dataesr/scientific-paragraphs-categorization huggingface dataset:
| CON CLUS IONS Our goal was to demonstrate the use of DNA-based detection methods that can be used in educational settings to explore and raise awareness of urban biodiversity. Thus, we propose our study as an example of an entirely new form of a BioBlitz, involving a noninvasive eDNA sampling, next-generation sequencing, and rapid data analyses using a freely accessible, user-friendly online platform. We also suggest improvements for how to implement the field and laboratory protocols, although the latter can be done in collaboration with a local university or company. Moreover, while these studies can be carried out in the absence of direct interaction with taxonomic experts thanks to the digitization of current taxonomic knowledge in the Barcode of Life Database, it is still highly encouraged to collaborate with them.
The deep sea is the largest biome on Earth but the least explored. Our knowledge of it comes from scattered sources spanning different spatial and temporal scales. Implementation of marine policies like the European Union's Marine Strategy Framework Directive (MSFD) and support for Blue Growth in the deep sea are therefore hindered by lack of data. Integrated assessments of environmental status require tools to work with different and disaggregated datasets (e. g. density of deep-sea habitat-forming species, body-size distribution of commercial fishes, intensity of bottom trawling) across spatial and temporal scales. A feasibility study was conducted as part of the four-year ATLAS project to assess the effectiveness of the open-access Nested Environmental status Assessment Tool (NEAT) to assess deep-sea environmental status.
Selection of indicators NEAT includes a database of > 500 indicators for MSFD descriptors D1-D8, D11  (Teixeira et al. , 2016). From this database, indicators deemed to be the most appropriate for assessing deep-sea environmental status were selected (e. g. biomass of commercial fish, areal extent of human affected area). This selection took into account the special features of the deep sea including habitat remoteness (and associated difficulties in sampling) and the high sensitivity of deep-sea organisms to human activities. In addition, new indicators were suggested by 15 individual deep-sea experts from the ATLAS consortium and added to those extracted from the NEAT database. This was done to supplement the assessment using deep-sea specific indicators (the NEAT database contains several indicators that are not applicable to the deep.
Assessing the assessment design, quantity and quality of data Data availability for this analysis was much lower than previous analyses using the NEAT in shallow European seas, as expressed through comparisons of SAUs, habitats, ecosystem components, unique indicators, and indicator values. This is mainly due to the remoteness of deep-sea environments. It should also be taken into account that there is a smaller number of species in the deep sea than on continental shelves due to the lower temperature, productivity, and habitat heterogeneity in the deep sea  (Costello and Chaudhary, 2017). In the analyses performed here, there were two hierarchical levels in most cases whereas in some coastal areas studied by  Uusitalo et al. (2016)  there were often three to four hierarchical levels of SAUs, up to nine habitats and up to nine ecosystem components.
For example, for the indicator "Areal extent of litter" measurements units were "Items/km 2 ", "Number of items/minute of video tow" and "Litter/ image". This highlights the need to increase standardization in quantification and reporting of marine litter (OSPAR, 2017). A similar situation was also recorded for the units used to express density of specimens (Table  3 ). ATLAS experts also identified cases of limited data quality for some indicators. Specifically, in LoVe Ocean Observatory and Bay of Biscay the low quality of the data was due to technical issues (e. g. absence of laser points from the Remotely Operated Vehicles (ROV) cameras, hampering image scalation) that did not allow the quantification of ecosystem components. In the case of Mingulay Reef Complex, data on fish assemblages were collected with a ROV which, in general, is not the most appropriate method to measure fish species richness, abundance and biomass due to avoidance behaviour of objects like large ROVs  (Andaloro et al.
5B ). In the Faroe-Shetland Channel the good quality of the data was mainly due to rigorouslydesigned towed camera surveys collecting high-quality video material from areas inside and outside the Faroe-Shetland Channel Marine Protected Area, that allowed quantitative analyses using collected image material  (Kazanidis et al. , 2019). Quantitative data available online for demersal fisheries inside and outside the FSC MPA (https:// marinescotland. atkinsgeospatial. com/nmpi/) contributed also to this high data quality. In the Condor seamount (Azores) the collection of reliable measurements on fish body size and biomass of the commercially-important fishes Helicolenus dactylopterus and Pagellus bogaraveo using appropriate sampling methods (long-line surveys) over the period 1983-2017 (with gaps) allowed the collection of high-quality data for the analyses.
Vessel monitoring system (VMS) and electronic logbook data for the Reykjanes Ridge were available at very high spatial resolution (gridded as fine as 150x150 m) that allowed the collection of high-quality information. In Porcupine Seabight fishing pressure data were represented by swept area ratio collated in  ICES (2016a ICES ( , 2018) )  and calculated by analysis of VMS data.
5). In previous assessments the indicators "Physical Damage" and "Cumulative impacts on seabed habitats" were used in OSPAR (2017) and  Korpinen et al. (2013)  to assess the impact of anthropogenic activities in northeast Atlantic and Baltic Sea deep-sea habitats, respectively. In principle, these indicators used habitat maps, sensitivity/resilience to disturbance and distribution/intensity of human activities highlighting the urgent need to advance our understanding about the distribution of habitats, and especially those that are particularly vulnerable to human pressures, in the deep sea. Case-study leaders have identified some new indicators (i. e. other than those already existing in NEAT database; highlighted in grey in Table  3 ) that could contribute in the assessment of the deep-sea environmental status.
The combination of very low measured values for indicators in a large SAU had a major impact in the overall result of the assessment. The result of the assessment for the ecosystem components "benthic invertebrates" and "benthos" -for which data quality was good-was that both components were in good status, which was in complete agreement with expert judgement and the information from the area in the public domain  (Roberts et al. , 2005; Henry et al. , 2013; Kazanidis et al. , 2016; De Clippele et al. , 2017). The results for the Mingulay Reef Complex highlight that low-quality data should be treated with much caution  (Uusitalo et al. , 2016). The Rockall Bank assessment also revealed a moderate agreement between NEAT and expert judgement. Specifically, the NEAT results showed a poor environmental status (Tables  4 5 ; see also.
, 2010; Amoroso et al. , 2018). Intersectoral collaborations (e. g. industryacademia; in present study data from the LoVe Ocean Observatory were supplied by Equinor, an energy company) and (meta)data archiving in online repositories (e. g. ICES VME data base, EMODnet, PANGAEA) will improve knowledge about the impacts of human activities and ecosystem functioning, in general  (Stratmann et al. , 2019; Vad et al. , 2020). The assessment of the distribution of marine litter on the seafloor will be facilitated through the data acquisition taking advantage of the already existing regular scientific trawling surveys and development of specific common protocols (see  OSPAR, 2017; Baudrier et al. , 2018). Advances in recording the spatial and long-term distribution of ecosystem components are also fundamental to improve the accuracy of the deep-sea environmental status assessments.
, 2019a; Ramiro-Sánchez et al. , 2019). Research efforts need to address key points like the response of habitat-forming species to multiple stressors  (Lunden et al. , 2014; Hennige et al. , 2015; Büscher et al. , 2017)  and the behavior of deep-sea species' larvae  (Larsson et al. , 2014; Strömberg and Larsson, 2017). The latter is especially important in order to improve our understanding about connectivity among cold-water ecosystems and source-sink dynamics  (Fox et al. , 2016; Kenchington et al. , 2019b)  which can help to set up priorities for environmental status assessments and conservation strategies. The quality of data collection will benefit from the standardization of methodologies used e. g. through the establishment of standardized reference image databases  (Howell et al. , 2019)  and protocols on data collection and reporting [e.
Acknowledgments We would like to thank  Nikolai Roterman ,  Steve Ross ,  Pablo Duran ,  Marina Gallardo-Núñez ,  Ana García-Alegre ,  Francis Neat ,  Mar Sacau ,  Gerald Taranto  and  Javier Urra  for their support in supplying data to perform the selection of indicators as well as data to perform the NEAT analyses. We would like to thank  Maria Uyarra -FP7 Devotes  ( GA 308392 ) who provided an initial training workshop on NEAT and the  H2020 Columbus project  ( GA 652690 ) for providing her with travel support. We would also like to thank the crew and officers of the vessels used in data collection. We would like also to thank  Joana Gafeira  ( BGS ) for her help in the creation of the maps. JLR would like to acknowledge the projects ISUNEPCA,  LIFE + INDEMARES  and  LIFE IP PAF INTEMARES  which provided data for carrying out the  NEAT analyses for the Gulf of Cádiz.
Funding This study was funded by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 678760  ( ATLAS ) to JMR. This output reflects only the authors' view and the  European Union  cannot be held responsible for any use that may be made of the information contained therein. TS was partly funded by the  Dutch Research Council (NWO)  within the  research programme Rubicon  with project number  019. 182EN. 012. Funding for EK contributions was through  Fisheries and Oceans Canada ,  International Governance Strategy Science Program. HE was funded by the  Icelandic Research Fund, RANNÍS , under grant agreement no. 174552. TM received additional support from the  Program Investigador FCT  ( IF/ 01194/2013 ), IFCT  Exploratory  Project ( IF/01194/2013/CP1199/ CT0002 ) from the  Fundação para a Ciência e Tecnologia (POPH and QREN) , and from the  PO2020 Azores MapGES project  ( Acores-01-0145-FEDER-000056 ).
Data description The data made available through this article consists in a set of 5616 three-dimensional brain images in compressed NIFTI format, i. e 144 images for each of the 39 participants. For a given participant, each of the 144 images contains estimates of the blood-oxygen-level-dependent (BOLD) response induced by hearing an audio stimulus, either vocal or non-vocal, as measured using fMRI. These brain maps were computed using the processing pipeline described below. The naming of the files is straightforward, with one directory for each subject (called 'sub-XX', where XX is a two-digit number), and image names following the convention 'beta_0YYY. nii. gz', where YYY is a three-digit number between 1 and 144, padded with zeros when necessary. In order to allow reproducing the brain decoding results presented in Ref.
Stimuli and paradigm As part of this experiment, the participants were presented with an event-related voice localizer protocol in the MRI scanner. They were asked to close their eyes while passively listening to 144 sounds each lasting approximately 500 ms, 72 vocal sounds and 72 non-vocal sounds. Most of the stimuli (95%) belonged to a database created for a previous study  [3] , while others were downloaded from public databases free of copyright. A sequence of randomized inter-stimulus intervals (ISI) of a duration that varied between 4 and 5 seconds separated each stimulus.
Data acquisition The images were acquired on a 3T Prisma MRI scanner (Siemens, Eerlangen, Germany) with a 64channel head coil. A multi-band gradient echo-planar imaging (EPI) sequence with a factor of 5 was used to cover the whole brain and cerebellum with 60 slices during the TR (repetition time) of 955 ms, with an isotropic resolution of 2 mm, a TE (echo time) of 35. 2 ms, a flip angle of 56 and a field of view of 200 Â 200 mm for each slice. A total of 792 vol were acquired in a single run of 12 min and 36 s. In addition, a high resolution three-dimensional T1 image was acquired for each subject (isotropic voxel resolution of 0. 8 mm, TR ¼ 2400 ms, TE ¼ 2. 28 ms, field of view of 256 Â 256 mm), as well as a phase-reversed pair of spin echo images with the same geometry as the fMRI data (field of view ¼ 240 Â 240 mm, slice thickness ¼ 2.
Acknowledgments This work was carried out within the  Institut Convergence ILCB  ( ANR-16-CONV-0002 ). The acquisition of the data, performed at the  Centre IRM-INT in Marseille, France, was made possible thanks to the infrastructure France Life Imaging  ( 11-INBS-0006 ) of the  French program Investissements d'Avenir , as well as a grant from the  Agence Nationale de la Recherche  ( ANR-15-CE23-0026 ).
band gradient echo-planar imaging (EPI) sequence with a factor of 5 was used to cover the whole brain and cerebellum with 60 slices during the TR (repetition time) of 955 ms, with an isotropic resolution of 2 mm, a TE (echo time) of 35. 2 ms, a flip angle of 56 and a field of view of 200 Â 200 mm for each slice. Description of data collectionData were recorded in 40 subjects. Each participant was asked to close their eyes while passively listening to 144 sounds each lasting approximately 500 ms, 72 vocal sounds and 72 non-vocal sounds. The fMRI data were processed to estimate the brain activation map induced by hearing each of these sounds, yielding 5616 brain images (144 stimuli x 39 subjects, after the exclusion of one subject) Specifications Table Subject Cognitive Neuroscience Specific subject area Functional neuroimaging, multivariate pattern analysis, brain decoding, voice perception Type of data Image How data were Magnetic Resonance Imaging (3T Prisma scanner; Siemens, Eerlangen, Germany) acquired Data format Raw NIFTI (Neuroimaging Informatics Technology Initiative) format Parameters for data collection A multi-Data source location Institution: Aix-Marseille Universit e City/Town/Region: Marseille Country: France Data accessibility Repository name: Zenodo Data identification number: 10.
5281/zenodo. 2591038 Direct URL to data: https://doi. org/10. 5281/zenodo. 2591038 Related research Wang Q. article , Cagna B. , Chaminade T. , et Takerkart S. (2020). Inter-subject pattern analysis: A straightforward and powerful scheme for group-level MVPA. NeuroImage, 204: 116205. https://doi. org/10. 1016/j. neuroimage. 2019. 116205.
With an increase in awareness regarding a troubling lack of reproducibility in analytical software tools, the degree of validity in scientific derivatives and their downstream results has become unclear. The nature of reproducibility issues may vary across domains, tools, datasets, and computational infrastructures, but numerical instabilities are thought to be a core contributor. In neuroimaging, unexpected deviations have been observed when varying operating systems, software implementations, or adding negligible quantities of noise. In the field of numerical analysis these issues have recently been explored through Monte Carlo Arithmetic, a method involving the instrumentation of floating point operations with probabilistic noise injections at a target precision. Exploring multiple simulations in this context allows the characterization of the result space for a given tool or operation.
In this paper we compare various perturbation models to introduce instabilities within a typical neuroimaging pipeline, including i) targeted noise, ii) Monte Carlo Arithmetic, and iii) operating system variation, to identify the significance and quality of their impact on the resulting derivatives. We demonstrate that even low-order models in neuroimaging such as the structural connectome estimation pipeline evaluated here are sensitive to numerical instabilities, suggesting that stability is a relevant axis upon which tools are compared, alongside more traditional criteria such as biological feasibility, computational efficiency, or, when possible, accuracy. Heterogeneity was observed across participants which clearly illustrates a strong interaction between the tool and dataset being processed, requiring that the stability of a given tool be evaluated with respect to a given cohort.
As neuroscience has evolved into an increasingly computational field, it has suffered from the same questions of numerical reproducibility as many other domains  [2]. In particular, neuroimaging often attempts to fit alignments, segmentations, or models of the brain using few samples with variable signal to noise properties. The nature of these operations leaves them potentially vulnerable to instability when presented with minor perturbations in either the data themselves or their processing implementations. High Performance Computing (HPC), commonly used in neuroimaging, is one such perturbation. As datasets grow in size, the adoption of HPC environments becomes a necessity. Given that these environments are highly heterogeneous in terms of hardware, operating systems, and parallelization schemes, this heterogeneity has been shown to compound with tool-specific instabilities and impact results  [3].
While the independent evaluation of atomic pipeline components may be feasible in some cases, as was done by Skare et al. in  [4]. Here, the authors computed the theoretical conditioning of various tensor models used in diffusion modeling, and compared these values to the observed variances in tensor features when fit on simulated data. While approaches like the above provide valuable insights to algorithms and their implementations independently, the impact of these stepwise instabilities within composite pipelines remains unknown. Even if one were able to evaluate each step within a pipeline, identifying the impact these instabilities may have on a result when composed together, both structurally and analytically, remains practically difficult to evaluate. Various forms of instability have been observed in structural and functional magnetic resonance (MR) imaging, including across operating system versions  [3] , minor noise injections  [5] , as well as dataset or implementation of theoretically equivalent algorithms  [6] ,  [7].
II. METHODS All processing described below was run using servers provided by Compute Canada. Software pipelines were encapsulated and run using Singularity  [12]  version 2. 6. 1. Tasks were submitted, monitored, and provenance captured using Clowdr  [13]  version 0. 1. 2-1. All code for performing the experiments and creating associated figures are available on GitHub at https://github. com/gkiar/stability and https://github. com/gkiar/stability-mca, respectively.
A. Dataset and pre-processing The dataset used for processing is a 10-session subset of the Nathan Kline Institute Rockland Sample dataset (NKI-RS)  [14]. This dataset contains high fidelity structural, functional, and diffusion MR data and is openly available for research consumption. The 10 sessions used were chosen by randomly selecting 10 participants and selecting their alphabetically-first session of data. This data was preprocessed prior to the modelling evaluated here using a standard denoising and image alignment pipeline  [15]  built upon the FSL toolbox  [16]. The steps in this pipeline include eddy current correction, brain extraction, tissue segmentation, and image registration. The boundary between white and gray matter was obtained by computing the difference between a dilated version of the white matter mask and the original.
B. Modeling After pre-processing the raw diffusion data using FSL, structural connectomes were generated for an 83-region cortical and sub-cortical parcellation  [17]  using Dipy  [11]. A sixcomponent tensor model was fit to the diffusion data residing within white matter. Seeds were generated in a 2 × 2 × 2 arrangement for each voxel within the boundary mask, resulting in 8 seeds per boundary voxel. Deterministic tracing was then performed using a half-voxel step size, and streamlines shorter than 3-points in length were discarded as spurious. Once streamlines were generated they were traced through the parcellation. Edges were added to the graph corresponding to the end-points of each fiber, and were weighted by the streamline count. This pipeline was implemented in Python, including a few components in Cython, and relies on the Numpy library for a large proportion of operations.
F. Monte Carlo Arithmetic Verificarlo  [10]  is an extension of the LLVM compiler which automatically instruments floating point operations at build-time for software written in C, C++, and Fortran. Once compiled with Verificarlo, the Monte Carlo emulation method and target precision can be set as environment variables. For all simulations a rounding error on the least significant floating point bit in the mantissa (bit 53) was introduced. The simulations were computed using the custom QUAD backend which is optimized to reduce computation time over the traditional mcalib MPFR backend leveraging GNUs multiple precision library  [9]. Noise through Verificarlo can be injected as "Precision Bounded", simulating floating point cancellations, "Random Rounding", simulating only rounding errors on computation, and "MCA", which includes both of these modes.
A particularity of the Random Rounding mode is that it only injects rounding noise on inexact floating-point operations (i. e. operations that have a rounding error in IEEE-754 at the target precision). Therefore, RR mode preserves the original exact operations, it is a more conservative noise simulation. We used both the RR and MCA modes of simulation. Verificarlo was used to instrument tools in two modes we will refer to as "Python" and "Full Stack". In the Python instrumentation, the core Python libraries were recompiled with Verificarlo as well as any subsequently installed Cython libraries. In the Full Stack instrumentation, BLAS and LA-PACK were also recompiled, meaning that Numpy, a dominant Python library for linear algebra, was also instrumented. The Full Stack implementation did not run successfully using the MCA mode.
G. Operating System Variation Operating system noise was evaluated across Alpine Linux 3. 7. 1 and Ubuntu 16. 04. Alpine is a lightweight distribution which comes with minimal packages or libraries, and Ubuntu is a popular Linux distribution with a large user and development community. Alpine was chosen as its lightweight nature makes it an efficient choice for the packaging and distribution of libraries in containers for scientific computing, reducing the overhead of shipping code towards data sources. Ubuntu was chosen due to is high adoption and community support by major libraries. While Alpine comes with a minimal set of libraries, a core difference between these systems as noted by DistroWatch (https://distrowatch. com/) is their dependence on a different version of the Linux kernel.