dataesr/scientific-paragraphs-categorization huggingface dataset:
This integration of earlycareer researchers into the entire research team and effort will give early-career professionals deep knowledge of the database and procedures, as well as the skills to conduct such work in the future. (5) The decision of how to disseminate the database documentation needs to be considered early in the project. For example, database documentation papers are rare, especially in ecology, but are very important. The documentation and procedural approaches for developing this large, integrated, and heterogeneous database had to be disseminated through publication prior to making the database available  [17]  and prior to publication of research results stemming from LAGOS-NE because methods sections in journal articles are too short to include all the necessary documentation of such methods.
Other researchers may be discouraged by the very real consequence that publishing such products takes time and energy investments that may slow down production of research publications. However, such a paper was instrumental in supporting later research articles that used LAGOS-NE. Therefore, we recommend that this (and other) database documentation papers become a more standard type of paper to describe the extensive methods involved and to supplement data papers. Such papers will facilitate the use, extension, and translation of these databases well into the future, as well as foster future research on broadscale, complex, and societally relevant environmental questions.
Funding The creation of LAGOS-NE was supported by the  National Science Foundation (NSF)   MacroSystems Biology Program  in the  Emerging Frontiers Division of the Biological Sciences Directorate  ( EF-1065786 ,  EF-1638679 ,  EF-1065649 ,  EF-1065818 ,  EF-1638554 ) and the  United States Department of Agriculture National Institute of Food and Agriculture ,  Hatch  project  176820  to P. A. S. K. E. W. thanks the  STRIVE Programme  ( 2011-W-FS-7 ) from the  Environmental Protection Agency, Ireland. S. M. C. thanks the  NSF Division of Biological Infrastructure  ( 1401954 ). The water quality data that are incorporated into LAGOS-NE were originally funded by the following sources:  State of Maine ;  Michigan Agricultural Experiment Station ;  Fisheries Division, Michigan Department of Natural Resources ;  New York State Division of Water Quality ;  Wisconsin Department of Natural Resources ;  University of Wisconsin-Madison ; State/Trust;  Michigan State University Agriculture Experimental Station Disciplinary   Research Grant Program ;  US EPA ;  US EPA  Section  106/319  Grants;  Tribal General Fund ;  US Army Corps of Engineers Federal Lakes Operation and Maintenance Funds ;  Aquatic Plant Management Society; Aquatic Ecosystem Restoration Foundation ;  Michigan State University ;  Michigan State University Department of Fisheries and Wildlife ;  EPA  Star Fellowship to K.
Acknowledgments We thank the contributions over the past several decades of many hundreds to thousands of governmental, tribal, and citizen scientists whose efforts from lake sampling to water quality analysis to data set compilation enabled LAGOS-NE to become a reality and a resource for the future. Specifically, we dedicate this paper to the memory of  Jody Connor , whose 3 decades of innovative and science-driven lake management while working for the  New Hampshire Department of Environmental Services  generated meaningful contributions to the protection and restoration of lake quality and to lasting data legacies such as volunteer monitoring in the state. This is Great Lakes Environmental Research Laboratory contribution number 1863. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the  US Government.
Availability of supporting source code and requirements Project name: LAGOS-NE Project home page: https://github. com/cont-limno/LAGOS Operating system(s): e. g. , platform independent Programming language: R Other requirements: R packages required (with associated versions): dplyr (≥0. 7. 0), rappdirs (≥0. 3. 1), lazyeval (≥0. 2), purrr (≥0. 2. 2. 2), magrittr (≥1. 5), sf, curl (≥2. 7. 0), stringr (≥1. 2. 0) License: GPL.
8 31. 1 27. 9 21. 4 New York 126 070 4461 7. 6 1094 21. 9 9. 3 54. 1 7. 2 Ohio 106 917 1279 10. 6 1003 50. 0 14. 7 30. 9 1. 0 Pennsylvania 117 293 1755 9. 3 1109 22. 7 12. 3 59. 5 1. 6 Rhode Island 2809 253 10. 0 1246 4. 9 29. 5 44. 6 13. 6 Vermont 24 913 528 5. 9 1176 13. 3 5. 5 70. 0 4. 7 Wisconsin 145 295 6009 6. 6 831 36. 7 7. 5 35. 5 13. 7 This table includes the numbers of lakes and geophysical setting of each state and state averages for climate and the 4 major land use/cover types, which do not add up to 100% because we do not include all cover types. Temperature and precipitation data are 30-year climate norms (1981-2010; PRISM, http://www. prism. oregonstate. edu/normals/); land use/cover data are from the 2011 National Land Cover Database (NLCD; USGS, http://www. mrlc. gov). Note, border lakes are only counted in 1 state.
Data Data used in this paper are from NOAA-National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis  [Kalnay et al. , 1996]. Global pressure-level data were reprocessed into the HYSPLIT compatible format in the Air Resources Laboratory (ARL), NOAA, U. S. They are available from 1948 at 6 hourly temporal resolution and 2. 5°(latitude/longitude grid) spatial resolution with 17 levels in the vertical. Data have been downloaded from the ARLˈs archives (http://ready. arl. noaa. gov/gbl_reanalysis. php). For daily rainfall, we use a data set provided and validated by the National Climate Center (NCC), China Meteorological Administration (http://ncc. cma. gov. cn), with 753 stations from 1951 to 2010  [Lu et al. , 2014]. After a quality control, we removed those stations whose observation sites were changed or record lengths are not sufficient, we finally keep 11 stations in North China.
Variability of Precipitation in North China Previous studies indicated that precipitation in North China in the past decades had a strong interdecadal variability. There were two main regime shifts occurring in mid-1960s and 1980, respectively. The latter was believed to cause the long-lasting drought in the 1980s  [Zhou et al. , 2008; Ding et al. , 2008 Ding et al. , , 2009]]. In this study, we will first check these behaviors with the daily rainfall data set from NCC. The time series of regional accumulated precipitation in North China is calculated for the rainy season as from 19 July to 14 August and in our target area. Figure  1a  (red line) shows the time series of precipitation in North China; a notable interdecadal variation of precipitation can be found: a wet period from the 1950s to the mid-1970s, a dry period from the early 1980s to the early 1990s, and a relatively wet period appearing again from the mid-1990s onward.
Acknowledgments We thank the Editor and anonymous reviewers for their constructive comments. We acknowledge the  National Climate Center (NCC) of China  (http:// ncc. cma. gov. cn) for the observations and  NOAA , the  Air Resources Laboratory (ARL) , from ARLˈs archives (http://ready. arl. noaa. gov/gbl_reanalysis. php) for NCEP/NCAR reanalysis data. This work is supported by the  National Natural Science Foundation of China  ( 41675081 ), the  National Key Research and Development Program of China  (grant  2016YFA0600402 ), and a project funded by the  Priority Academic Program Development of Jiangsu Higher Education Institutions. Zhengyu Liu  is partly supported by the  U. S. NSF  and  DOE. Laurent Li  is partly supported by the  French ANR  project  China-Trend-Stream.
II. LPWAN : LOW POWER WIDE AREA NETWORK A. Principles of LPWAN LPWAN has been proposed to be the solution to the Internet of objects being supported by most media-driven technologies such as LoRa, SigFox and LTE-M. This technology allows the sending and receiving of messages of very small sizes, over very long ranges up to 40km. The major advantage of this technology is that the equipment set up in its network is very inexpensive and does not consume much energy  [2]. LPWAN technology is perfectly suited to connect equipment that needs to send small amounts of data over a long range while maintaining their autonomy. Some IoT applications may only transmit small amounts of information, such as a parking parking sensor, which transmits only when there is a change in state (vehicle detected yes or no).
This company has a cloud system for its web interface as well as the management of its devices (API Access Point Interface) and data configuration. The company offers a secure, responsive and efficient network with low throughput, but can support a wide range of products and sensors. The emission absorption for a SigFox modem can vary from 20 mA to 70 mA and its use is negligible when it is stagnant. The transmission power can be adjusted up to 14 dBm. The antenna radiation power should not exceed 25 mW. In the SIGFOX network, communication is two-way, meaning that devices send and receive data from a cloud platform. On this network, objects can send up to 140 messages of sizes equal to 12 bytes per day. This functionality is implemented as a polling, for which the object remains the leader, it avoids to remain permanently connected and allows him to ask the information system if there is information to download.
The technology is not free, each component LoRa must pay royalties to the company Sem Tech, originally LoRa, to benefit from its use. The architecture of the LoraWan network is generally presented in a star-shaped star topology in which the gateways are a transparent bridge connecting messages between the sensors and a central network server in the back end  [9]. The gateways are connected to the network server via standard IP connections, while the sensors use wireless communication to one or more gateways. All communications from the sensors are generally bidirectional, but they also support operation such as multicast, allowing software upgrade on the AIR interface or mass message distribution to reduce the communication time on air. The communication between the sensors and the gateways is distributed over different frequency channels and data rates.
Selecting the data rate is a compromise between the communication range and the length of the message. Using Spread Spectrum Technology, communications with different data rates do not interfere with each other, and create a set of virtual channels that increase the gateway's capacity. Lo-raWan data rates range from 0. 3 kbps to 50 kbps. To maximize the battery life of the sensors and the overall network capacity, the LoraWan network server individually manages the data rate and RF output for each terminal device by means of an adaptive data rate scheme (ADR Adaptive Data Rate).
The domain of biobanking has gone through many stages and as a result there are a wide range of commercial and open source software solutions available. The utilization of these software tools requires different levels of domain and technical skills for installation, configuration and ultimate us of these biobank software tools. To compound this complexity the biobanking community are required to work together in order to share knowledge and jointly build solutions to underpin the research infrastructure. We have evaluated the available tools, described them in a catalogue (BiobankApps) and made a selection of tools available to biobanks in a reference toolbox (BIBBOX) that are use-case driven. In the BiobankApps tool catalogue, both commercial and open source software solutions related to the biobanking domain are included, classified and evaluated.
All tools associated to a use-case are available as BIBBOX applications (technically this is achieved by docker containers), which are integrated in the BIBBOX framework with central identification and user management. In future work we plan to share the acquired knowledge w i t h o t h e r n e t w or k s , d e v el o p a n A p p l i ca t i o n Programmable Interface (API) for the exchange of metadata with other tool catalogues and work on an ontology for the evaluation of biobank software.
Due to the growing complexity of biobanking, a wide range of commercial and open source software solutions were developed in recent years. The tools are available in different development stages (from alpha version to production releases) and require different domain and technical skills for installation, configuration and use. At this time the number of solutions dedicated to the biobanks is growing. There are many different software approaches available, e. g. solutions to manage a biobank in a similar manner as a laboratory with the help of a LIMS, solutions dedicated to study-based biobanks, solutions with modules or solutions with extensions to join other working areas (genomic, imaging, etc. ) However, for the community of biobankers the main question is BWhich is the Bone^software tool I should use for my new biobank?^, or if they already work in a biobank BWhat is the solution to replace my existing homemade database, to work more efficiently?T hese questions imply many conclusions.
Related work The NASA Software Catalogue provides an overview about general purpose scientific software packages [2] and the EGI Applications database (AppDB)  [3]  collects metadata about software tools integrated with the EGI infrastructure. Both catalogues cover a wide range of scientific disciplines. In the life science and bioinformatics domain the European research infrastructure ELIXIR, provides the software tools platform https://bio. tools [4]  covering both a tool registry and service registry. The European bioinformatics community generated a curated registry and the associated EDAM ontology  [5]  by running several community-driven hackathons and knowledge exchange workshops. The ELIXIR tools and data services registry evaluates bioinformatics methods in terms of quantitative performance and user friendliness.
Further domain-specific catalogues of tools and web services are the BioCatalogue  [6] , BioDBCore  [7]  or myExperiment  [8] , just to mention some of the many catalogues and registries in the bioinformatics field. Stol and Babar  [9]  compare different open source software evaluation methods and Taibi et al. developed an OpenBQR, a framework for the assessment of open source software tools  [10, 11].
Examples of scenarios are a study-based DNA / liquid biobank; a clinical biobank focusing on cancer tissues and digital pathology or a collection of cell lines and plasma for a specific rare disease. Tools necessary to cover the functionality of a scenario are selected from BiobankApps and Bdockerized^, i. e. they are installed within docker containers, which can then be integrated and orchestrated in the BIBBOX (Basic Infrastructure Building BOX) framework. For this task the BIBBOX framework provides functionality for the deployment of docker containers, a central ID and user management and a process monitoring dashboard, see Fig. 3. In all scenarios the biobank operational module (BOM) covers the core functionality to operate a biobank, e. g. collection / study management, sample acquisition and sample metadata management, sample processing, sample storage, sample and data retrieval/distribution as well as data integration and cataloguing.
With the help of an ID management system data objects describing samples, patients or medical records are linked between the biobank operational module and all other Bdockerized^software tools. Each part of the biobank operational module is described with generic attributes, as defined by BiobankApps, and in addition by a functional classification as described in http://bibbox. org/biobankoperational-module. This list of functional requirements was generated on the basis of the ISBER Best Practices for Repositories, Collection, Storage, Retrieval, and Distribution of Biological Materials for Research by harmonizing several requirements and recommendation documents  [12]  and in addition gathering requirements through interviews with Biobank IT managers and IT representatives of BBMRI-ERIC national nodes.
BBMRI. uk (also known as the UKCRC Tissue Directory and Coordination Centre) have been undertaking work to try and understand the various motivations, concerns and profiles of different stakeholders in biobanking. The development and refinement of personas allows the different groups to be represented in a manner that can be easily communicated (https://www. biobankinguk. org/personas/). These personas can be used in any engagement events to test if they do indeed accurately represent the different users and further work can be undertaken to determine if subgroups exist within each  Each data object is named by an identifier (ID), which itself can be characterized with the following metadata attributes describing the & Scope of the identifier Global unique ID (GUID), e. g. the NIH Global Unique Identifiers  [14]  Locally unique ID (LUID), either in a BIBBOX scope or locally within an organisation/biobank.
Tool specific ID (TSID) here the ID is only unique within a specific tool. & Identifiability of personally identifiable information (PII) fully identifiable (FIID) i. e. name/location/type of the object is embedded in the ID and/or in the denoted data object. coded by organization ID (COID), i. e. all personal identifiable information was removed from the ID and denoted data object by the organization itself. coded by trusted third party ID (CTID) i. e. all personal identifiable information was removed from the ID and denoted data object by a trusted third party, the organization itself cannot re-identify a person. For all software tools installed in a specific BIBBOX instance the identifier management component describes this meta information for the different data classes used and provides a graph database for provenance description of data objects and their causal dependencies based on the Open Provenance Model  [15]  and the W3C provenance data model  [16].
Future outlook In the planning and setup of BiobankApps and the BIBBOX framework we faced technical challenges and had to decide on architectural issues and ontologies, but of equal importance we involved all stakeholders in the process and actively build a community. In the future we will further enhance the community building process by addressing the needs of different stakeholder groups (software developers, IT administrators and end users). In our community building strategy we will analyse the current needs and abilities of the community and understand what they care about. We will stimulate people to join, both for just visiting the tool catalogue and -most importantly -to actively contribute with their feedback, and we will connect the virtual catalogue to real-life events such as conferences and meetings.
The UK will be undertaking further work, both nationally and also across the BBMRI national nodes, to develop the understanding behind the use-cases, and the validation of the personas in order ensure that any services and software developed are in line with user expectations, and play to their motivations rather than their fears and concerns. As an example, there is a desire to explore the underlying motivation that may prevent the adoption of software tools. Although technical capabilities in biobanks are low, it cannot be expected that users will simply install and use tools once they become available. Ongoing evaluation and communication concerning the tools will be a continuing effort. On the technical side we will investigate the Open Archives Initiative Protocol for Metadata Harvesting OAI-PMH, the W3C Data Catalogue Vocabulary (DCAT) and the FAIR data exchange principles as possible protocols and API for exchange and harvesting of metadata with other tool catalogues.
Acknowledgements Open access funding provided by  Medical University of Graz. The work was performed and supported in the context of the  BBMRI-ERIC Common Service IT  and  Austrian Biobanking and BioMolecular Research Infrastructure  (BBMRI. at)funded by the  Austrian Federal Ministry of Science, Research and Economy  ( BMWFW GZ 10. 470/0016-II/3/2013 ). Acc. to B3Africa, the research leading to these results has received funding from the  European Community 's  Horizon 2020 programme  under grant agreement n°6 54404. We thank  INSERM, French National Infrastructure for the Medical Research , to give us support to start the project,  ANR ,  French National Agency  to fund those operations and the infrastructure BIOBANQUES to coordinate the projects and host the development team. We thank the work of the  University of Nottingham Information Services team  for their support of the project that is funded by the  Medical Research Council ;  Cancer Research UK ;  British Heart Foundation ;  Chief Scientist Office (Scotland) ;  National Institute for Health Research/Department of Health; Health and Social Care Research & Development Division, Public Health Agency, Northern Ireland ;  National Institute for Social Care and Health Research (NISCHR) /  Welsh Government ; and the  Wellcome Trust.
Both patients with DOC and those who are locked-in (LIS) or completely locked-in (CLIS) experience variations in cognitive functions, making an objective system to describe their functions desirable. BCIs have the potential to provide objective descriptions of remaining brain functions based on the classification of recorded EEG, evoked potentials and EEG analysis maps. Furthermore, BCIs can provide communication for some of these patients. BCIs for this type of application use either motor imagery or evoked potentials. For a motor imagery design, patients are asked verbally to perform certain imagined motor movements that will produce different event-related desynchronization (ERD)/event-related synchronization (ERS) patterns. The BCI system classifies the associated EEG data and reports the accuracy with which it can separate the EEG associated with different motor imagery instructions.
Trial goals are to determine duration and frequency of intervention, follow-up of clinical and neuroplasticityrelated benefit, and standardization of neurophysiological intervention outcome measures. An 'associative' BCI for lower limb motor rehabilitation provides timely coupling between brain commands and afferent response signals through detection of movement-related cortical potentials (MRCP) combined with functional electrical stimulation (FES)  [17]. Clinical efficacy in a cohort of chronic stroke patients has been shown  [18]  and this BCI is now being tested in acute patients. Preliminary data also show promising results for cognitive (attention and memory function) rehabilitation assisted by BCI-mediated neurofeedback in chronic and sub-acute stroke patients  [19]. Group discussion produced priority directions for further BCI development for stroke rehabilitation.
Future efforts should not concentrate exclusively on either the development of more effective decoding algorithms or the integration of evidence-based clinical principles to harness brain plasticity through task-dependent experience. A 'hybrid' approach pursued by multidisciplinary teams will best fulfill the complexity of the rehabilitation requirements. Further, the development of new algorithms to decode motor/cognitive 'intentional' signals should be physiologically driven instead of data driven. This will enable incorporation of the growing knowledge on brain reorganization after stroke damage. Such development will synergistically advance neuroscience questions relevant to translation of BCI into practice, such as identifying determinants of response-to-treatment and tailoring/shaping interventions according to patients' clinical and neurophysiological characteristics.