dataesr/scientific-paragraphs-categorization huggingface dataset:
) has kept the burden on COSORE's designers and maintainers manageable; we are acutely aware that every additional field or piece of information imposes a cost, both immediately (for implementation) and in perpetuity (for maintenance). This was the rationale behind focusing initially on previously uncollated continuous measurements: to maximize scientific impact in terms of labour involved. In fact, nothing in COSORE's design itself precludes incorporation of spatially distributed, survey-style measurements. COSORE also remains relatively immature, with e. g. no 'level 2' data product incorporating external data (e. g. Fick & Hijmans, 2017). This imposes an additional cost-of time and effort-on database users to locate and integrate externally available data themselves. Finally, analyses using COSORE will be limited by the nature of soil respiration and other soil-atmosphere gas flux measurements, and the state of the disciplines' networks and community.
Automated measurements trade space for time: the systems are more expensive and require dedicated power, and do not perform well under certain conditions, limiting their spatial and temporal coverage at many scales  (Barba et al. , 2018). There remains no institutionally backed network akin to AmeriFlux or ICOS, and while there have been efforts to integrate chamber flux data into these networks' data products, this has inevitable consequences for continuity and consistency. There is also no standardization of measurement depths for ancillary measurements (e. g. soil temperature and moisture) in the manner of a top-down network such as NEON  (Schimel et al. , 2007)  or ICOS  RI (Op de Beeck et al. , 2018).
| Future directions As noted above, every expansion or addition to a database imposes   et al. , 2020) , for example. Currently, the COSORE team accepts flux data in any tabular format and performs unit conversion, restructuring and/or reformatting, etc. , as needed. This was useful in the database's initial stages, as minimizing the work for contributors meant increased submissions. We intend however to shift this responsibility to data contributors before version 1. 0, providing a template form that contributors must follow. This will allow for semiautomated data ingestion and follows the practices of many other earth sciences databases. Unusual or outlier measurements could also be automatically flagged for downstream users. More ambitiously, we have put substantial design work into ensuring interoperability so that COSORE data should flow relatively seamlessly into (or from) ESS-DIVE, Ameriflux and ICOS RI.
A long-term vision is that COSORE data could, for example, automatically be made available in the larger community database. It is crucial, we believe, that COSORE contributors have assurances that their data contributions are traceable across versions and that it is not necessary to prepare and submit their data to multiple repositories. Finally, currently all data are included in the COSORE R package download. While convenient for users, this model will likely break down when the database doubles or triples in data volume. At that point, the data will need to be hosted elsewhere and downloaded only on demand.
ACK N OWLED G EM ENTS The authors declare no conflicts of interest. This research was supported by the  US Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER)  as part of the  Terrestrial Ecosystem Sciences Program. D. S. C. was supported by the  AmeriFlux Management  Project funded by the  DOE's Office of Science  under Contract No. DE-AC02-05CH11231. S. C. P. was supported by  ESS-DIVE Community Funds Program , from the  Data Management program  within the  Climate and Environmental Science Division of DOE  BER. N. B. was supported by  Swiss National Science Foundation  project  ICOS-CH  ( 20FI20_173691 ). J. Z. was supported by a  joint Ph. D. program  grant ( 201206300050 ) from the  China Scholarship Council (CSC)  and  University College Dublin (UCD) ,  Special Project on Hi-Tech Innovation Capacity  ( KJCX20200301 ) and the  Excellent Youth Scholars program  from  Beijing Academy of Agriculture and Forestry Sciences.
C. L. P. and J. G. were supported by  DOE  # DE-FG02-05ER64048. C. L. P. is supported by the  USDA Agricultural Research Service  (project  2072-12620-001 ). The  USDA  is an equal opportunity provider and employer. J. W. R. was funded by  NSF   DEB-0236502  and  DEB-0703561. M. P. was supported by the  Swedish Infrastructure for Ecosystem Science. M. U. was supported by the  Arctic Challenge for Sustainability II  ( JPMXD1420318865 ) project and  JSPS KAKENHI  Grant Numbers  23681004  and  26701002. J. J. A. and J. F. P. -Q. were supported by the  National Commission for Scientific and Technological Research (Chile)  through grants  FONDECYT 1171239 ,  FONDEQUIP AIC-37  and the  Associative Research Program   AFB170008. Q. Z. was supported by  NSFC-NSF  collaboration funding (P. R. China -U.
script. Both the flat-file releases and cosore R package include extensive documentation, including an in-depth vignette included both in the package and online (https://rpubs. com/bpbon d/502069). The R package includes documentation available via R's standard help system. F I G U R E 2 Geographic distribution of COSORE datasets (N = 89), with point sizes corresponding to the number of records in each dataset. Map tiles show USGS land cover and national elevation data and are by Stamen Design, under CC BY 3. 0; data by OpenStreetMap, under ODbL; figure rendered using R's ggmap (Kahle & Wickham, 2013) Distribution of COSORE datasets (black markers) in global climate space (WorldClim 2, Fick & Hijmans, 2017) of mean annual temperature (MAT) versus mean annual precipitation (MAP). Background colours indicate the number of half-degree grid cells with each particular MAT-MAP combination.
Summary of COSORE's contributors table, which provides information on the researchers (at least one; there may be arbitrarily many listed) who measured and contributed each dataset. Columns include field name, description, class (i. e. type of data), units and whether or not the field is required (required fields are marked by an asterisk) Field name CSR_FIRST_NAME CSR_FAMILY_NAME CSR_EMAIL CSR_ORCID CSR_ROLE TA B L E 3 Field name Description Class Units Req. First (personal) name character Family name character Email address character ORCID ID; see https://orcid. org character CReDiT role; see https://www. casrai. org/credit. html character Description Class Units TA B L E 4 Summary of COSORE's Req. ports table, which provides information CSR_PORT Port (chamber) number; '0' integer * on the various multiplexed chambers means all ports that are frequently connected to a single CSR_MSMT_VAR Flux should be interpreted as: character * measurement analyser.
Summary of COSORE's data table, which holds the actual flux observations and accompanying time-stamped data. Columns include field name, description, class (i. e. type of data), units and whether or not the field is required (required fields are marked by an asterisk); although not indicated, at least one flux observation (CSR_FLUX_CO 2 or CSR_FLUX_CH 4 ) is required in every database row. Note that all data in this table are acquired at the point of GHG flux measurement; see TableS1for site-level data Field name Description Class Units Req. CSR_DRY_CO 2 Chamber CO 2 concentration during flux measurement numeric ppmv CSR_DRY_CH 4 Chamber CH 4 concentration during flux measurement numeric ppbv CSR_CO 2 _AMB Ambient CO 2 concentration at measurement chamber numeric ppmv CSR_CH 4 _AMB Ambient CH 4 concentration at measurement chamber numeric ppbv CSR_COMMENTS Comments character CSR_CRVFIT_CO 2 CO 2 flux computation method ('Lin' or 'Exp' for linear and character exponential) CSR_CRVFIT_CH 4 CH 4 flux computation method ('Lin' or "Exp" for linear and character exponential) CSR_ERROR Error raised by instrument or during import logical CSR_FLUX_CO 2 CO 2 flux (positive = to atmosphere) numeric µmol CO 2 m -2 s -1 CSR_FLUX_CH 4 CH4 flux (positive = to atmosphere) numeric nmol CH 4 m -2 s -1 CSR_FLUX_SE_CO 2 Standard error of CO 2 flux numeric µmol CO 2 m -2 s -1 CSR_FLUX_SE_CH 4 Standard error of CH 4 flux numeric nmol CH 4 m -2 s -1 CSR_LABEL Port/chamber label character CSR_PAR Photosynthetically active radiation inside measurement chamber numeric µmol photons m -2 s -1 CSR_PAR_AMB Photosynthetically active radiation outside measurement chamber numeric µmol photons m -2 s -1 CSR_PORT Port/chamber number integer * CSR_PRECIP Precipitation at measurement chamber numeric mm CSR_R2_CO 2 CO 2 flux computation R2 numeric fraction CSR_R2_CH 4 CH 4 flux computation R2 numeric fraction CSR_RECORD Record number within file integer CSR_RH Chamber relative humidity numeric % CSR_SMx Volumetric soil moisture at × cm (other CSR_SMx fields follow numeric m 3 /m 3 same format) CSR_SOIL_O 2 Soil oxygen level at measurement chamber numeric % CSR_Tx Soil temperature at × cm (other CSR_Tx fields follow same numeric °C format) CSR_TAIR_AMB Ambient air temperature at measurement chamber numeric °C CSR_TAIR Chamber air temperature numeric °C CSR_TWATER Groundwater temperature at measurement chamber numeric °C CSR_TIMESTAMP_ Timestamp of beginning of flux observation, written POSIXct * BEGIN YYYY-MM-DD HH:MM:SS CSR_TIMESTAMP_END Timestamp of end of flux observation, written YYYY-MM-DD POSIXct * HH:MM:SS CSR_VPD Vapour pressure deficit at measurement chamber numeric Pa CSR_WTD Water table depth at measurement chamber, positive numbers numeric cm are depth TA B L E 5.
COSORE data releases are available via Zenodo (as noted above), as well as the GitHub 'Releases' page at https://github. A release download is fully self-contained, with full data, metadata and documentation; a file manifest; a copy of the data license; an introductory vignette; a summary report on the entire database; and an explanatory README with links to this publication. An alternative way to access COSORE data, including minor updates between major releases, is to install and use the cosore R (R Core Team, 2019) package. This provides a robust framework, including dedicated access functions, dataset and database report generation and quality assurance and checking (see below). Because the flux data are currently included in the repository itself, the latter is quite large (compared to most Git repositories), ~215.
4 MB. (Note that the data are stored in R's compressed RDS file format; when loaded into memory, the entire database is significantly larger, ~565 MB. ) It thus cannot easily be hosted on CRAN (the Comprehensive R Archive Network), the canonical source for R packages. Installing directly from GitHub is however straightforward using the devtools or re- motes packages: com/bpbon d/cosor e/releases; we anticipate that institutional repositories such as ESS-DIVE (Environmental Systems Science Data Infrastructure for a Virtual Ecosystem, https://ess-dive. lbl. gov/) may host releases at some point in the future. Downloads via this page are flat-file CSV (comma-separated values), and readable by any modern computing system. Missing values are encoded by a blank (i. e. two successive commas in the CSV format)devtools::install_github("bpbond/cosore")library(cosore).
both immediate development costs and unending maintenance costs. Nonetheless, there are some areas into which COSORE could be expanded. Many automated systems record isotopes and H 2 O in addition to CO 2 and/or CH 4 , and these data could be incorporated at relatively low cost; N 2 O and NH 3 are other frequently measured GHGs. As noted above, downstream users would also benefit in the future from COSORE data premerged with global climate, ecological, field inventory or remote sensing data products. This feature is provided by the International Soil Radiocarbon Database (Lawrence.
Background: The amygdala and the hippocampus are two limbic structures that play a critical role in cognition and behavior, however their manual segmentation and that of their smaller nuclei/subfields in multicenter datasets is time consuming and difficult due to the low contrast of standard MRI. Here, we assessed the reliability of the automated segmentation of amygdalar nuclei and hippocampal subfields across sites and vendors using FreeSurfer in two independent cohorts of older and younger healthy adults. Methods: Sixty-five healthy older (cohort 1) and 68 younger subjects (cohort 2), from the PharmaCog and CoRR consortia, underwent repeated 3D-T1 MRI (interval 1-90 days). Segmentation was performed using FreeSurfer v6. 0. Reliability was assessed using volume reproducibility error (ε) and spatial overlapping coefficient (DICE) between test and retest session.
nmr. mgh. harvard. edu/) is one of the most widely used. Previous studies showed a general consistency between the automated and the manual segmentation of the hippocampus and the amygdala using the FreeSurfer software suite, both in healthy and clinical populations  (Tae et al. , 2008; Morey et al. , 2009 Morey et al. , , 2010;; Dewey et al. , 2010; Mulder et al. , 2014; Iscan et al. , 2015; Grimm et al. , 2015). Recently, a new atlas for the segmentation of the amygdalar nuclei and the hippocampal subfields  (Iglesias et al. , 2015; Saygin et al. , 2017)  has been released as part of FreeSurfer v6. 0. This feature allows the simultaneous segmentation of both structures, thereby avoiding structural overlap between them, which represents a major problem in neuroimaging studies involving these regions  (Amunts et al.
MRI processing All MRI scans included in this study underwent a visual quality check before processing, according to a previously published rating system  (Backhausen et al. , 2016) , and were processed on the same operating system and workstation using both the cross-sectional  (Fischl et al. , 2002)  and the longitudinal  (Reuter et al. , 2012)  standard pipelines implemented in FreeSurfer v6. 0. FreeSurfer  (Fischl et al. , 1999; Dale et al. , 1999)  is a software suite designed to analyze structural and functional neuroimaging data.
The Interclass Correlation Coefficient (ICC)  (Shrout and Fleiss, 1979 ) was included as further index of absolute agreement between the test-retest measures (ICC 2,1 )  (Rajaratnam, 1960). The ICC calculated (IBM SPSS Statistics for Windows, version 23) for all volumes of both the cohorts. All analyses used raw volumes instead of volumes corrected for total intracranial volume.
Statistical analysis Non-parametric statistic tests were used since they are more appropriate for small samples, for which normality of data distributions cannot be assumed. Statistical analyses were conducted using IBM SPSS Statistics for Windows, version 23. One-way Kruskal-Wallis tests (nonparametric version of ANOVA) were used to: i) compare the demographic features between sites, and ii) to test for potential effects of MRI site and vendor on reproducibility measures. Wilcoxon signed rank tests were performed to: i) compare the reliability of the cross-sectional vs. the longitudinal pipelines, and ii) test the effect of averaging two T1-weighted images (older cohort) compared to the use of a single scan (scan 1 and scan 2) on reproducibility metrics. The Mann-Whitney U test was used when comparisons involved only two groups.
, 2005; Konrad et al. , 2009). Taken together, these results support the usage of volumetric measures in multicentric MRI studies, while those spatial should be used more carefully, particularly in the old population. As reported in previous studies  (Marizzoni et al. , 2015; Morey et al. , 2010; Van Leemput et al. , 2009; Worker et al. , 2018) , the segmentation reliability was strongly influenced by the absolute volumes of the respective structures. Indeed, we observed a strong negative correlation between volume and ε and a strong positive association between volume and DICE, irrespective of cohort and region. Similar results were reported when considering the nuclei and subfields' groups, suggesting that data derived from smaller nuclei/subfields should be interpreted very cautiously. Finally, in the older cohort we evaluated the effect of the withinsession T1 averaging on the test-retest reproducibility of the amygdalar nuclei and the hippocampal subfields.
, 2017; Gryglewski et al. , 2019). Within this framework, our results would be useful for future multicentric neuroimaging studies in the field of psychiatry and dementia. In particular, the possibility to obtain reliable segmentation of nuclei/subfields with the advantage in terms of processing time compared to manual segmentation could facilitate the study of structural abnormalities and treatment responses in large multicentric dataset. Indeed, these nuclei/subfields could be used as diagnostic markers or as outcome markers in future clinical trials of potentially disease-modifying therapies. This work has several limitations. The major limitation was that, in the older cohort, the number of enrolled volunteers was only 5 per site, as already discussed in other articles on the PharmaCog project  (Jovicich et al.
Acknowledgements PharmaCog is funded by the  EU-FP7  for the  Innovative Medicine Initiative  (grant no. 115009 ). We wish to thank all the co-Investigators from our Study Group (WP5-PharmaCog/European Alzheimer's  Disease Neuroimaging Initiative ) (for the complete list see Supplementary Table 3). Moreover, we are grateful to all members and collaborators of the  PharmaCog  project, and particularly to  Luigi Antelmi  and  Daniele Orlandi. FB is supported by the  NIHlgR biomedical research center at UCLH.
Funding PharmaCog is funded by the  EU-FP7  for the  InnovativeMedicine Initiative  (grant no. 115009 ). Funding acquisition, Resources, Writing -review & editing. Roberto Tarducci: Investigation, Writing -review & editing. Magda Tsolaki: Funding acquisition, Resources, Writing -review & editing. Pieter J. Visser: Funding acquisition, Resources, Writing -review & editing. Jens Wiltfang: Funding acquisition, Resources, Writing -review & editing. Jill C. Richardson : Project administration. Giovanni B. Frisoni : Funding acquisition, Resources, Writing -review & editing,  Supervision. Moira Marizzoni : Conceptualization, Methodology, Investigation, Writing -original draft, Writing -review & editing, Supervision.
In this paper, we propose some techniques to support end-users when designing IoT applications that are not simplistic, thus cannot be built by simple rules or existing recipes using popular tools such as IFTTT  [13]. We propose a composition language, which allows one to write not only event-action rules, but also to compose these rules to impose an order of execution between two rules, a choice among several rules or a simultaneous execution of several rules. The writing of a composition expression is achieved using a user-friendly interface we implemented, which does not require a high-level of domain expertise. Building such advanced compositions can be error-prone (e. g. , resulting into erroneous or deadlocking executions), therefore, we also provide analysis techniques for automatically detecting errors in the composition.
Last but not least, our approach supports not only the design of the application but also its deployment. The whole approach was implemented using three different components gathered under the name of mozart (portmanteau of Mozilla and Advanced Rule Triggers). First, we extended the Mozilla Web of Things (WoT)  [4]  platform by developing an interface to support advanced compositions. This interface also enables users to perform the successive steps, i. e. , design, analysis, and deployment. Second, the analysis part is achieved by encoding the objects and composition expression into a formal specification language and by invoking the verification tools available in the CADP toolbox  [5]. Third, application deployment is managed by an execution engine built on top of the Mozilla WebThings platform.
TOOL SUPPORT WebThings by Mozilla 1  is a WoT platform for monitoring and controlling objects. It provides Things UI (backed by Things Gateway) to monitor and control IoT objects via a unified web interface. The UI allows users to discover objects available on the network, build rules and deploy them. We extended this platform to support design and deployment of advanced applications specified using the composition language described in Section 2. We chose to extend an existing platform as it provides proven benefits over starting from scratch. First, it saves the effort of building another UI for rule-based automation. Importantly, the list of devices supported by WebThings platform is constantly growing and if we had implemented our own discovery and deployment, additional effort would be spent on adding APIs to support new devices in our platform.
Finally, WebThings implements the abstract W3C WoT specification. As part of the extension, we developed three major components. First, we designed a new user interface (UI) to compose advanced rules. Users create rules using the existing WebThings UI. Once the rules are created, they can use the new interface to compose rules using the composition language. A screenshot from the newly built UI to design advanced applications is shown in Figure  1. Second, we built a verification component, which transforms WoT models and composition to formal specification to perform property verification such as the absence of deadlocking situations, completeness of execution, and probability of successful execution. Once the composition is built, the interface allows users to verify if the properties hold true.
Upon clicking the verify button, users are provided a choice to verify generic properties or to write functional and quantitative properties. As soon as the user input is registered, the tool transforms the composition to process algebraic specification and generates the required model checking scripts as an input to CADP. The results of verification are obtained from CADP and returned to the web interface. If the properties are not satisfied, a diagnostic message is displayed to the users to help them redesign the application so that all properties of interest are satisfied. Third, we built an execution engine to activate these rules respecting the composition language semantics. When a composed model is validated, users can proceed to deploy the application. As soon as the deploy button is clicked, the newly developed execution engine kicks in.
It creates appropriate listeners to capture events and executes relevant actions respecting the composition semantics. In addition to these features, users familiar with Business Process Model Notation (BPMN) can visualise the BPMN model of the application as process oriented notation as shown in Figure  1 , which can be more intuitive for advanced scenarios  [2]. Technology Stack: A simplified view of the components and technologies used in the tool is shown in Figure  2. Mozilla WebThings Gateway is built on NodeJS. Our extension takes advantage of the existing packages available in the platform to implement the execution engine. Rules and composite rules are stored in a file-based SQLite database. BPMN visualisation is handled using bpmn. io JavaScript library. The backend transformation and verification component is implemented as a Spring Boot application hosted on an embedded Tomcat server.
Then, users were shown 8 advanced scenario descriptions in natural language. Further, users were shown how each of these scenarios can be designed using MOZART. After walking through these scenarios, users were  asked how comfortable they would be to use the tool to build advanced applications. As shown in Figure  4 , nearly 60% of the users felt that they could use the tool to build advanced applications with the provided training. As a second experiment, 6 users with varying programming skills (none to expert) from the 26 users were given 2 scenarios described in natural language and were asked to design the application themselves using the tool. The IoT devices required for the scenario were already connected to the WebThings platform. Users had to create individual rules and compose them to build the application.