dataesr/scientific-paragraphs-categorization huggingface dataset:
Threats to validity External validity. A threat to generalization of our results is that all code used in the case studies has been re-modeled by the authors themselves and consists of only 840 LoC. It should be noted that in two of the three case studies, the non-variable context was not considered (for example, in FormbaR less than 10% of the code was re-modeled). On the other hand, any reasonable product line is bound to have a substantial degree of commonality and there is no point to look at the invariant aspects of an application for the present evaluation. Another threat is that, while VMs are a general concept for product lines, we only investigated its use for DOP, and only for the ABS language. We consider this to be unproblematic, because (i) DOP is a fairly general SPL approach that encompasses FOP  (Schaefer and Damiani, 2010)  and (ii) the ABS implementation of DOP follows general DOP principles  (Schaefer and Damiani, 2010)  and closely resembles other implementations, such as DOP in Java  (Koscielny et al.
, 2014). Internal validity. A threat concerning our conclusions is that, due to our aim of reusing code from the existing flattening and type checker implementation, we rely on pre-existing code and design choices for both flattening and applicability: we reuse the Choco solver part of the ABS compiler and do not optimize the flattening mechanisms. Another threat is that the applicability checks take constant time due to the design of our examples. We mitigated this by using random feature models to exclude artifacts stemming from the structure of the feature model.
In contrast, the VM concept does not require feature model composition. None of the proposals mentioned above support variant interoperability  (Damiani et al. , 2018b). Setyautami et al. (2018)  address variant interoperability at the level of static UML class diagrams. In this paper we consider executable Java-like code. Variant interoperability in terms of ABS code is addressed in our previous work  (Damiani et al. , 2018b) , where we consider a set of product lines, each comprising a set of modules. However, in that proposal, encapsulation is not realized by mechanisms at the module level (as in VMs): unique declarations are supported (unsatisfactorily) by common modules (which is not fine-grained enough), and the concepts of modularity (through modules) and variability (through product lines) are intertwined.
Hence, VAMS is not concerned with explicit dependencies between modules, which are crucial to usability and central to the PEV introduced in this work. By exploiting PEV, the VM concept achieves simplicity: configuring a single VM M triggers automatic generation of all required variants of M and other VMs. (ii) The design of VAMS does not target variant interoperability  (Kästner et al. (2012b)  do not mention this issue). Making two variants of the same module co-exist, requires to create a copy of the module and to rename (possibly by using the module composition language provided in VAMS) all its features and all its exported functions. In contrast, providing usable support to variant interoperability is a central design goal of VM. (iii) Variability in VAMS is achieved explicitly by using an annotative approach: code elements (import/export declarations and function declarations) are annotated with presence conditions (propositional formulas over features).
In VM variability is achieved explicitly by DOP for class/interface declarations and implicitly for export/import declarations. (iv) VAMS is formalized by building on a calculus in the spirit of Cardelli's module system formalization  (Cardelli, 1997)  for procedural programming languages, where a module consists of a set of imported typed function declarations and a list of typed function definitions, and is implemented as a module system for C code. Therefore, VAMS is tailored to procedural languages, where the interface of each module describes names and types of imported and exported functions, and there is a global function name space. Moreover, even though each module has its own feature model, there is a global feature name space. In contrast, the VM concept targets Java-like languages, it is based on the module system of ABS  (Hähnle, 2013; Johnsen et al.
We evaluated the VM concept and our implementation of it quantitatively and qualitatively by case studies that were partly taken from industrial code used in production. We are currently formalizing and implementing an extended analysis called dependence consistency checking: it aims to fully support family-based checking of type-safety (see the discussion at the beginning of Section 9). The extended tool chain will be subjected to validation by novel and even larger case studies. • Rules for Interfaces modify interfaces. Rule D:I:Empty is applicable when no further modification is requested on the given interface, so that the result is the interface itself. Rule D:I:Adds adds the specified method header to the interface (provided no header with this name is already present). Rule D:I:Rems removes an existing method header from the interface.
Neurosurgical implantation of the BSI In the context of the Stimulation Movement Overground (STIMO)-BSI clinical trial (clinicaltrials. gov, NCT04632290), we enroled a 38-year-old male who had sustained an incomplete cervical (C5/C6) spinal cord injury during a biking accident ten years before. He had previously participated in the STIMO clinical trial (clinicaltrials. gov, NCT02936453), which involved a five-month neurorehabilitation programme supported by targeted epidural electrical stimulation of the spinal cord  4, 5. This programme enabled him to regain the ability to step with the help of a front-wheel walker. Despite continued use of the stimulation at home, for approximately three years, he had reached a neurological recovery plateau, which motivated him to enrol in STIMO-BSI. To guide the implantation of the BSI, we developed pre-operative planning procedures that enabled us to optimize the positioning of the recording and stimulation implants over the brain and spinal cord.
Article to move, and to configure stimulation programs that modulate specific ensembles of lower limb muscles. The first procedure consisted of extracting the spatial, spectral and temporal features of ECoG signals that were linked to the intention to mobilize each joint of both lower limbs. For this purpose, the participant was asked to attempt hip, knee and ankle movements of the left and right sides in a seated position, during which ECoG signals were recorded concurrently. This mapping enabled the identification of the electrodes, spectral features and temporal windows that captured the larger amount of movement-related information 4,21-24 (Fig. 2a  and Extended Data Fig. 2 ). The electrodes that measured neural signals correlating with leg movements were located on the most medial aspect of the implant, rostral to the central sulcus, as expected on the basis of pre-operative magnetoencephalographic recordings.
Study design and participant All experiments were carried out as part of the ongoing clinical feasibility study STIMO-BSI ('Brain-controlled Spinal Cord Stimulation in Patients With Spinal Cord Injury'), which investigates the safety and preliminary efficacy of brain-controlled spinal cord stimulation after spinal cord injury (clinicaltrials. gov, NCT04632290). All surgical and experimental procedures were performed at the Lausanne University Hospital (CHUV) except for the magnetoencephalography experiments, which were performed at the CEA Clinatec facilities (Grenoble). The study involved functional assessments before implantation of the cortical devices, the neurosurgical procedure, a 6 week period during which various decoders were calibrated and spinal cord stimulation libraries were established, and a 15 week period of neurorehabilitation with physiotherapists.
which amounted to a total of 40 sessions lasting one to three hours. The neurorehabilitation programme was personalized on the basis of the participant's improvements. At the end of the neurorehabilitation period, the participant exited the active participation phase of the clinical trial, and was offered the opportunity to continue using the BSI at home. The participant is currently being followed up on a regular basis by the study team until the end of the three-year study extension phase of home use of the system. Before enrolment in the STIMO-BSI study, the participant had completed the clinical protocol STIMO ('STIMO: Epidural Electrical Simulation (EES) With Robot-assisted Rehabilitation in Patients With Spinal Cord Injury', NCT02936453) during which a spinal cord stimulation system had been implanted and he had completed a five-month intensive neurorehabilitation programme supported by EES, followed by a two-year period of independent use at home.
The recording sampling rate was 1,000 Hz. Continuous head position indicator signals were recorded during the experiments to track the head movements of the subject. Before experimentation, a three-dimensional (3D) digitization system (Isotrak II, Polhemus) was used to localize anatomical fiducial points for later coregistration with head computerized tomography (CT). Temporal signal space separation (tSSS) was applied to reduce the noise in the MEG data using MaxFilter v. 3. 0 software (Elekta). First, manual review of raw data enabled the marking of bad channels. Second, the tSSS filter was applied using head movement compensation and automatic bad channel correction. The main parameters were kept to default (tSSS correlation threshold of 0. 98, orders of expansion for 'in' and 'out' components of signal set to 8 and 3, respectively, and a 10 s time buffer).
Notch filtering at 50 Hz and harmonics (100 Hz, 150 Hz, 200 Hz and 250 Hz) were also applied to remove power line contamination. Stereotypical artefacts (cardiac, ocular) were identified by independent components analysis using MNE-Python software  45  and rejected on visual screening (Infomax method, calculated separately for magnetometers and gradiometers using 64 components). The head, skull and cortex geometries were calculated from CT scan using the MRI segmentation routine included in the Brainstorm software  46  , followed by calculation of the head model using the overlapping spheres method. A 3D inversion kernel was calculated using Brainstorm implementation of the Minimum Norm Imaging method with default parameters. It enabled the reconstruction of cleaned raw data at brain source level for the subsequent calculations.
Decoder architecture ECoG data were collected from 32 channels per implant at an acquisition frequency of 586 Hz. The signals were band-pass filtered between 1 Hz and 300 Hz. The data were streamed through the fieldtrip toolbox to a custom-made decoding software running in the Matlab Runtime Environment (Mathworks). To decode the intention to perform lower limb movements, we implemented a variant of the recursive exponentially weighted Markov-switching multilinear model (REW-MSLM) algorithm that we had previously developed to decode upper limb movements  26. REW-MSLM is a mixture of multilinear algorithms form experts. It consists of a hidden Markov model (HMM) classifier, called 'gate', and a set of independent regression models, called 'experts'. Each expert is generally dedicated to the control of a group of degrees of freedom, a specific limb or movement (for example, joint movement).
Input features X(t) were computed from the ECoG signals and then fed to the decoder. Epochs ranging from 200 to 500 ms of ECoG signals from the 64 electrodes were created to generate a 100 ms sliding window. The epochs were mapped to the temporal frequency space with complex continuous wavelet transform (CCWT). The wavelets declined from the Morlet mother wavelet are centred around specific frequencies (2, 5:5:100, 125, 150, 200 Hz). The absolute value of the output of the CCWT was then decimated to obtain 2-5 points along the temporal modality, which defined the epoch. The prediction was computed every 100 ms. During the experiments, the REW-MSLM algorithm recursively updated the experts and gate coefficients every 15 s. The training data consist of 15 s batches of input ECoG features associated to output movement features.
ECoG mapping procedure To assess the spatial and spectral information in the ECoG signals to discriminate a specific task, we computed the linear regression weights associated with cued motor attempts. To map the features related to lower limb movements (hip, knee, ankle bilaterally) we recorded cortical signals during 57 (±6 s. e. m. ) repetitions of each movement attempt cumulating 226 s (±25 s s. e. m. ) during each state. The weights generated from this dataset were projected onto the spatial dimension for different frequency bands (0. 5-10 Hz, 10-40 Hz, 40-100 Hz and 100-200 Hz) or on the spectral dimension.
Decoder calibration procedure During the calibration of the decoders, the participant received visual cues through a custom-made interface displaying the targeted state with or without the direction of movement. The cues were generated as a pseudo-random sequence with programmable duration (2/4 s per state) or manually by the experimenter. The decoding environment enabled visualization of the duration spent in each state as well as the number of transitions between states. Once the performance of the decoder was judged sufficient by the participant and experimenter, cueing was discontinued and the participant could use the model without further calibration. From day to day, models were updated when deemed necessary. The iterative nature of the implementation facilitated these updates. Typically, the model supporting control over left hip and right hip flexions during walking was trained on the basis of 30 repetitions of each active state, whereas the resting state was predicted from 3 min of ECoG data that were acquired while the participant was performing unspecific hand and trunk movements, as well as talking to ensure robustness of the predictions.
Quantitative gait analysis EMG activity during walking was acquired bilaterally at 1,259 Hz using 16-channel wireless sensors (Delsys Trigno) placed over the iliopsoas, rectus femoris, vastus lateralis), semitendinosus, tibialis anterior and medial gastrocnemius. Kinematic recordings were acquired using a 3D motion capture system (Vicon Motion Systems). A network of 14 infrared cameras, which covered a 12 × 4 × 2. 5 m 3 workspace, was used to record the motion of markers attached to body landmarks. Data were acquired at a 100 Hz sampling rate using. A PCA was applied over a total of 26 kinematic and EMG parameters that were calculated for each gait cycle, as described previously  4. The following parameters were included: step length, step height, knee height, knee angle and knee maximum angle, hip angle and hip maximum angle, limb angle, vastus lateralis activation, vastus lateralis stance activation, vastus lateralis swing activation, tibialis anterior activation, tibialis anterior stance activation, tibialis anterior swing activation, rectus femoris activation, rectus femoris stance activation, rectus femoris swing activation, iliopsoas activation, iliopsoas stance activation, iliopsoas swing activation, semitendinosus activation, semitendinosus stance activation, semitendinosus swing activation, medial gastrocnemius activation, medial gastrocnemius stance activation, medial gastrocnemius swing activation.
Statistical analysis Individual data points are represented on each figure. Measurements were taken from distinct samples, except for the observational gait analysis for which the expert physiotherapists were independently ranking the same videos. We used paired (when applicable) or unpaired one-tailed t-test, except as otherwise specified, with α = 0. 05. P values are reported with ***P < 0. 001, **P < 0. 01 and *P < 0. 05.
His neurological status was evaluated according to the International Standards for Neurological Classification of Spinal Cord Injury. Key inclusion criteria: o Having completed the main phase of the STIMO study (NCT02936453) o Age 18-65 (women or men) o SCI graded as American Spinal Injury Association Impairment Scale (AIS) A, B, C & D o Level of lesion: T10 and above, based on AIS level determination by the PI, with preservation of conus function o The intact distance between the cone and the lesion must be at least 60 mm. oFocal spinal cord disorder caused by either trauma or epidural, subdural or intramedullary bleeding o Minimum 12 months post-injury o Completed in-patient rehabilitation program o Stable medical, physical and psychological condition as considered by Investigators o Able to understand and interact with the study team in French or English o Adequate care-giver support and access to appropriate medical care in patient's home community o Agree to comply in good faith with all conditions of the study and to attend all required study training and visit o Must provide and sign the STIMO-BSI Informed Consent prior to any study related procedures Key exclusion criteria remain the same as for the STIMO study: o Women who are pregnant (pregnancy test obligatory for woman of childbearing potential) or breast feeding.
Acknowledgements We thank our test pilot for his commitment and trust. Supported by  Defitech Foundation ,  Rolex Award for Enterprise, International Foundation for Research in Paraplegia ,  Translational Medical Research Award 2021  from the  Leenaards Foundation ,  Pictet Group Charitable Foundation, ONWARD medical, Medtronic , the  Swiss National Science Foundation  through the  National Centre of Competence in Research in Robotics  ( 51NF40-185543 ),  Sinergia  ( CRSII5-183519 ), the  Lead Agency Program  with the  French National Research Agency  ( Think2Move SNF-32003BE-205563 ,  ANR-21-CE19-0038 ),  A F Harvey Prize award, Swiss Innovation Agency InnoSuisse (CTI-41871. 1  IP-LS Bridge) ,  Eurostars  ( E!12743 Confirm  and  E!113969 Prep2Go ), the  European Commission  ( ERC-2019-PoC Braingait 875660 ,  EIC 2021-TransitionChallenges-01-01 ReverseParalysis 101057450 ,  Horizon-EIC-2021-Pathfinderchallenges-01-02 NEMO-BMI 101070891 ),  Fonds de dotation Clinatec  (WIMAGINE implant development) and  Institut Carnot Leti.
Data analysis All softwares and software versions used to analyze data are described in the Method section at the relevant paragraph. Following is a list of softwares used: Matlab v2018a and later, Sim4Life by ZMT. , Brainstorm software. For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers. We strongly encourage code deposition in a community repository (e. g. GitHub). See the Nature Portfolio guidelines for submitting code & software for further information.
Acknowledgments We would like to thank  Thomas H. W. Goebel  and an anonymous reviewer for many valuable comments and suggestions that helped to improve the original manuscript. This work is financially supported by the  Helmholtz Association's Initiative and Networking Fund  for the  Helmholtz Young Investigator Group ARES  (contract  VH-NG-1516 ). This material is based on a part of the PhD work of the first author, and thus the first author acknowledges the PhD Research Scholarship from  Nanyang Technological University (NTU), Singapore. Open Access funding enabled and organized by  Projekt DEAL.
The collection of in situ data is generally a costly process, with the Arctic being no exception. Indeed, there has been a perception that the Arctic is lacking in situ sampling; however, after many years of concerted effort and international collaboration, the Arctic is now rather well sampled, with many cruise expeditions every year. For example, the GLODAP (Global Ocean Data Analysis Project) product has a greater density of in situ sampling points within the Arctic than along the Equator. While this is useful for open-ocean processes, the fjords of the Arctic, which serve as crucially important intersections of terrestrial, coastal, and marine processes, are sampled in a much more ad hoc process. This is not to say they are not well sampled but rather that the data are more difficult to source and combine for further analysis.
It was therefore noted that the fjords of the Arctic are lacking in FAIR (findable, accessible, interoperable, and reusable) data. To address this issue, a single dataset has been created from publicly available, predominantly in situ data from seven study sites in Svalbard and Greenland. After finding and accessing the data from a number of online platforms, they were amalgamated into a single project-wide standard, ensuring their interoperability. The dataset was then uploaded to PANGAEA so that it can be findable and reusable in the future. The focus of the data collection was driven by the key drivers of change in Arctic fjords identified in a companion review paper. To demonstrate the usability of this dataset, an analysis of the relationship between the different drivers was performed.
, 2022). The work outlined below focuses on the European Arctic, the definition of which is taken from Copernicus (https: Published by Copernicus Publications. //climate. copernicus. eu/european-arctic, last access: 17 August 2023) and closely matches the AMAP (Arctic Monitoring and Assessment Programme) definition of the Arctic Circle (https://www. amap. no/about/geographical-coverage, last access: 17 August 2023). The rate of loss for the Arctic cryosphere driven by the changing climate is alarmingly rapid  (Schlegel et al. , 2023) , making it critical that Arctic fjord systems be as actively monitored as possible. Even though this monitoring in the polar north is both challenging and costly, there has been a concerted international effort to maintain and increase it. The majority of this in situ data collection, both at the surface and at depth, is conducted via large research ships or autonomous platforms, and the sampling of data throughout all but the mouths of fjords tends to be limited due to their depth.
The sampling of data within fjords is therefore carried out in a more ad hoc manner, with many smaller teams and experiments creating disparate datasets that suffer from issues with regard to FAIR (findable, accessible, interoperable, and reusable) data, and some fjords are sampled much more heavily than others  (Bischof et al. , 2019). This is a known issue, and there has already been work done to create unified datasets for specific aspects of Arctic fjords (e. g. physical oceanography via the UNIS CTD database;  Skogseth et al. , 2019 ; https://data. npolar. no/ dataset/39d9f0f9-af12-420c-a879-10990df2e22d, last access: 17 August 2023). However, there is not yet a unified dataset that provides data for investigating the range of possible relationships throughout the entire socio-ecological fjord system.
The dataset detailed in this report aims to address this shortcoming. The combination of the many different socio-ecological datasets is not as simple as identifying the sources of in situ data and putting them together into a folder. With the exception of a network of meteorological stations operated by the Norwegian Meteorological Institute (https://frost. met. no/ index. html, last access: 17 August 2023) and a few well established instrument installations (e. g. Fischer et al. , 2020) , there are precious few long-running time series collected within or adjacent to the fjords of the EU Arctic. Because maintaining these stations in the Arctic is so expensive and because the hostility of the terrain dramatically limits their potential size or scope, there is always much more demand for research support than can be given.