dataesr/scientific-paragraphs-categorization huggingface dataset:
r-project. org/, last access: 16 December 2019). The ISRaD-R package primarily consists of the code that is used to assemble the database and perform the QA/QC checks for the ISRaD datasets. Users may ingest their own data within a local version of the ISRaD dataset by running the function ISRaD::compile(). This functionality is intended to allow researchers to interpret their own new or unpublished data in the context of ISRaD data. Additionally, the ISRaD-R package provides simple tools to download and quickly import the most recent ISRaD data into the R environment and produce basic data summaries and visualizations for the full dataset or user-defined subsets using report functions. www. earth-syst-sci-data. net/12/61/2020/ Earth Syst. Sci. Data, 12, 61-76, 2020 If no spatial coordinates are specified for a profile, this function fills those cells with the site coordinates, which are required for template ingestion.
CStocks Measurements of carbon concentration are not, on their own, good estimates of the mass of carbon in soils. Bulk density and soil layer depth information is also needed. If not measured directly, organic carbon concentration is filled with total carbon concentration (carbonates are accounted for only if reported). Then, with user-supplied bulk density, these values are used to calculate the mass of carbon in each soil layer (i. e. , C stocks). fill_expert In some cases, data measured at one extent may be reasonably substituted at another extent for the purposes of conducting comparisons across incomplete datasets. Original reported data can be merged with expert suggested data to provide unreported bulk layer values. These estimates are not from original studies and may be approximations, but they are useful for large-scale global analyses.
The ISRaD GitHub repository The source code for the ISRaD-R package is hosted under version control on the GitHub repository ISRaD (https: //github. com/International-Soil-Radiocarbon-Database/ ISRaD, last access: 16 December 2019; GitHub Inc. , San Francisco, CA). This platform is used to facilitate the open-source collaborative development of ISRaD data and additional database tools. Through the GitHub interface, users may (1) access data entries included in the compiled database; (2) evaluate and suggest modifications of the underlying code used for compilation, QA/QC, and calculation of the additional variables included in either the compiled ISRaD data object (ISRaD_data) or in the augmented data product (ISRaD_extra); and (3) report problems, questions, or other issues.
The soil carbon information hub For students or non-experts interested in learning more about the science behind the data, we have developed the Soil Organic Carbon Information Hub (SOC-Hub). The SOC-Hub (https://international-soil-radiocarbon-database. github. io/SOC-Hub/, last access: 16 December 2019) is a set of articles in the form of blog posts providing background information on soils, radiocarbon, the terrestrial carbon cycle, and soil models. A large portion of the content for this site was created by students and whenever possible uses nontechnical language to describe topics pertinent to ISRaD. Technical editing of the SOC-Hub is facilitated through the ISRaD GitHub repository. Users are welcome and encouraged to contribute to or improve the content in the SOC-Hub. We will update the SOC-Hub annually.
Accessing code Access to the source code underlying the ISRaD database compilation and calculations allows for users to check for errors and contribute to the functionality of ISRaD. Users with a registered GitHub account are invited to write code that adds to or improves upon the existing database tools. Using standard GitHub tools, users will submit a "pull request", and following code testing and evaluation of utility to the IS-RaD community, user-submitted code will be incorporated into the ISRaD-R package.
Reporting issues, making suggestions, and asking questions One of the most important tools available to ISRaD users is the ability to post questions, report issues, or make suggestions, including requests to incorporate new variables. We use issue tracking tools provided by GitHub to track and categorize user input including: suggestions for improvements; problems or errors with the website, the R package, code, or any other aspects of ISRaD; requests for new variables or issues related to existing variables (e. g. , incorrect acceptable ranges used in QA/QC); or asking questions related to template entry or any other aspect of ISRaD. While the GitHub issue-reporting functionality is the preferred means for reporting questions or issues with the database or process, it does require that users register a GitHub account.
Database versioning and archiving Official releases of ISRaD data will be issued periodically, following major structural changes to the database or after the ingestion of a substantial amount of new data. Versioning is tracked using a three-tiered sequential numeric identifier, i. e. , the version number (in the form of "major. minor. patch") in addition to the date of the most recent change. The major version (i. e. the first numeral of the version number) is used to track official releases. Following each official release a DOI will be issued and the data will be archived by Zenodo (https://zenodo. org/, last access: 28 March 2019) and the USGS Science Base repository. These archives will be maintained in perpetuity to facilitate reproduction of any analyses conducted using a past version of the database.
Ingestion of new data are considered minor updates and are tracked with the minor version number. Finally, the patch number of the version number, i. e. , the third numeral, is used for tracking patches and minor changes to the code. All changes to the data or code-base are maintained under version control and are immediately available through the GitHub repository or via the website; however, only official releases will be issued a DOI. To ensure repeatability of analyses and accurate citations, users accessing the ISRaD dataset should always record both the full version number and the date. The names of ISRaD data files reflect the dataset versioning using the following standardized structure: (data name)(v. X. X. X. )(date). (format), where data name reflects the file type and structure (e.
Data sharing between soil databases ISRaD is not the only soil database available to the international research community  (Malhotra et al. , 2019). The primary niche of ISRaD is the ability to synthesize soil radiocarbon data and provide a framework for comparing soil carbon fraction data. For other purposes, there may be other soil databases that are more applicable. However, as a benefit of adding data to ISRaD, we facilitate sharing of data ingested into ISRaD with other databases developed by the soil science community. At present, ISRaD has a reciprocal agreement with the International Soil Carbon Network (ISCN), which is focused on soil carbon content and related variables from bulk soils (i. e. , no isotope or soil fraction information). As per this agreement, the ISCN retrieves bulk-soil data from ISRaD and is responsible for filtering duplicate entries and incorporating any new data into the ISCN database.
Database governance ISRaD is a community effort with multiple contributors operating at different levels. Governance of ISRaD is required in order to ensure continuity of services and to plan for the future evolution of this data repository. The governance structure of the ISRaD is pyramid-shaped (Fig. 5 ). The ISRaD scientific steering committee (SSC) consists of a rotating group of seven scientists and data managers. The committee members are nominated and voted into service by a majority vote of the existing steering committee. The role of the steering committee is to determine the feasibility of major changes to ISRaD proposed by the community; to oversee data management, archiving, and establishment of cooperative agreements; and to coordinate activities and funding of affiliated institutions.
Database maintainers oversee the development and maintenance of the technical resources underlying ISRaD. For example, these individuals are responsible for overseeing GitHub pull requests and managing major changes in the IS-RaD data template and/or data structure. The ISRaD associate editor is a special case of maintainer, whose role also includes assigning submitted templates to expert reviewers (described below) and periodically rebuilding the database with new entries that have passed the expert review process. Data contributors are users who contribute data to ISRaD. Anyone can be a data contributor, provided they agree to the terms of use and follow the proper steps for contributing data to ISRaD. Within the pool of data contributors, individuals with significant experience working within the ISRaD structure may be designated, either by the steering committee or database maintainers, as expert reviewers.
These individuals are tasked to assist maintainers and oversee peer review of contributed entries. Although the automated QA/QC tools are designed to catch many common errors in the data ingestion process, review by these expert contributors ensures the integrity of the data within ISRaD. Finally, ISRaD data users are individuals who access IS-RaD or ISRaD-supported resources to utilize data and other resources rather than to contribute data. Anyone can be a data user, provided they agree to the basic user guidelines and terms of use described in the next section. Although the structure of the ISRaD governance pyramid is oriented around individual users, the nature of scientific research is often more group-focused. For example, teams of researchers generally work together to seek out funding and to conduct research.
Thus, in some cases a group or team of individuals may seek to utilize or modify ISRaD for their purposes. Such groups can petition the scientific steering committee to be formally designated as an ISRaD organization. This process should be followed when groups seek to leverage the ISRaD resources beyond the scope of a basic user or contributor. The steering committee will consider the scope of the work proposed by the group and, when appropriate, provide a letter of support for funding proposals. Approved organizations should nominate a member to serve on the steering committee and, in the case of organizations making large changes or additions to ISRaD, a data maintainer to coordinate the technical aspects of that work.
Database availability and user guidelines As detailed above, ISRaD is an open-source project that provides several ways for participation. ISRaD v1. 0 data  (Lawrence et al. , 2019)  are archived and freely available at https://doi. org/10. 5281/zenodo. 2613911. Anyone may share or adapt the ISRaD dataset, provided they do so in accordance with the Creative Commons Attribution 4. 0 International Public License (https://creativecommons. org/licenses/ by/4. 0/legalcode, last access: 13 March 2019), also referred to as CC BY. In addition, we strongly encourage ISRaD users to follow two simple guidelines for use: 1. When utilizing the resources provided by ISRaD, including the complete dataset, individually curated entries, or value-added calculations included in the Rpackage, users should cite this publication and reference the version of ISRaD that was used for their work (see Sect.
Conclusions and outlook ISRaD is an interactive open-source data repository specializing in radiocarbon data associated with measurements of soils spanning a broad range of spatial scales. The ISRaD dataset is unique in that it includes not only measurements of bulk soils but also measurements of soil water, gases, and the wide diversity of soil pools isolated through different fractionation methodologies. Most of the studies summarized in ISRaD were conducted with a goal of understanding the factors controlling timescales of carbon cycling in specific sites, regions, or biomes. ISRaD is an attempt to gather the data from these individual studies in one place and in the same format to facilitate comparisons and synthesis activities. There are three ways through which potential users can access ISRaD: (1) the web interface enables users to download the most recently compiled report formatted as a.
csv file, (2) the ISRaD-R package provides access to the compiled reports as well as visualization tools and R-based querying tools, or (3) the GitHub repository provides direct access to the source code for the ISRaD-R package as well as data from individual entries and the compiled database. Currently, the ISRaD dataset contains ∼ 12 000 radiocarbon analyses, which, at a typical cost of approximately USD 500 each, represent over USD 6 000 000 of research investment. By providing a useful platform for existing data, we hope to encourage the community to increase the effectiveness of that investment and to use the ISRaD platform as a repository to increase the impact of new results. Many opportunities exist for applying ISRaD data for improving our understanding of controls on soil carbon dynamics, for comparing different methodologies of characterizing soils, or for constraining soil processes in models ranging from profile to global scales.
The creation of ISRaD was a community effort. The initial effort to build ISRaD started with the USGS Powell Center working group on Soil Carbon Storage and Feedbacks but benefited greatly from early efforts of the International Soil Carbon Network and other individual efforts to compile soil fraction or radiocarbon data. Scientists from the Max Planck Institute for Biogeochemistry joined forces with the Powell Center group to greatly expand the scope and technical complexity of ISRaD. CRL, JBM, AMH, GM, CAS, SS, KH, JCB, SEC, GMc, and ST designed and built ISRaD and led the preparation of the paper. PL, OV, KTB, CR, CEHP, CAS, KM, and SD provided technical contributions, including coding, to the creation of the database and assisted with the ingestion of data. CH, YH, CT, JH, MT, and CEA provided large datasets or data compilations.
| ME THODS As described above, the MAAT  (Walker et al. , 2018 ; https://github. com/walke ranth onyp/MAAT) is a hyper-modular, multi-hypothesis modelling framework. MAAT is written in R (R Core Team, 2020) and provides a general framework and code structure for building models that allows for new processes to be added easily. Higher-level 'system models' integrate multiple processes into a coherent representation of a given system. A number of these system models have been coded into MAAT and in this study we use the leaf-scale enzyme-kinetic model of photosynthesis  (Walker et al. , 2018). MAAT also encodes process and parameter SA algorithms. In this study we used MAAT (tag: v1. 2. 1_walkeretal2020_GCB, commit hash: 09b1479). 1 ), (  4 ), (  5 ) and (  8 ).
First, the algorithm employs multiple parallel MCMC chains and parameter proposals are generated from randomly selected chain pairs. A scaling factor is used to scale the 'jump' distance of the new proposal from the previously accepted proposal. At approximately every fifth iteration the scaling factor is set to 1 to avoid convergence in local minima. The algorithm avoids the inefficiency that arises from updating all parameters of a chain simultaneously by updating only a randomly selected subset (the 'crossover') of the parameters on a chain with optimized probability. Outlier chains are identified based on the interquartile range of the posterior likelihood and replaced with the sample history of another randomly chosen non-outlier chain. Coded within the MAAT software framework, the DREAM algorithm used the high-resolution A-C i data to formally estimate the parameter values for V cmax,25 , J max,25 , and θ cj for each leaf.
| TBM simulations We use three (c) The Sheffield Dynamic Global Vegetation Model (SDGVM, tag: Walkeretal2020_GCB), a carbon-only model with multi-leaf-layer canopy photosynthesis scaling and also with an LAI optimisation scheme  (Walker et al. , 2017; Woodward & Lomas, 2004). The leaf photosynthesis model in both ELM and FATES is based on CBGB but with an electron transport function that includes a J max term (Equation  3a ). While SDGVM uses the FvCB model with a similar electron transport function (Equation  3b ). For more information about the models, see Notes S1. Models were run each using their common configurations and input datasets to allow for a cross-section of results. The goal was to assess the possible impacts of smoothing in global TBM simulations across a range of model types rather than to quantify the exact impact under a specific set of conditions.
ACK N OWLED G EM ENTS This research was supported as part of the  ORNL Terrestrial Ecosystem Science SFA and Next Generation Ecosystem Experiments-Tropics , funded by the  U. S. Department of Energy (DOE), Office of Science, Office of Biological and Environmental Research  and under U. S. DOE contract numbers  DE-AC05-00OR22725  to  Oak Ridge National Laboratory  and  DE-SC0012704  to  Brookhaven National Laboratory. MY was supported by  U. S. DOE  grant  DE-SC0019438  and  NSF-EAR  grant  1552329. RF acknowledges the support of the  National Center for Atmospheric Research , which is a major facility sponsored by the  National Science Foundation  under Cooperative Agreement 1852977.
DATA AVA I L A B I L I T Y S TAT E M E N T The Multi-Assumption Architecture and Testbed (MAAT) code is freely available (https://github. com/walke ranth onyp/MAAT). MAAT and TBM data, and postprocessing scripts are archived on the ESS-DIVE archive (https://data. ess-dive. lbl. gov/view/doi:10. 15485/168 2250, https://data. ess-dive. lbl. gov/view/doi:10. 15485/ 1682253). The raw A-C i data are freely available on the NGEE-Tropics project data archive (http://doi. org/10. 15486/ ngt/1674983).
Furthermore, this huge amount of data to process can affect the classification latency which can be crucial in real-case scenarios, such as prosthetic control. Neuromorphic technologies can be deployed to overcome these limitations since they allow real-time processing in parallel at low power consumption. In this paper, we present a fully neuromorphic sensor fusion approach for hand-gesture recognition comprised of an event-based vision sensor and three different neuromorphic processors. In particular, we used the event-based camera, called DVS, and two neuromorphic platforms, Loihi and ODIN + MorphIC. The EMG signals were recorded using traditional electrodes and then converted into spikes to be fed into the chips. We collected a dataset of five gestures from sign language where visual and electromyography signals are synchronized.
3). For the case of EMG, the data was collected using the Myo armband that senses electrical activity in the forearm muscles. The data was later converted into spikes to be fed into the neuromorphic systems. Here, we propose a feasible application to show the neuromorphic performance in terms of accuracy, energy consumption, and latency (stimulus duration + inference time). The performance metric for the energy consumption is the Energy-Delay Product (EDP), a metric suitable for most modern processor platforms defined as the average energy consumption multiplied by the average inference time. The inference time is defined as the time elapsed between the end of the stimulus and the classification. To validate the neuromorphic results, we are comparing it to a baseline consisting of the network implemented, using a standard machine learning approach, where the inputs are fed as continuous EMG signals and video frames.
MATERIALS AND METHODS In the following section, we describe the overall system components. We start from the description of the sensors used to collect the hand-gesture data, namely the event-based camera, Dynamic Vision Sensor (DVS), and the EMG armband sensor, Myo. We then describe the procedure with which we collected the dataset used for the validation experiments presented here and which is publicly available. Afterwards, the two neuromorphic systems under consideration, namely Loihi and ODIN + MorphIC, will be described, focusing on their system specifics, characteristics, and the model architectures that will be implemented on them. Finally, we describe the system that we call baseline and which represents the point of comparison between a traditional von-Neumann approach and the two neuromorphic systems.
EMG Sensor In the proposed work, we collected the EMG corresponding to hand gestures using the Myo armband by Thalmic Labs Inc. The Myo armband is a wearable device provided with eight equally spaced non-invasive EMG electrodes and a Bluetooth transmission module. The EMG electrodes detect signals from the forearm muscles activity and afterwards the acquired data is sent to an external electronic device. The sampling rates for Myo data are fixed at 200Hz and the data is returned as a unitless 8-bit unsigned integer for each sensor representing "activation" and does not translate to millivolts (mV).
DVS-EMG Dataset The dataset is a collection of five hand gestures recorded with the two sensor modalities: muscle activity from the Myo and visual input, in the form of DVS events. Moreover, the dataset also provides the video recording using a traditional frame-based camera, referred to as Active Pixel Sensor (APS) in this paper. The frames from the APS are used as ground truth and as input in the baseline models. The APS-frames provided in the dataset are gray-scale, 240 × 180 resolution. The dataset contains recordings from 21 subjects: 12 males and nine females aged from 25 to 35 (see Data Availability Statement for the full access to the dataset). The structure is the following: each subject repeats three sessions, in each session the subject performs five hand gestures: pinky, elle, yo, index, and thumb (see Figure  2 ), repeated 5 times.
DISCUSSIONS As it has been discussed in  Davies (2019) , there is a real need for a benchmark in the neuromorphic engineering field to compare the metrics of accuracy, energy, and latency. ML benchmarks, such as ImageNet for image classification  (Deng et al. , 2009) , Chime challenges for speech recognition  (Barker et al. , 2015) , and the Ninapro dataset containing kinematic and surface EMG for prosthetic applications  (Atzori et al. , 2014)  are not ideal for neuromorphic chips as they require high performance computing for processing. For example, floating point bit resolution, large amounts of data and large power consumption. There have been some efforts in creating relevant event-based datasets, such as N-MNIST  (Orchard et al. , 2015) , the spiking version of the widespread MNIST digits recognition dataset, N-TIDIGITS18  (Anumula et al.
, 2018) , the spiking version of the spoken digits recognition dataset from LDC TIDIGITS, and the DVS gesture recognition dataset from IBM  (Amir et al. , 2017). These datasets are either toy examples or are not meant for real-world applications. Here, we are introducing a hand gesture benchmark in English sign language (e. g. , ILY) using the DVS and Myo sensors. This kind of benchmark can be directly used as a preliminary test for Brain-Machine Interface (BMI)/personalized medicine applications. We have collected this dataset from 21 people and in this paper have benchmarked it on three digital neuromorphic chips, measuring the accuracy, energy, and inference time. We believe this work takes an important first step in the direction of a real use-case (e. g. , rehabilitation, sports applications, and sign interpretation) which we would like to encourage the community to use.