dataesr/scientific-paragraphs-categorization huggingface dataset:
The PPEAO information system Starting in the early 1960s, the French «Office de la Recherche Scientifique et Technique Outre-Mer» (ORSTOM), which in 1998 became the «Institut de Recherche pour le Développement» (IRD), studied West African aquatic ecosystems. These studies included sampling of aquatic communities by scientists and surveys of artisanal fisheries that exploit the ecosystems. Several research programmes focused on various estuarine, lagoon, coastal and lake ecosystems in West Africa involving the same scientific team succeeded one another between 1979 and 2013. From 2001 onwards, the IRD research programme "Adaptative responses of fish populations and assemblages to the environmental pressures" ("Réponses Adaptatives des populations et des peuplements de Poissons aux pressions de l'environnement", French acronym RAP) funded the development of two MS-Access databases and of the software needed for their management and for user consultation: "Pechart" for the artisanal fisheries database  (Ndiaye et al.
2003a ) and "Pechexp" for the experimental fishing database  (Ndiaye et al. 2003b). In these two databases, historical data  was gathered in a homogeneous format along with new data collected in the RAP research programme between  2001 and 2008. Then, in 2006 , to make the data available to a wider scientific community, the two databases were merged into one PostgreSQL database, which is located on an IRD server. The development of this database, called PPEAO for "Peuplements de poissons et Pêche artisanale des Ecosystèmes estuariens, lagunaires et continentaux d'Afrique de l'Ouest" and of its management and consultation software, were funded by IRD, through the support project "Soutien aux Projets Informatiques dans les équipes Scientifiques" (SPIRALES -  Ecoutin et al. 2007). PPEAO software can be accessed at http://ppeao.
The dataset available on GBIF. org A first dataset gathering all the species occurrences observed by experimental fishing was extracted from PPEAO and put online on GBIF. org. It is available at 10. 15468/ra4voa. The present datapaper describes this dataset, which corresponds to 213 fishing surveys, 5,362 fishing hauls and 31,709 occurrences (28,428 of fish taxa and 3,281 of crustaceans and molluscs). The number of individuals per species and per haul is available and 80% of occurrences are geolocated. In the following section, we provide a general description of the dataset, after which we give a more detailed description of each ecosystem with their respective sampling protocols. Sadio (sample collection, sample identification), Monique Simier (data management), Luis Tito de Morais (sample collection, sample identification), Guy Vidy (sample collection, sample identification).
Other collectors: Eric Baran (Guinea), Famara Darboe (Gambia), Itaf Deme-Gningue (Senegal), Papa Samba  Diouf (Senegal)  Study area description: General spatial coverage: West Africa (Fig. 1 ) Coordinates: 4°N and 21°N Latitude; 18°W and 2°W Longitude Design description: The sampling surveys completed and embedded in the PPEAO database had different scientific objectives, for example, describing the fish community of an aquatic ecosystem and its space-time variability, searching for particular species, comparing and calibrating biological indicators, testing new sampling devices, demonstrating fishing techniques or carrying out evaluations. In the first case, a sampling protocol was defined, with particular fishing gear and sites that were regularly sampled during each survey. Funding: IRD, Institut de Recherche pour le Développement, France.
Sampling methods Study extent: This dataset covers several aquatic ecosystems in West Africa (estuaries, lagoons and reservoirs -Fig. 1 ): "historical" data collected since 1979, data sampled by the IRD RAP research unit between 2000 and 2008 and recent data, i. e. up to 2013 (Table  1 ). The taxa are mainly fish and a few crustaceans and molluscs. Sampling description: Methods: The sampling unit was the fish haul defined by: a site (station), a date (survey), the fishing gear used. After each fishing haul, all the individuals captured (fish, crustaceans, molluscs) were identified, if possible at the species level. All the individuals captured in the same haul and belonging to the same species were counted. Sampling: Each geographical entity studied was considered as an ecosystem. In each ecosystem, a number of sampling sites was defined.
Taxonomic coverage Description: The main aim of samplings for this dataset was to describe the fish communities. Consequently, fish account for the majority of observed taxa (278 out of 314), but some crustaceans, molluscs and cnidarians were also observed (18, 17 and 1 individual, respectively). Amongst the 278 fish taxa, 261 were identified at the species level, representing 98. 6% of the occurrences, 14 at the genus level (0. 8% of the occurrences) and 3 at the family level (0. 6% of the occurrences). Fish taxonomy is given according to the Catalog of Fishes Online Database  (Fricke et al. 2018) , the authoritative reference for taxonomic fish names. Despite the high variability of the sampling effort in the different countries (from 3 surveys in Mauritania to 93 surveys in Senegal), the total number of families identified per country was stable, ranging from 40 to 60, except in Mali where only 14 families were observed (Table  3 ).
Detailed description of the dataset We now provide a detailed description of the climate, geography, sampling protocol and main characteristics of the assemblages in each monitored ecosystem in chronological order. In the case of the Sine Saloum delta, which was the target for several research projects from the 1990s to the 2000s, all the sampling periods are included in the same section. Conversely, the Bamboung MPA, situated in the Sine Saloum delta, was the subject of a particular monitoring programme and is described separately. Sampling in 1993 in Guinea-Bissau and monitoring of the Urok Islands MPA between 2011 and 2013 are also described separately because they had different objectives.
The 1990s In the early 1990s, a research programme was conducted in the Sine Saloum delta by Orstom and the "Centre de Recherche Océanographique de Dakar Thiaroye" (CRODT), part of the "Institut Sénégalais de Recherches Agricoles" (ISRA), with the collaboration of the "Ecosystèmes Lagunaires" laboratory belonging to Montpellier University, France. This research programme was funded by Orstom. The dataset contains data collected between 1990 and 1997 by the two components of the programme. The "adult and subadult fish assemblages" component was conducted by Jean-Jacques Albaret and the results were published by  Diouf (1996)  and later by  Simier et al. (2004). For the "juvenile fish assemblages" component, conducted by Guy Vidy  (Vidy 2000) , the dataset specifically comprises data collected using fyke nets.
General description of catch data The consecutive research programmes in the Sine Saloum delta recorded 8,567 fish occurrences, in all the periods combined, corresponding to 56 families and 126 taxa, 95. 5% of them being identified at the species level, 2. 5% at the genus level and 2% at the family level ( Table  6. List of the 22 main fish taxa identified in the Sine Saloum delta, with their total abundance (number of individuals) and their proportion (%). These taxa represent 98% of the total number of fish caught. During the samplings in the Sine Saloum delta, 969 occurrences representing 14 taxa other than fish were also recorded, of which 66. 7% were identified at the species level, 18. 3% at the genus level, 7. 9% at the family level and 7% at the order level. Most of the occurrences corresponded to shrimps Penaeus notialis and crabs Callinectes amnicola.
These taxa represent 98. 6% of the total number of fish caught. The Banc d'Arguin National Park was sampled as part of the AMPHORE project "Marine Protected Areas: biodiversity conservation tools and sustainable management of fishery resources" ("Les Aires Marines Protégées: outil de conservation de la biodiversité et de gestion durable des ressources halieutiques") funded from 2008 to 2011 by the French National Research Agency (http://www. agence-nationale-recherche. fr/?Projet=ANR-07-BDIV-0009). A purse seine (250 m long, 18 m deep with a 14 mm mesh size) was used to sample fish assemblages by a team of IRD scientists, in cooperation with the Mauritanian Institute of Oceanographic Research and Fisheries (IMROP). Thirty four sites were defined inside and outside the MPA. Some had to be cancelled because they were not accessible by boat.
Sample collectors: Jean-Jacques Albaret, Jean Raffray, Oumar Sadio and Luis Tito de Morais. This dataset is the result of the biological survey of the Bamboung Marine Protected Area (MPA) in Senegal located 130 km south of Dakar, between 13. 75° and 13. 84° North and 16. 5° and 16. 56° West, in the Biosphere Reserve of the Sine Saloum Delta (Fig. 4). The Bamboung bolong is a small affluent of the Diomboss, one of the main branches of the Sine Saloum Delta. The MPA covers 68 km², with a wetland zone composed of mangrove and savannah and a water body of approximately 4 km². It is one of the sites chosen for the purpose of demonstration in the "Narou Heuleuk" (tomorrow's share) project. It was proposed and driven by the Oceanium, a Senegalese organization for the protection of marine resources, and originally funded by the French Fund for the World Environment (Fond Français pour l'Environnement Mondial -FFEM).
The biological monitoring of the Bamboung MPA, was implemented by the RAP research unit of IRD and funded from 2008 to 2011 by the French National Research Agency (http://www. agence-nationalerecherche. fr/?Projet=ANR-07-BDIV-0009) as part of the AMPHORE project "Marine Protected Areas: biodiversity conservation tools and sustainable management of fishery resources" ("Les Aires Marines Protégées: outil de conservation de la biodiversité et de gestion durable des ressources halieutiques"). From 2009 to 2012, this study was part of the CEPIA project of the Sub-Regional Fisheries Commission (Commission Sous-Régionale des Pêches -CSRP) entitled "Building together a fisheries management integrating MPA" ("Construire Ensemble une gestion des Pêches Intégrant les AMP"), funded by the French Development Agency (AFD) and coordinated by the International Union for Conservation of Nature (IUCN).
sp. 1817) Soleidae Kaup, 1858 Kaup, 1856 Solea senegalensis Syngnathidae Hippocampus algiricus Schilbeidae Parailia pellucida Valenciennes, 1843 Soleidae Solea sp. (Bleeker, 1863) (Linnaeus, 1758) (Valenciennes, 1840) Sciaenidae Umbrina ronchus (Bleeker, 1863) Syngnathidae Enneacampus kaupi Scaridae Sparisoma rubripinne Valenciennes, 1843 Soleidae Pegusa triophthalma Linnaeus, 1758 (Bloch, 1795) Scaridae Scarus hoefleri (Steindachner, 1881) 1758 Sciaenidae Umbrina canariensis Soleidae (Moreau, 1881) Stromateidae Stromateus fiatola 1758 Dicologlossa cuneata Cuvier, 1829 Zeidae Zeus faber Linnaeus, 10,659 2 323 513 496 1 18 3 1,918 5 72 616 9 1 2 171 1 2,293 53 3 14 731 1 3 171 112 456 16 4 383 134 239 152 882 879 2 1 22 17 1 7 1 4 5 1 22 1 1 63 1 1 3 191 56 66 83 2 27 20 7 283 12 327 454 1 239 42 432 1 150 63 2 93 138 25 3 17 253 9 105 2 1 23 4,394 2 1 1 2 2 62 3 1 2 1 2 1 16 74 1The PPEAO experimental fishing dataset: Fish from West African estuaries,.
As mentioned above, fish assemblages were the main target of experimental fishing surveys. However, other groups (crustaceans, molluscs and cnidaria) were caught accidentally and, in some ecosystems, recorded in the database. Their taxonomy is given according to the World Register of Marine Species (WoRMS Editorial Board 2019). Country Surveys Families Taxa Guinea 20 40 86 Guinea-Bissau 5 45 71 Gambia 13 40 92 Senegal 93 59 141 Mauritania 3 41 84 Mali 6 14 50 Phylum Class Order Family Taxon Nt Arthropoda Malacostraca Decapoda Galatheidae Galathea sp. 8 Arthropoda Malacostraca Decapoda Inachoididae Stenorhynchus sp. 7 Arthropoda Malacostraca Decapoda Menippidae Menippe nodifrons Stimpson, 1859 1 Arthropoda Malacostraca Decapoda Ocypodidae Afruca tangeri (Eydoux, 1835) 2 Arthropoda Malacostraca Decapoda Palaemonidae Macrobrachium vollenhoveni (Herklots, 1857) 269 Arthropoda Malacostraca Decapoda Palaemonidae Nematopalaemon hastatus (Aurivillius, 1898) 1 Arthropoda Malacostraca Decapoda Penaeidae Holthuispenaeopsis atlantica (Balss, 1914) 1 Arthropoda Malacostraca Decapoda Penaeidae Penaeus kerathurus (Forskål, 1775) 16 Arthropoda Malacostraca Decapoda Penaeidae Penaeus monodon Fabricius, 1798 2 Arthropoda Malacostraca Decapoda Penaeidae Penaeus notialis Pérez Farfante, 1967 17,644 Arthropoda Malacostraca Decapoda Portunidae Callinectes sp.
of taxa other than fish, ordered by phylum, class, order, family and species, with their total abundance (number of individuals)Usage rightsUse license: Other IP rights notes: This work is licensed under a Creative Commons Attribution Non Commercial (CC-BY-NC) 4. 0 License Data resources Data package title: The PPEAO experimental fishing dataset: Fish from West African estuaries, lagoons and reservoirs Resource link: https://www. gbif. org/dataset/41f549a6-c7ae-4460-8b2b-3333b1a036cd Number of data sets: 1 Data set name: PPEAO -Pêches scientifiques. Peuplements de poissons des écosystèmes estuariens, lagunaires et continentaux d'Afrique de l'Ouest Character set: UTF-8 Download URL: http://ipt. gbif. fr/archive. do?r=ppeao-pechexp Data format: Darwin Core Archive format Data format version: 1.
Introduction The LHCb Online group provides engineering support and development to the different LHCb subdetectors, mainly in relation to readout board firmware, low-level software for frontend configuration and data acquisition, and control system components. Given the relatively small size of the core developer group (half a dozen people), the very hetereogeneous nature of the different subsystems to be supported, and the constant evolution of the needs of subdetector groups under very different circumstances (ranging from frontend validation, to test beams, to detector assembly and final commissioning), a clear need emerged for a way to reduce the time and effort required to detect, reproduce and correct integration issues during the development cycle. The following sections describe, in order, the different phases of this cycle.
Build Submission of a firmware merge request automatically triggers a dedicated GitLab continuous integration pipeline. This pipeline executes Questa RTL simulations according to several predefined firmware configurations and, if successful, executes the Quartus FPGA synthesis flow for each of the different applications where the common readout boards are used throughout LHCb. The flow automatically targets all required hardware platforms, including legacy (but still used and maintained) revisions of our readout boards (figures 1, 2), in addition to the final PCIe40 design to be used in Run3 (figure  3 )  [2]. For each board type, FPGA synthesis needs to be repeated for both the data plane and the control plane of each of the six LHCb subdetectors. Given the complexity of modern high-capacity FPGAs, producing all required permutations requires of the order of.
• Documentation • SRAM Object Files to reconfigure the readout board FPGA at runtime • Programmable Object Files to persist the FPGA firmware on the readout board flash memory • Firmware quality reports Firmware reports are generated by analyzing the internal Quartus result database and are stored in JSON format to be easily imported by dedicated scripts for plotting and analysis. The reports include metrics such as: • FPGA resource utilization (ALMs, BRAMs, DSPs, HSSIs) • System resource utilization (CPU time, wall time, memory) • Critical path and Total Negative Slack for all clocks and all timing corners Finally, both software and firmware are automatically packaged in RPM format and published to dedicated RPM repositories for distribution, as is customary on all Linux installations deployed at CERN.
PoS(TWEPP2018)069 Continuous integration of FPGA firmware and software for the LHCb Run3 upgrade Paolo Durante kind of setup requires all hardware, firmware and software components to correctly integrate with each other. In order to validate this kind of integration we have instrumented WinCC OA with dedicated tools and scripts, implementing an automated test harness. A test harness consists of a linear sequence of steps, each step representing operations that an operator or an automatic run control would execute on the system (for example: programming a readout board, synchronizing all its clocks sources, configuring its optical links, configuring some frontends, starting a run, reading out data, and so on. ). The process generates a report in TAP format. The "Test Anything Protocol" (TAP) is a formal specification used for communication between unit tests and a test harness.
Acceptance After testing (both manual and scripted), maintainers can decide to accept currently unstable versions (thus promoting them to become the new stable releases) for the firmware and software modules that have been modified since the last release cycle, or to iterate more in case of issues. Depending on complexity, new code can either be fast-tracked for immediate release, or require up to days or weeks of iteration. In either case, the release process has also been automated to require, for the maintainer, only a single operation through git or the GitLab interface for each repository to release from.
Conclusions At the time of this writing, continuous integration has been part of our development flow for over one year. Initially introduced to assist in the software transition from the SLC6 to the CC7 operating system, and from the initial 10GbE-based prototype to the final PCIe-based FPGA readout, its scope has progressively increased to encompass automated firmware simulation and compilation for the six LHCb subdetectors (each requiring multiple configurations) across three hardware platforms, and automated packaging and deployment of JCOP components and WinCC OA projects, while still retaining support for legacy hardware and software. Broken builds are quickly identified and can be immediately acted upon, while nightly builds ensure that firmware images for all desired configurations are always up to date, immediately available for deployment, and easily traceable for comparative testing.
d) A new public dataset, Grasping In The Wild (GITW) which, in contrast to previous databases, addresses the detection of grasping actions in natural environments before the objects are actually manipulated. This paper extends the work proposed in  [24]  , which focused on active object recognition. Here, we present the whole system for grasping action prediction, introduce the block to perform the action recognition, describe all modules in detail, and present an extended set of experiments and conclusions. The remainder of the paper is organized as follows: in Section 2 we discuss related works, in Section 3 a detailed description of our approach is provided, in Section 4 we show our experiments and results; finally, conclusions are drawn and perspectives of research are outlined in Section 5.
Weakly supervised active object recognition We consider a weak label as an annotation provided at a granularity different than the element to be recognized in a particular task: e. g. learning segmentations using image-level labels that indicate the presence of an object in an image, but do not provide any cue about its location in the scene  [25]. Although scene analysis based on weak labels allows reducing the human effort devoted to annotate training datasets  [14]  , it is yet a challenging and open problem due to the distinct granularity of data and labels. In our particular case, whereas object detection networks provide object scores at bounding boxes, our active object labels are given for short video clips, which correspond with those segments of the videos when a subject fixates an object to be grasped.
Gaze-driven active object detection The processing scheme of the Gaze-driven active object detection module is depicted in Fig. 3. It is decomposed into two submodules: a) a Data Processing and Noise Handling block, and b) the Gaze-driven Active Object Detection using weak annotations. The first sub-module processes the aligned gaze points provided by the Geometric Alignment module and generates a set of Regions of Interest (ROIs) per video frame, candidates to contain the object-ofinterest. It also incorporates several processing modules that estimate and reduce the level of noise present in gaze recordings. The second module evaluates the candidate ROIs detecting the objectof-interest along frames of a video clip.
In particular, we have validated these parameters and found that the following values yielded reasonable results: a) if c represents the active object in a video clip, then K ao,min = 0. 85 , and K ao, max is deactivated; b) for those classes c that are considered as non-active in a video segment, K nao,max = 0. 01 , whereas K nao, min is deactivated; and c) for the background class c = 0 , K 0 ,min = 0 and K 0 ,max = 1 -K ao,min = 0. 15. These values deserve a discussion: K ao, min is dependent on the dataset, as it models the percentage of time a subject fixates the object, and is closely related to the probability of finding gaze deviations from the active object. Hence, the presence of very cluttered scenes with many potential distractors would lead to lower values of the parameter whereas simple scenarios in which the active object is not surrounded by any other element would probably accept larger values closer to 1.
Dataset and experimental set-up We run our experiments using a new recorded egocentric dataset: Grasping In The Wild (GITW) 1 ; which was designed and recorded because all previous databases focused on the detection of active objects during their manipulation  [21, 22, 39]  and did not fit with our goal of to-be-grasped object detection. GITW has been recorded with Tobii Glasses 2 worn by subjects performing activities of everyday life in an ecological environment (7 kitchens). It contains 404 egocentric videos of lengths varying between 3. 5 and 26 s, with a total length of 62 min. Videos have been recorded with a resolution of 1920 × 1080 at a frame rate of 25 fps, whereas gaze points were acquired at 50 Hz. As we have two gaze recordings per frame duration, the gaze fixation point associated with each frame has been computed using spline interpolation based on previous and current gaze recordings.
The dataset contains 16 categories of objects to be grasped, which correspond to objects often found in a kitchen: bowl, can of coca-cola, frying pan, glass, jam container, pan lid, milk container, mug, oil bottle, plate, rice container, sauce pan, sponge/scourer, sugar container, vinegar bottle, and washing up liquid. A maximum of 4 different subjects perform activities in each kitchen. The recording protocol was as follows: Each subject first listened to the instruction with the name of the object-to-grasp. Hence the subject explored a visual scene to find the location of target object 1 www. labri. fr/projet/AIV/dossierSiteRoBioVis/GraspingInTheWildV2. htm and finally grasped it. We have annotated the dataset labeling the temporal segment starting when the user fixates the active object and ending at the instant just before the object is grasped.
This segment corresponds with the moment during which we aim to detect the object to-be-grasped. GITW dataset is very challenging because active objects should be recognized before they are actually manipulated (the subject is just looking at them) and scenes usually contain many objects that are not active. We have divided our dataset into 5 folds of 80-81 video clips and around 18,700 frames each, out of which 2290 correspond to the temporal segment of interest. In order to obtain statistically significant results, we have followed a 5-fold cross-validation approach, repeating the experiments 5 times and leaving each time one fold for test. In addition, we decreased the initial resolution of the videos by a factor of 2, working with frames of size 960 × 540.