable from the title and abstract of a peer-reviewed publication. The names of some other metadata elements were also changed to make them more self-explanatory.
A new metadata element called platform type, which is backed with controlled vocabularies, is added to allow data users to filter the datasets based on the type of the specific observing platform. For example, in the future, a user would be able to search for only Saildrone USVs-based measurements. For the funding information section, two new elements about the start date and end date of the project are added. Most importantly, an element is added to enable multiple datasets generated out of a research expedition or experiment to be linked to each other. Terms that were either obsolete or rarely used, e. g. , spatial reference system, purpose, section, etc. , were discarded. One of the most important elements of the above metadata template is the "variable metadata section"  (Jiang et al. , 2015).
It enables all ancillary information of a variable to be organized around the observed property (Table  3 ). Note that here "variables" refer to observed oceanographic properties, e. g. , temperature, salinity, dissolved oxygen, pH, Nitrate, etc. They should not be confused with other supporting variables such as EXPOCODE, Cruise_ID, year, month, day, yearday, longitude, latitude, depth, flags, etc. The latter elements are important for understanding the dataset, but the "variable metadata section" as described here is not applicable to them. Note that Table  3  shows the available metadata elements for a generic oceanographic variable. Customized variable metadata sections for ocean carbon variables (DIC, TA, fCO2, and pH) allow additional information to be documented. Refer to the metadata template file for more details about these metadata elements (https://www.
Moving forward, it is important to develop clear guidelines and standards to foster collaboration and communication among different communities. Specifically, it is recommended to manage controlled vocabularies for different types of information separately. Imagine the CF convention only has one clean term called "Dissolved inorganic carbon", with a preferred unit of "µmol/kg". The list will be significantly shorter, and each of the terms will be much broadly used. It would also be much more cost-effective to manage a shorter list. Ideally, such vocabulary development efforts should be driven by the scientific community to ensure their accuracy and the developed list will conform to the needs and  There are three important considerations when it comes to minting DOIs for datasets. Firstly, it is advisable to avoid using different DOIs for different versions of the same dataset.
Instead, it is recommended to mint one DOI that covers all versions of the dataset. This approach ensures that users with a DOI can always access the latest version of the dataset, as well as any historical versions. To differentiate between versions, the citation for the dataset should include its version information. Secondly, it is crucial to wait until the dataset is published in a long-term archive with a stable link before minting a DOI. A DOI is only as reliable as the link it resolves to, so it is essential to ensure that the link is stable and will not change in the future. If the link changes later on, the DOI will become broken. Thirdly, it is important to ensure that only one DOI is assigned to a dataset in the data flow. It is not uncommon for a dataset to be submitted to a data assembly center (DAC), and be carbon and acidification data of a specific type.
Upon discovering a dataset through the Portal, the user can then return to the respective regional DAC to access the data files and locate pertinent metadata information. In order for the above mentioned federated system to work, each of the DACs would need to meet these standards as below: 1. A long-term archive to ensure uninterrupted data access into the future. 2. Strict version control capabilities, preserving all historical versions of a dataset on a permanent basis. 3. An online submission interface, enabling users to prepare metadata in a machine-readable format, and to upload data files. Ideally, it should incorporate a user profile management interface, enabling users to keep track of all historical submissions, and resume a submission at a later time. 4. A community-driven common metadata template to support the management of comprehensive metadata information needed for ocean alkalinity enhancement research.
g. , the Surface Ocean CO2 Atlas (SOCAT), and Global Ocean Data Analysis Product Version 2 (GLODAPv2). OCADS manages a wide range of ocean carbon and acidification data, including chemical, physical, and biological observations collected from research vessels, ships of opportunity, and uncrewed platforms, as well as laboratory experiment results, and model outputs  (Jiang et al. , 2023). It has an established setup to channel incoming datasets to existing data products, such as SOCAT and GLODAPv2. OCADS welcomes submissions from scientists and institutions around the world. Follow this link to access the homepage of OCADS: https://www. ncei. noaa. gov/products/ocean-carbon-.
forwarded to another DAC for different purposes later on. To avoid the risk of confusing users with multiple versions of the same dataset in different places, it is essential to make sure that only one DOI is minted for the authoritative version of the dataset. According to the NOAA plan to increase Public Access to Research Results (PARR) (NOAA, 2015), only NOAA National Data Centers are authorized to mint DOIs for NOAA funded datasets. https://doi. org/10. 5194/sp-2023-6 Discussions Preprint. Discussion started: 23 June 2023 c Author(s) 2023. CC BY 4. 0 License. 6 Data repositories Instead of attempting to create new data repositories specifically for OAE data, it is recommended to upgrade existing OA data repositories to accommodate OAE data. After all, most types of OAE data are very similar to OA data.
https://doi. org/10. 5194/sp-2023-6 Discussions Preprint. Discussion started: 23 June 2023 c Author(s) 2023. CC BY 4. 0 License. acidification-data-system. Genetics or eDNA data is an exception and should be sent to the National Center for Biotechnology Information (NCBI). In Europe, in situ OA data are typically submitted to National Oceanographic Data Centres (NODCs) along with other types of measurements. Some research groups may also submit their OA data to specialized data assembly centers like SOCAT or publish their experimental data through data publishers like Pangaea. Government monitoring agencies in Northern Europe typically send their OA data to the International Council for the Exploration of the Sea (ICES). Data centers may then integrate these data with other measurements in their databases using controlled vocabularies and standardized metadata elements.
S. West Coast California Current System during the 2011 West Coast Ocean Acidification Cruise (WCOA2011) from 2011-08-12 to 2011-08-30". Dataset description is similar to the abstract of a publication, encompassing essential information on data collection and generation methods, the variables and attributes present in the dataset, as well as any limitations or restrictions on data usage. No. Component Description Controlled vocabularies 1 Submitter Information about the submitter, including full name, institution, address, email, phone number, a persistent digital identifier (e. g. , ORCID), etc. https://orcid. org/ https://doi. org/10. 5194/sp-2023-6 Discussions Preprint. Discussion started: 23 June 2023 c Author(s) 2023. CC BY 4. 0 License. 2 Investigators Information about the investigators, including their full names, institutions, addresses, emails, phone numbers, a persistent digital identifier (e.
g. , ORCID), etc. This component can be repeated as many times as needed. https://doi. org/10. 5194/sp-2023-6 Discussions Preprint. Discussion started: 23 June 2023 c Author(s) 2023. CC BY 4. 0 License. 12 Sea names Names of the seas where the data collection takes place, e. g. , Gulf of Mexico, Puget Sound, Baltic Sea, etc. Controlled vocabularies for the types of the platform (e. g. , research vessel, ships of opportunity, fish vessel, oil tanker, mooring, Saildrone, glider, Argo float, etc). See Table 7 for a list of controlled vocabularies for this element. https://doi. org/10. 5194/sp-2023-6 Discussions Preprint. Discussion started: 23 June 2023 c Author(s) 2023. CC BY 4. 0 License. Moreover, it may provide instructions on accessing and utilizing the data. Here is an example of a well-crafted dataset description: "This dataset contains discrete bottle (CTD profile) data of the first West Coast Ocean Acidification cruise (WCOA2011).
Dataset. https://doi. org/10. 7289/v5jq0xz1. Accessed on 2023-03-15. " 13 Floating air-water equilibrator Equilibrators [SDN:L05::EQUIL] http://vocab. nerc. ac. uk/collectio n/L05/current/EQUIL/ An "h"-shaped bubble equilibrator assembly commonly used in MAPCO2 systems on moorings. For more information, refer to Friederich et al. (1995). Equilibrators While seawater is passed through a membrane, CO2 in the 14 Membrane equilibrator [SDN:L05::EQUIL] http://vocab. nerc. ac. uk/collectio water diffuses across the membrane and equilibrates with the gas mixture, which is then analyzed to determine the n/L05/current/EQUIL/ CO2 concentration. Such flasks are typically made of glass and have a capacity 15 Flask for discrete carbon dioxide measurement n. a. of around one liter. Seawater samples are collected from a specific depth using a Niskin bottle or other sampling device and transferred to the flask without exposing them to the air.
GPR hyperpararameters are optimized component-by-component through a maximum a posteriori (MAP) procedure informed by POD. We provide a detailed analysis of the reducedorder model performance on a two-dimensional case study corresponding to a turbulent atmospheric boundary-layer flow over a surface-mounted obstacle. We show that near-source concentration heterogeneities upstream of the obstacle require a large number of POD modes to be well captured. We also show that the component-by-component optimization allows to capture the range of spatial scales in the POD modes, especially the shorter concentration patterns in the high-order modes. The reduced-order model predictions remain acceptable if the learning database is made of at least fifty to hundred LES snapshot providing a first estimation of the required budget to move towards more realistic atmospheric dispersion applications.
It also provides higher order statistics such as concentration fluctuation and turbulent scalar flux fields that are not available with RANS. Yet, LES demands much larger computational cost, which is a bottleneck for application in an uncertainty quantification context. Many sources of uncertainty in microscale CFD simulations come from large-scale atmospheric conditions that are difficult to represent due to their intrinsic variability, but also from limited knowledge on emission source properties. To deal with these uncertainties and build a multi-query uncertainty quantification context suitable for CFD, one promising approach consists in substituting the parametric CFD model by a reduced-order model. The reduced-order model can be seen as a statistical model that has learned from a reduced CFD database, the relationship between uncertain input parameters and the flow/tracer quantities of interest, and that can then estimate the CFD model response over a wide range of parametric variation  [Margheri and Sagaut, 2016 , García-Sanchez et al.
, 2017 , Lamberti and Gorlé, 2021]. For example,  García-Sanchez et al. [2017]  studied how atmospheric uncertainties (inlet wind magnitude/direction and ground roughness) propagate on plume dispersion in Oklahoma City based on ensemble RANS simulations combined with polynomial chaos expansion. In this work, we aim at designing and evaluating a reduced-order model suitable for LES. One key issue when building a CFD reduced-order model relates to the high dimension of the CFD outputs and in the possible nonlinear input-output relationship that is difficult to learn from a limited training database. This is especially true in a LES setting that requires more computational resources than RANS, implying that the training stage can only rely on a very limited LES database. To make the reduced-order model approach tractable in a LES framework, we adopt a two-step reduced-order model approach.
The first step is to reduce the LES output dimension, i. e. by projecting the quantities of interest on a reduced basis using proper orthogonal decomposition (POD)  [Margheri and Sagaut, 2016, van den Bos and Sanderse, 2017]. The second step is to interpolate (or to metamodel) the POD coefficients on the uncertain input parameters. The resulting reduced-order model can then be used to predict the quantities of interest for any input parameter entry. Since a reduced basis generally accounts for non-affine input/output relationships, basic interpolation techniques for the POD coefficients mapping may fail unless a sufficient number of LES samples is used during the training stage. In the literature, a wide variety of methods have been applied for predictive regression learning on numerical data such as neural networks  [Hesthaven and Ubbiali, 2018 , Swischuk et al.
They noted the need for interpretation of reduced-order model predictions, but also the choice of the reduced-order model approach depending on available data. The POD/GPR reduced-order model approach we propose in this work is rooted in this need for interpretability and physical understanding of the reduced-order model predictions. In this study, the objective of our POD/GPR reduced-order model is to emulate mean (time-averaged) tracer concentration field statistics obtained with LES for a two-dimensional boundary-layer flow interacting with a surface-mounted obstacle. We consider this simplified test case compared to actual urban canopy to have the capacity to generate a very large LES database and to analyze the reduced-order model accuracy with respect to uncertain inflow boundary conditions and emission source location, while remaining near to validation experiments that have been well-documented through field trials and physical modeling  [Peterka et al.
e. turbulent scalar flux) is also presented to investigate the capacity of the reduced-order model to emulate other coupled LES statistics. We also identify the minimum learning database required to obtain acceptable reduced-order model performance to prepare for more realistic atmospheric dispersion applications. The outline of this paper is as follows. Section 2 presents the selected test case and the numerical configuration to generate the LES database. In Sect. 3, we present our POD/GPR reduced-order modeling approach. Section 4 analyses in detail the performance of our reduced-order model with focus on POD truncation, prior information for GPR hyperparameter optimization, and sensitivity to training data. 2 Large-eddy simulation of turbulent flow over a surface-mounted obstacle This section presents the two-dimensional test case and the numerical configuration considered in this study, with a focus on the modeling of inflow boundary conditions and parametric uncertainties.
Numerical solver The LES database is obtained with the AVBP 3  LES code developed by CERFACS  [Schönfeld and Rudgyard, 1999]. AVBP solves the filtered compressible Navier-Stokes equations on unstructured mesh. Additional advection-diffusion equations are solved for passive scalar dispersion. It is widely used to predict non-reactive and reacting unsteady flows in simple or complex geometry, and is applicable to pollutant formation and atmospheric dispersion  [Paoli et al. , 2020]. In the low-Mach context of atmospheric dispersion, an artificial compressibility approach  [Ramshaw et al. , 1985]  is used to increase the time-step resulting from the Courant-Friedrichs-Lewy (CFL) condition. The numerical discretization is based on an explicit, centered scheme from the continuous Taylor-Galerkin family called TTG4A, which is third-order in space and fourth-order in time  [Colin and Rudgyard, 2000].
Choice of quantities of interest LES is used here to simulate unsteady flow and tracer features, and to obtain reference statistics (temporal mean) of the flow velocity and tracer concentration fields across the domain. Figure  2  shows several instantaneous tracer concentration fields for a given snapshot of the LES dataset. This snapshot can be considered as the nominal solution since it is representative of the average atmospheric conditions considered in this study (Sect. 2. 4). The emission source, located upstream and at the height of the square, illustrates the complexity of the dispersion process, which is driven by a combination of the quasi-periodic vortex shedding induced by the flow/obstacle interactions and the inflow turbulence. The main quantities of interest targeted in this study are the time-averaged tracer concentration coefficient spatial fields obtained with LES (an extension to the turbulent scalar flux is also presented in Sect.
In this study, tracer emission can occur upstream, downstream or above the obstacle. The range of variation is chosen so as to cover a broad panel of existing experimental studies  [Mavroidis et al. , 2007 , Gamel, 2015 , Du et al. , 2020]. The marginal distributions associated with the source position and height are set uniform as: x src ∼ U([-3. 5, 3. 5]) m, z src ∼ U([0. 2, 2. 0]) m. ( 6 ) The range of variation for the source height is slightly away from the ground surface to avoid boundary issues. The area [0, 1. 2] × [-0. 2, 1. 2] is removed from the range of variation to avoid having a source inside the obstacle. 2. 5 Full-order model database.
Dataset acquisition In this study, we consider different sources of uncertainty in the LES full-order model: i) uncertainties associated with the large-scale atmospheric flow conditions µ atm (affecting the inflow and surface boundary conditions), and ii) uncertainties with the tracer emission source characteristics µ tr. These uncertainties are described by uncertain scalar parameters that are considered as inputs to the LES problem (Sect. 2. 4), and that form the input vector parameter: µ = (µ atm , µ tr ) T = (u zc , z 0 , x src , z src ) T ∈ R 4. (7) The input parameters impact the simulated flow response and drive the quantities of interest K les = {K 1 ,. , K N h } T ∈ R N h , namely means of tracer concentrations predicted at the N h grid elements of the discretized computational domain (the nominal snapshot example is given in Fig.
Experimental sampling design In this study, we build a dataset of 750 LES in order to train and carefully test our reduced-order model (this dataset can be considered as large from a LES perspective but still remains limited from a statistical learning perspective). Each snapshot of the dataset corresponds to different realizations of the input vector µ = (u zc , z 0 , x src , z src ) T. The samples are obtained using Halton's low-discrepancy sequence, since the number of input parameters remains small and we need to have a good coverage of the uncertain space for a limited number of samples. Note that we restrict the area of interest on the x-axis to the interval  [-3. 5, 13. 5 ] m, containing 99. 9% of the overall ensemble variance (this area still encloses the space of uncertainty on the source location position x src and height z src ).
Moreover, all LES were conducted on the same numerical setting (same grid, convection scheme, LES model, etc. ). Since the LES setup is robust, the non-intrusive procedure is well decoupled from the LES model to only handle the parametric variability. The physical time of simulation necessary to achieve converged LES statistics is one challenge with the LES database generation to avoid introducing noise during the training stage. For each snapshot, the initial flow field is specified from the inlet boundary condition, and a spin-up is then necessary to establish turbulence throughout the computational domain and to obtain LES field statistics that are consistent with the presence of the obstacle. Characteristic time scales of the flow are extracted for each snapshot (the flow periodicity is based on the Strouhal number).
In a first step, the modes are obtained from POD. This procedure transforms LES data (i. e. projects the LES snapshots in a reduced space spanned by a set of parameter-independent functions {ψ l } l=1,••• ,L ), and thereby returns discrete reduced-basis coefficients from the original output fields (Sect. 3. 2). In a second step, regression metamodels based on GPR are trained to map the uncertain parameters µ onto the reduced coefficients {k l } l=1,••• ,L (Sect. 3. 3). The resulting reduced-order model (Eq. 8) can finally be used to estimate new quantities of interest K rb at unexplored parameter values µ * (i. e. at parameter values that are not included in the LES training database). The difficulties in building the reduced-order model are three-fold: i) the quantities of interest simulated using LES are of very large dimension (N h is on the order of 10 5 in this study); ii) the number of snapshots is limited due to the computational cost of a single LES (i.
Reduced-coefficient dataset The quantity of interest vector field can be projected upon the POD space. The projection can be expressed as: W : R N h -→ R L K les -→ k = Σ -1/2 Ψ T T (K les ), (14) where Σ -1/2 = diag(1/ √ σ 1 ,. , 1/ √ σ N h ) ∈ R L×L and Ψ ∈ R N h ×L are the matrices restricted to the L first modes (Sect. 3. 2. 1), k = [k 1 , • • • , k L ] ∈ R L is the vector of POD reduced coefficients (Eq. 8), and T (•) is the operator applying an affine transformation (centering and normalization) to the snapshot data: T : R N h -→ R N h K les -→ 1 √ N -1 (K les -Ê(K les )), (15) where Ê(K les ) = Ê(K les,1 ),. , Ê(K les,N h ) ∈ R N h is the mean of the quantity of interest over the snapshots for each grid element (Eq. 13). The linearity in the operator T makes it simple to express the inverse reconstruction operator from which the original quantity of interest field may be recovered from the compressed information carried by the POD reduced coefficients: K rb (µ) = T -1 L l=1 √ σ l k l ψ l.