dataesr/scientific-paragraphs-categorization huggingface dataset:
10 , a more stable behavior is observed. The matrix MCAs, which should deliver the same performance independently of the database, provide practically the same performance with the SH equivalence, despite showing numerical differences in the final inhomogeneity. From this observation, the SHR was envisioned as a proposed new metric for assessing shim system performance and comparing MCAs (more results bolstering SHR robustness are shown in  38  ). Despite a realistic SCOTCH performance lower on OAD than on IHD, SHR still exceeds 6. 5, making SCOTCH3. 48 equivalent to a system correcting up to partial 7 th order SH with no power limitation. The loss of performance on the OAD could be explained by a IHD-biased SF-SVD design. Alternatively, the re-positioning of the OAD fieldmaps to match the AVANTI2 coil dimensions was performed such that subjects were lifted in the PA direction only enough to respect the estimated position limits.
This caused the fieldmaps to be located further away from the frontal channels, compared to imaging routine where cushions can be positioned under the subject head, lifting it even more. In this way, OAD performance could be further reduced. This was nevertheless a conscious choice to avoid biasing the shifted database in our favour, but might have biased it to provide lower performance. The issue of the variability of inhomogeneity values observed across different research sites has been mentioned in some works  15;17. This variability might be caused by different factors ranging from fieldmap acquisition and postprocessing to brain mask topology differences. Translating inhomogeneity values into the SH equivalents can provide a sense of engineering effort required to achieve a certain homogeneity level.
| In-Vivo Measurements A good match between measured and simulated fieldmaps was observed after motion correction, with an average difference of only 1. 6 Hz between expected and measured inhomogeneity (with measured inhomogeneity higher than simulated). SCOTCH provided a mean 38% shimming performance improvement compared to the scanner shim, comparable to what would be achieved by a partial 7 th order unconstrained SH system, as indicated by an average SHR of 6. 34, which is close to the 6. 5 score found for the 0A database. Our inhomogeneity match was slightly better than what was observed in  7  (average 2 Hz difference in global inhomogeneity across 6 subjects in dynamic shimming) and significantly better than in  23  , where the difference was 4. 8 Hz (when re-scaled to 7 T) averaged across 5 subjects.
SCOTCH shimming simulations performed on the (a) in-house (IHD) and (b) open-access (OAD) databases. Measured and simulated field bases are used. M-MCAs are also simulated on both databases for comparison with the SCOTCH optimized MCA designs. Electric current is constrained to 3 A in all MCA shimming simulations. Average inhomogeneity values obtained by unconstrained SH shimming simulations are indicated by dotted lines with the SH order indicated on the right of the plots. Shimming simulations on the OAD serve the purpose of external validation. F I G U R E 1 1 Power dissipation for different shimming system configurations as estimated from simulations on the IH and OA databases at 3 A maximum current per channel. Simulated and measured field bases are also considered for comparison.
by the 96-channel m-MCA, power dissipation values are 38. 2 W (SD: 4. 3 W) and 35. 7 W (SD: 4. 4 W) for the IH and OA databases, respectively. For SCOTCH-3. 48, power dissipations estimated from simulations with measured fields and resistances were 22. 6 W (SD: 3. 7 W) and 22. 6 W (SD: 3. 3 W) on the IH and OA databases, respectively. These are slightly superior to the 19. 4 W (SD: 2. 9 W) and 20. 0 W (SD: 2. 6 W) obtained from ideal SCOTCH simulations. Power dissipation is multiplied at least by six between the 1. 24 configuration and the 3. 48 configuration, with the former.
Acknowledgements We would like to thank  Guy Aubert  for helpful discussions,  Edouard Chazel  for the making of the AVANTI2 coil,  Benedikt Poser  for the 3D-EPI sequence,  Michel Bottlaender  and  Josselin Houenou  for allowing the IHD constitution,  Chantal Ginisty ,  Valérie Berland  and  Yann Lecomte  for the acquisition of the IHD data. Furthermore, we also wish to thank the  Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA) for internal grants ("CFR" for BPM 's PhD scholarship, and " DRF-Impulsion SHIM11. 7"for the making of SCOTCH).
F I G U R E 1 0 1 SCOTCH shimming simulations performed on the (a) in-house (IHD) and (b) open-access (OAD) databases. Measured and simulated field bases are used. M-MCAs are also simulated on both databases for comparison with the SCOTCH optimized MCA designs. Electric current is constrained to 3 A in all MCA shimming simulations. Average inhomogeneity values obtained by unconstrained SH shimming simulations are indicated by dotted lines with the SH order indicated on the right of the plots. Shimming simulations on the OAD serve the purpose of external validation. F I G U R E 1 1 Power dissipation for different shimming system configurations as estimated from simulations on the IH and OA databases at 3 A maximum current per channel. Simulated and measured field bases are also considered for comparison.
by the 96-channel m-MCA, power dissipation values are 38. 2 W (SD: 4. 3 W) and 35. 7 W (SD: 4. 4 W) for the IH and OA databases, respectively. For SCOTCH-3. 48, power dissipations estimated from simulations with measured fields and resistances were 22. 6 W (SD: 3. 7 W) and 22. 6 W (SD: 3. 3 W) on the IH and OA databases, respectively. These are slightly superior to the 19. 4 W (SD: 2. 9 W) and 20. 0 W (SD: 2. 6 W) obtained from ideal SCOTCH simulations. Power dissipation is multiplied at least by six between the 1. 24 configuration and the 3. 48 configuration, with the former presenting 3. 2 W (SD: 1. 6 W) and 2. 6 W (SD: 1. 2 W) power dissipation, for IH and OA databases, respectively, with ideal SCOTCH simulations.
The first and perhaps most time-consuming task in preparing conservation assessments under criterion B is to obtain a realistic view of a taxon's past and current distribution. For tropical plants, this is generally derived from herbarium specimens. Although large-scale digitisation programmes have increased the availability of digital biodiversity data, the specimen data are far from complete, up-to-date, accurate, or clean  (Graham et al. 2004; Yesson et al. 2007; Nelson and Ellis 2018; Zizka et al. 2020b ). On the contrary, limitations in the quantity and quality of plant occurrence data along taxonomic, geographic, and temporal dimensions may hamper their use in research and conservation applications  (Meyer et al. 2016). Many specimens in herbaria and databases lack geographic coordinates  (Nic Lughadha et al.
Also, conservation assessments should be preferably carried out on manually cleaned and expert-validated data  (Hjarding et al. 2015; Panter et al. 2020). Here, we provide a high-quality, expert-validated occurrence dataset compiled by the ECAT project, which is part of the larger Global Tree Assessment (GTA) coordinated by Botanic Gardens Conservation International (BGCI 2021). The ECAT project aimed to prepare global conservation assessments for all trees endemic or sub-endemic to the region comprising DR Congo, Rwanda, and Burundi, hereafter referred to as Central Africa. It was executed in collaboration with the IUCN SSC Central African and Eastern African Plant Red List Authorities (CARLA and EAPRLA). We summarise how the data was compiled, which included normalisation, harmonisation, aggregation, data transcription, geo-referencing, quality control, data cleaning, and validation by data managers and botanical experts.
We conclude by highlighting the taxonomic, spatial, and temporal coverage of the data. The ECAT dataset was not only used to develop or update the conservation assessment of 347 Central African tree taxa, many of which are threatened with extinction, but will also be used in a series of future studies (e. g. , studying the effectiveness of the protected area network in the conservation of threatened tree species). Through this project, we hope to support and help guide effective management and conservation strategies to preserve the unique plant diversity of Central Africa.
Methods Based on data available at Meise Botanic Garden, supplemented with data from the BGCI GlobalTreeSearch  (Beech et al. 2017) , we compiled a list of 347 tree taxa endemic or sub-endemic to Central Africa, including their geographic distribution and nomenclatural synonyms (totalling 481 names). Accepted names generally follow the African Plant Database (version 3. 4. 0. 2018), which for Central African taxa draws heavily on the Flore d'Afrique centrale, Flora of Tropical East Africa, and Flora Zambesiaca. We defined a tree as any woody single-stemmed plant at least 3 m tall. Taxa still met this definition if they usually occur as a shrub or a liana and only occasionally in the form of a tree. This list was then verified by the IUCN SSC Global Tree Specialist Group. We differentiated between endemic and sub-endemic taxa based on their spatial distribution relative to the land borders of DR Congo, Rwanda, and Burundi.
We considered 219 taxa as Central African true endemics as their current distribution range is restricted to DR Congo (186 taxa), Rwanda (3), Burundi (2), or a combination of these three countries (28). The remaining 128 taxa from our list were deemed sub-endemic to Central Africa. For 116 of these sub-endemic taxa, all herbarium specimens in our dataset originated from the area delineating DR Congo, Rwanda, and Burundi, extended by a 5-degree buffer zone. For the remaining 12 sub-endemic taxa in our study, most specimens were from Central Africa (70-94%), with only a few collected outside the 5-degree buffer zone (1-23%). We retrieved the specimen data for these taxa and their synonyms from our institutional collection database (BR; all herbarium acronyms according to Thiers 2018) and supplemented them with data from RAINBIO, a database of tropical African vascular plants distributions  (Dauby et al.
2016). RAINBIO contains geo-referenced specimen records from a number of institutional collections with a strong focus on Africa, together with several personal databases collated by individual researchers (for details on the construction of the RAINBIO database and the quality checks performed, see  Dauby et al. 2016). Occasionally, missing specimen records were added from online institutional data portals such as TROPICOS and JSTOR-Global Plants, while some verified specimen records were added from other sources such as taxonomic revisions and floras (e. g. , Flora of Tropical East Africa, Flora Zambesiaca). The specimen data from all these different sources were thoroughly pre-processed before aggregating them into one comprehensive dataset. For instance, discrepancies in taxon names were resolved and harmonised with respect to synonymy and re-identifications, two-digit country codes were employed in accordance with ISO 3166, collector names were standardised, and collecting dates were converted to YYYY-MM-DD format.
The dataset was meticulously reviewed and edited to merge duplicate specimen records (specimens of the same taxon made by the same collector at the same place and time, usually collected from the same tree). These duplicates often did not have the same quantity and quality of transcribed metadata due to missing information on some specimen labels, incomplete label transcription in the database, or transcription errors. When merging duplicates, we took care to retain all metadata relevant to Red List assessors and to resolve any inconsistencies. Transcription of specimen labels is often restricted to selected data fields due to resource constraints. As a result, a considerable amount of descriptive information relevant to Red List assessments may be missing from specimen databases. To enrich our data, we transcribed specimen label data focusing mainly on gaps in the locality description, habitat, and elevation.
The newly transcribed data allowed us to geo-reference several specimens without coordinates and improve the geo-referencing accuracy of others. Although recent herbarium specimens increasingly contain accurate coordinates captured in real-time using a GPS device, this is not the case for the bulk of the Central African collection at BR that predated GPS devices. It was often possible to infer the geographic coordinates from the transcribed data using historical topographic maps and gazetteers or by checking the collector's itinerary. Specimen records that could not be geo-referenced because the locality description was missing, too vague, or unclear (e. g. , illegible handwriting) were removed from the dataset. The dataset was checked for any spatial errors through an iterative series of inspections.
First, we used the R package CoordinateCleaner version 2. 0-18  (Zizka et al. 2019)  to flag records with potentially erroneous coordinates, including those that fall in the ocean or outside the indicated country, those that coincide with country and province centroids, and those with zero latitudes or longitudes. Next, we checked the data for intrinsic consistency in a GIS environment to flag additional problematic records. For example, we verified that the coordinates fell within the province or district as stated on the specimen label and checked the collector's itinerary to evaluate the geo-referencing quality in suspect cases. This approach revealed several spatial errors missed by the automated cleaning. We checked all flagged records one by one and made corrections where appropriate.
Common causes of spatial errors were the inversion of latitude and longitude, the lack of a minus sign for south or west, coordinate transformation errors, and typographical errors. Even after extensive data cleaning, some geographic discrepancies remained. Occurrences falling in a country different from the one stated on the specimen label but for which the distance to the land border was less than 5 km were retained in our dataset. We considered this margin acceptable given the uncertainties associated with natural history data, the precision of the locality data, and the spatial precision of the country GIS layers used. Other records whose coordinates were considered incorrect or too imprecise for our purposes (e. g. , specimens geo-referenced to the country centroid) were removed from the dataset.
Results The initial raw dataset contained data from 9956 specimens. The majority of these (83. 4%) were deposited in the herbarium of Meise Botanic Garden, underlining its importance for the flora of Central Africa. Other herbaria represented in the dataset are B, BM, BRLU, C, COI, EA, EALA, EPU, FHO, GENT, H, HBG, IEC, IUK, K, KAW, KISA, LBV, LG, LISC, LISU, LSHI, LUKI, LWI, M, MA, MB, MHU, MO, MPU, NDO, NHR, NHT, P, PRE, SRGH, UPS, W, WAG, and YBI. As part of the data enrichment, we transcribed locality data for 690 specimens, habitat data for 2802 specimens, and elevation data for 3796 specimens (this includes values indicating that information is 'known to be unknown'). One-third of the specimens (33. 1%) had no coordinates. During the ECAT project, 2923 specimens were geo-referenced, leaving 372 without spatial data.
The new coordinates were derived mainly from maps and gazetteers; only for a small number of them (374) could they be copied from duplicates. After several quality checks on the geo-referencing, we adjusted the coordinates for 1774 specimens. For three-quarters of them, it concerned a relatively minor adjustment moving the occurrence up to 10 km. For the remaining quarter, this exceeded 10 km (up to as much as 3915 km). The taxonomic identification was updated for 509 specimens (changes due to synonymy or misspellings not taken into account). We removed 1046 specimens from the dataset on taxonomic or spatial grounds, leaving 8910 specimens in the cleaned dataset. After merging all duplicate specimens, we obtained a dataset with 6361 geo-referenced plant collection records.
Taxonomic coverage The ECAT dataset contains distribution data of 337 taxa at specific or infraspecific level (subspecies or variety) belonging to 153 genera in 52 families and 20 orders. The family classification follows APG IV (The Angiosperm Phylogeny Group 2016), except in some cases where the authors thought a more conservative approach to be appropriate (such as still recognising Flacourtiaceae, Sterculiaceae, and Tiliaceae). The difference in the number of taxa in the dataset (337) compared to our taxon list (347) arose because in the latter several species are present including their infraspecific taxa as separate entries, whereas the corresponding specimens in the dataset were all identified to infraspecific level. The number of records per taxon ranged between 1 and 130 (median: 10; mean: 19 records per taxon).
Limitations of the data For a tropical region to be considered reasonably well-known botanically (vascular plants), a rule of thumb is that the minimal level of botanical exploration should be at least 100 specimens per 100 km 2  (Campbell and Hammond 1989). For Central Africa, this means that a minimum of 2. 4 million herbarium specimens are needed, or by country, roughly about 2. 35 million for DR Congo, 26,000 for Rwanda, and 28,000 for Burundi. The number of realised herbarium collections is estimated at 380,000 for DR Congo, 31,000 for Rwanda, and 37,000 for Burundi  (Sosef et al. 2021 ). These numbers suggest that while Rwanda and Burundi can be regarded as "reasonably well known", the botanical wealth of DR Congo remains "poorly known". Moreover, the ECAT dataset suffers from the same sampling bias characteristic of many other natural history collections from tropical areas, with specimens collected mainly along roads, near urban populations, and in areas of specific botanical interest, with few collection  records in remote areas (Fig.
Acknowledgements We acknowledge all those who have contributed to the development of the ECAT dataset, with special thanks to  Ann Bogaerts ,  Israel Borokini ,  Luís Catarino ,  Helen Chadburn ,  Sara Contu ,  Rogier de Kok ,  Sofie De Smedt ,  Mathias Dillen ,  Ryan Hills ,  Lucia Lopez Poveda ,  Barbara Mackinder ,  Pierre Meerts ,  Malin Rivers , and  Xander van der Burgt. We also gratefully acknowledge  Craig Hilton-Taylor  from the  IUCN Red List Unit  and the  IUCN SSC Global Tree Specialist Group  for verifying our taxon list and for their guidance in submitting all data to the  IUCN Species Information Service (SIS). This work was made possible thanks to funding provided by the  Franklinia Foundation.
However, in most automatic classification problems, obtained predictions are often closely linked to the representativeness and the variability of the observations composing the training set. Even if the creation of this kind of set represents a crucial step, obtaining labels for a large number of observations (as required for DL) can be very expensive in terms of time, human resources and skills to build an 'ideal' training set allowing to obtain accurate results for any disparate datasets, and in particular, for the study of living organisms which are subject to environmental pressures, and must therefore take into account the potential seasonal morphological modifications of the objects (e. g. plankton cells). In this sense, building a new relevant model from scratch, at each new detected event, can be time-consuming (from several hours to several days) and often requires balanced object classes with a large number of labeled images for each of them, which represents a hard challenge, due to the current limits of actual acquisition devices and the rarity of some organisms  (Agarwal et al.
, 2021; Lumini and Nanni, 2019). This shortcoming represents the motivation for Transfer Learning (TL). TL is used to improve a classifier by transferring information learnt from many domains thanks to a very large training set composed of various images, to another domain with a smaller amount of training data  (Pan and Yang, 2010; Yosinski et al. , 2014). Indeed, one person is able to take information from a previously learned task and use it in a beneficial way to learn a related task. To address the classification challenge and to compensate the lack of 'easy-touse' software for TL, we propose EcoTransLearn, an R-package including a simple graphical user interface (GUI) dedicated to image classification by TL, and we focus on needs identified by the scientific community involved in coastal marine observation, such as the identification of phytoplankton (via FlowCam or flow cytometers images), zooplankton (via ZooScan images or photomicrographs) or simple pictures of fish or benthic organisms.
Acquisition and image pre-processing EcoTransLearn supports different kinds of data, from individual images to collage files (regrouping several images), and JPEG, PNG and TIFF formats. An optional parameter can be set to keep only predictions with a class probability defined by the user. (Simonyan and Zisserman, 2015). Each model was pretrained on 1. 4 M images binned into over 1000 classes from the ImageNet dataset, which is an image database, organized according to the WordNet hierarchy  (Deng et al. , 2009). In this way, pre-trained models have already learned generalizable features from the ImageNet dataset, which includes animals, sports objects, computers and other classes which can be very different from ecological domains, that provides a powerful baseline for feature recognition.
A plankton case study The FlowCam V R device (Yokogawa Fluid Imaging Technology, Inc. ) is an imaging-in-flow system. A training set was built from samples acquired in Channel and North Sea in the frame of the IFREMER SRN network (Regional Observation and Monitoring Program for Phytoplankton and Hydrology in the English Channel. https://doi. org/10. 17882/50832), which consists in analyzing phytoplankton composition along three transects located in the Eastern English Channel in order to assess the influence of continental inputs on the marine environment and particularly on phytoplankton dynamics. The final dataset contains about 31 700 images sorted in 26 groups. Table  1  presents the relevance of performance scores obtained for each pre-trained CNN model, compared to a more traditional ML algorithm: Random Forest (RF with a number of trees set to 500).
Conclusion EcoTransLearn is dedicated to the performance improvement of automated recognition of digital images obtained from various devices used in marine biology and ecology, thanks to Transfer Learning technique. Its simplified GUI, coupled with its execution in a popular programming language (R) makes it easy to obtain and manipulate the results obtained. The performance of this tool and its ease of use suggest that it could be relevant for other scientific communities interested in image recognition, but with some adaptations during the pre-processing step (e. g. image format).
Funding This work was financially supported by the  European Union (ERDF) , the  French State , the  French Region Hauts-de-France and Ifremer , in the framework of the projects CPER  MARCO  and  S3-EUROHAB  ( Sentinel-3 products for detecting EUtROphication and Harmful Algal Bloom events ). The JERICO- S3  project is funded by the  European Commission 's  H2020 Framework Programme  [871153].