dataesr/scientific-paragraphs-categorization huggingface dataset:
changes in species-rich insect communities that include "dark taxa", which collectively represent the bulk of forest insect diversity  22. While read-counts may not provide accurate abundance estimates for species, promising developments such as the spike-in method  50  may improve this capability. Nevertheless, our metabarcoding pipeline coupled with recent efforts to complete the DNA barcoding reference library of European insect fauna  19, 51  allowed us to inventory nearly 3000 insect species, more than half of which were identified to species level, including hyper-diverse taxa such as Diptera and Hymenoptera  52. These two taxonomic groups were the most diverse in our study, like in other Malaise trap environmental surveys  53, 54. Although Malaisetrapped samples are biased toward flying insects  55  , the zeta decline analyses indicated that our dataset incorporated rare species as non-stochastic events and tracked changes in both species richness and community composition at both large and small spatial scales while overcoming sampling district discrepancies  29.
Methods Study sites, stand description and insect sampling. Fifty-six natural forest plots were selected for our study in 2017 in silver fir (A. alba) forests in the Central (Aure valley) and Eastern (Sault plateau) French Pyrenees (see Supplementary Data 3 for the complete plot list). In each plot, tree dieback was assessed using the ARCHI method  56. The 20 silver fir trees closest to the plot centre (defined by the Malaise trap), belonging to the dominant layer and with a diameter at breast height (dbh) >17. 5 cm, were examined with binoculars. The count of dying trees and the proportion of affected trees (i. e. trees expressing signs of climate-induced dieback and comprising stressed + resilient trees) were used as a proxy of the decline at the stand level to define a three-category gradient: 23 "high dieback level" plots (at least 1 dying tree and >60% of affected trees), 15 "medium dieback level" plots (no dying trees and >60% of affected trees) and 18 "low dieback level" plots (no dying trees and ≥40% of healthy or resilient trees).
62. Libraries consisting of the total 222 samples were sequenced on five MiSeq runs -v3 (Illumina) with 600 cycles. Use of positive controls and blanks in demultiplexing. Mock communities of known species composition with DNA barcode sequences available were used as PCs. The 248 species used in the mock community all have distributions restricted to east Asia, and hence allow for the examination of tag-jumping in the Illumina sequencing process. PCs were sequenced and bioinformatically treated the same way as bulk samples to ensure comparability. We focused on a restrictive approach, favouring false negatives to false positives (see Alberdi et al. 68  for more information on bioinformatic set-up). To decide both on the number of PCR replicates in which MOTUs must appear and minimum read numbers per PCR, we used BLAST+  69  on the PCs to find the set up in which we could recover the most MOTUs with 100% match and no MOTUs below 97%.
Both NC and EC were checked for reads after demultiplexing to ensure that there was no cross-contamination and removes potential MOTUs recovered in controls from the total dataset. Reads demultiplexing, taxonomic and functional group assignments. Adap-terRemoval ver. 2. 2. 2  70  was used to trim the twin-tagged adaptors of the reads and we employed sickle ver. 1. 33  71  to perform paired-end quality trimming. Error correction using Bayes Hammer was done via SPAdes 72 ver. 3. 12. 0, followed by paired-end merging with PandaSeq 73 ver. 2. 11. PCR replicates were treated following the DAMe pipeline  74. Heatmaps of primer tag combinations versus read numbers were generated using R ver. 3. 6. 1  75  to control for indexing quality. Our twin-tagging approach allowed us to easily identify errors and problems at this step.
Renkonen Similarity Indices (RSI value 76 ) were calculated for unique sequences found in at least three PCR replicates and represented by at least three reads (see section 'Use of positive controls and blanks in demultiplexing' for arbitration of required numbers of PCR replicates and reads). Average read size was plotted in R to ensure filtering and trimming quality, and VSEARCH 77 ver. 2. 8. 1 was run to remove chimeras. Sequences were clustered into Molecular Operational Taxonomic Units (MOTUs) with a 97% similarity threshold 78 using SUMACLUST ver. 1. 0. 20  79. Clustering quality was controlled via R, using the LULU approach  80. Taxonomic assignment was performed by comparing nucleotide sequences with the BOLD System database  27  in April 2019 using a 97% threshold similarity  22  with bold R-package ver.
We identified the insect families with parasitoid and/or floricolous species based on the literature  [82] [83] [84]  and with the help of expert taxonomists. Statistics and reproducibility. All statistical analyses were carried out in R ver. 3. 6. 1  75. Read-sequencing data were transformed in incidence-based data to account for presence/absence of MOTUs given that abundance data from metabarcoding can be misleading for metazoans  85. We generated accumulation curves using iNEXT package ver. 2. 0. 20  86  with incidence-based frequency dataset parameters and Hill number q value set to 0 to reflect truly observed MOTU diversity, regarded as species proxy. Total number of MOTUs was plotted against both sampling units and sample coverage for the late spring-early autumn sampling period as well as for each 4-weeks time series (named May, June, July and August from their respective starting months).
MOTUs belonging to the five most diverse taxonomic orders represented in our dataset (i. e. Coleoptera, Diptera, Hemiptera, Hymenoptera and Lepidoptera) as well as for the remaining 10 Orders (i. e. Blattodea, Ephemeroptera, Mecoptera, Neuroptera, Orthoptera, Plecoptera, Psocodea, Raphidioptera, Thysanoptera and Trichoptera) pooled into an "Others" category, were also plotted against sampling units to account for respective recovery success. We also used both Chao and firstorder Jackknife (Jack1) methods to have different biodiversity estimates from the 'specpool' function of R package vegan ver. 2. 5-6  87. These estimators also extrapolate species richness within the sampled area in regards to sampling effort and based on incidence data. Impacts of tree diebacks on species richness were assessed using generalised linear model (GLM).
For the total dataset, each taxonomic order category of the five main representatives (i. e. Coleoptera, Diptera, Hemiptera, Hymenoptera and Lepidoptera) or over the four ecological groups (i. e. floricolous/non-floricolous adults and parasitoid/non-parasitoid larval feeding guild), GLM was first tested with both Poisson and quasi-Poisson distribution, the latter always retained for its better fit. Then, each model tested the effect of the dieback gradient, stand types, their interaction as well as the effect of districts. When no interaction was found, a simpler additive model was tested. In all nine studied cases, quasi-Poisson distribution and additive model characteristics were retained, as follow: (1) model <-glm(richness ~dieback + salvage + district, family = quasipoisson). We finally applied type II Anova from car package ver.
4. 3. 1 89 R package. For each insect MOTU, taxonomic order was defined as a trait and GLM analyses run to define traits significantly associated to environmental covariates. To determine potential indicator species for each dieback gradient, we used IndVal analysis from 'multipatt' function of indicspecies R package ver. 1. 7. 9  91. Again, reported p-value ranges are resulting from 10 independent IndVal runs with post hoc Holm corrections subsequently applied. We also assessed the importance of rare species-definable from multiple dimensions (i. e. low abundance, habitat specificity, restricted range size, unfrequently encountered, etc) of rarity  92  -in species assemblages specific to both the gradient of dieback intensities and the sampling districts. Here, we specifically defined a rare species only from its frequency of appearance in a given dieback category or district, with occurrence (i.
e. number of independent plots of each relative category in which we found the species) as an index of relative abundance. Indeed, neither temporal rarity nor true abundance or read-based abundance (as counts of individuals or of sequencing reads) could be used with our study design with non-independent monthly replicates and metabarcoding approach. Hence, MOTUs more frequently sampled throughout the plots were defined as more abundant and more common. We therefore generated heatmaps of relative abundance using the phyloseq ver. 1. 30. 0 93 package for R. As rare species can thus be virtually locally abundant in a given dieback category or district by the use of occurrence only to define each MOTU rarity, relative abundance used in heatmaps was generated from 100 randomized bootstraps of the total dataset to virtually increase sampling effort.
Bootstrapped plots were then concatenated to recover a bootstrapped score giving the relative abundance. From this score, we could infer rarity in terms of habitat specificity, with common species occurring across spatial scale and/or dieback gradient, and rare species occurring only in a few plots in a particular district or level of dieback. Each of the three dieback categories (i. e. low, medium and high dieback levels) was rescaled to account for the difference in sample representativeness, as such that each category was depicting one-third of the total dataset. Each MOTU relative abundance was then calculated in percentage from the bootstrapped score for these rescaled categories, so that a MOTU present in all plots of one particular dieback category was present at 33% in the respective category, for a total of 100% across the total dataset if appearing everywhere.
Regarding abundance per district (i. e. Aure valley and Sault plateau, as well as total dataset), MOTUs' relative abundances were calculated in percentage based on the bootstrapped score with no further rescaling per district. Species compositional changes along the total sampling area were assessed by calculating the number of species shared across all 56 experimental plots using zeta diversity (i. e. extension of beta diversity over 'i' zeta order (ζ i ) or plots)  28  using zetadiv R package ver. 1. 2. 0 30. Two models were computed to perform plot comparisons either following a nearest-neighbour (NN) scheme or considering every comparison possible (ALL scheme) to assess for heterogeneity driven by distance. For all zeta-diversity analyses, sample number for site combination was set to 5000, with Monte-Carlo sampling performed for NN and ALL models.
The two models (NN and ALL) were also fitted both to power-law (pl) and exponential (exp) regressions to define whether the signal in our dataset was driven more by ecological niche and rare species turnover rather than by stochasticity, respectively  29. To assess the better fit to a model between pl and exp, we chose the lowest score from Akaike Information Criterion (AIC), which imposes penalty to each model in regards to its number of parameters, and thus favour models satisfying parsimony criterion  94. For both dieback gradient (i. e. low, medium and high dieback categories) and stand types (i. e. healthy, disturbed and salvaged), zeta ratio (i. e. the species retention rate, giving the probability of a common species to be retained in the community across zeta order here referring to plots) was generated for each of the five most represented taxonomic orders and the four different functional groups identified, as well as for the mean zeta ratio of the respective dieback category or stand type on the total dataset.
Zeta ratio calculations were performed with all sites kept for each category, including those without focal taxa. Estimations of environmental variables contribution to insect species assemblage changes were performed with zeta multi-site generalized dissimilarity modelling (zeta. msgdm) in zetadiv R package ver. 1. 2. 0 30 at different zeta order, ranging from 2 to 50. As for zeta-diversity analyses, zeta. msgdm model was run with the number of samples fixed to 5000. After checking for non-collinearity, a total of nine environmental variables were tested with zeta. msgdm: geographic distance to the nearest plot, altitude, canopy openness, total amount of deadwood in m 3 per ha, the basal area per ha, the tree diversity per ha, the density of very large trees (Ø > 67. 5 cm) per ha and both the TreM diversity and density per ha.
Acknowledgements We are grateful to all forest stakeholders-owners and managers-that allowed us to perform fieldwork in their stands. We are thankful to  Laurent Burnel ,  Jérôme Molina ,  Sylvie Ladet ,  Carl Moliard ,  Jérôme Willm ,  Wilfried Heintz ,  Denis Sabadie  and  Benoit Nusillard  for the fieldwork, field and GIS support, as well as to  Kévin Speder  for his help in the molecular lab. We are also thankful to  Yohann Graux  for his help with QGIS mapping and  Rodolphe Rougerie  for his valuable comments on the manuscript. Library preparation for sequencing as well as sequencing on Illumina MiSeq platform was performed at the  Berlin Center for Genomics in Biodiversity Research (BeGenDiv). This research is part of the international project  CLIMTREE "Ecological and Socioeconomic Impacts of Climate-Induced Tree Dieback in Highland Forests " within the  Belmont Forum Call : " Mountains as Sentinels of Change ".
The French team (L. S. , A. B. , B. C. , J. C. , C. B. , L. L. , E. A. H. and C. L. V. ) was funded by the  French National Research Agency (ANR)  ( ANR-15-MASC-002-01 ). L. S. , A. B. , E. A. H. and C. L. V. were also supported by  FEDER InfoBioS  ( EX011185 ). L. S. was partially supported by the  German Academic Exchange Service (DAAD)  ( Short-Term Grant   57440917 ). The German team (P. S. Y. , S. T. , J. M. and M. T. M. ) was funded by the  Deutsche Forschungsgemeinschaft (DFG)  ( MA 7249/1-1 ) and the  Federal Ministry of Education and Research (BMBF)  ( Förderkennzeichen 033W034A ). W. C. and D. W. Y. were supported by the  Strategic Priority Research Program  of the  Chinese Academy of Sciences  ( XDA20050202 ), the  National Natural Science Foundation of China  ( 41661144002-CLIMTREE  grant-,  31670536 ,  31400470 ,  31500305 ), the  Key Research Program of Frontier Sciences, CAS  ( QYZDY-SSW-SMC024 ), the  Bureau of International Cooperation  ( GJHZ1754 ), the  Ministry of Science and Technology of China  ( 2012FY110800 ), the  State Key Laboratory of Genetic Resources and Evolution.
Data availability All datasets used for analyses are publicly available on Zenodo  96  (https://doi. org/10. 5281/ zenodo. 5653307) or at the following GitHub repository: https://github. com/Lucasire/ Malaise_FR_2017. Supplementary Data are publicly available on Figshare  97  at the following https://doi. org/10. 6084/m9. figshare. 16975636. v1. Raw sequencing data are available on NCBI at the following accession number: PRJNA702908.
The Human Reference Atlas (HRA) is defined as a comprehensive, three-dimensional (3D) atlas of all the cells in the healthy human body. It is compiled by an international team of experts that develop standard terminologies linked to 3D reference objects describing anatomical structures. The third HRA release (v1. 2) covers spatial reference data and ontology annotations for 26 organs. Experts access the HRA annotations via spreadsheets and view reference models in 3D editing tools. This paper introduces the Common Coordinate Framework Ontology (CCFO) v2. 0. 1 that interlinks specimen, biological structure, and spatial data together with the CCF API which makes the HRA programmatically accessible and interoperable with Linked Open Data (LOD). We detail how real-world user needs and experimental data guide CCFO design and implementation, present CCFO classes and properties together with examples of their usage, and report on technical validation performed.
The CCFO graph database and API are used in the HuBMAP portal, Virtual Reality Organ Gallery, and other applications that support data queries across multiple, heterogeneous sources. object relationships (e. g. , what object is inside or next to which other object). There exists a crosswalk table that associates 3D object names with the anatomical structure terms in the ASCT+B tables that link to terminology in existing ontologies whenever possible. Fig. 1a  depicts how Visible Human Project data (VHP) 10 (a high-quality, whole-body dataset published by the National Library of Medicine) is used to create reference organs (different anatomical structures of a kidney are shown) and how resulting data is uniformly represented using the spatial and specimen ontology. All reference organs can be interactively explored in the Exploration User Interface (EUI) 11 and the Virtual Reality Organ Gallery 12 , both of which are detailed in the Applications section.
The spatial size, shape, location, and rotation of experimental tissue data is captured relative to the organs in the 3D Reference Object Library. Uniform tissue registration is facilitated by the Registration User Interface (RUI) (Fig. 2b , right). Specimen information. Data capturing the sex, age, and other information on donors that provided tissue data used in the construction of the HRA are tracked. For the HRA (v1. 2), data from the Visible Human Project was used in the construction of the 3D reference organs-the male was white, 180. 3 cm (71 inch) tall, 90 kg (199 pound) and was 38 years old; the female was white, 171. 2 cm (67. 4 inch) tall, obese (weight unknown), and 59 years old. Provenance. We track scholarly publications and experimental studies that provide evidence for entities in the HRA (e.
g. , for what cell types have what gene expressions in which tissues). In addition to data provenance, we capture data processing provenance so the HRA can be recomputed, as needed, when new data and algorithms become available. HRA construction versus usage. Just as with geospatial maps, there is a difference between atlas creation (i. e. , defining the reference space) and atlas usage (e. g. , finding a place in the reference space or a pathway between two locations). Fig. 1  provides an overview of HRA construction and usage by experts (e. g. , anatomists, pathologists, surgeons, single cell researchers). We show real-world entities on the left, standardized data in the middle, and CCF ontologies and value-added services on the right. Fig. 1a  shows HRA construction that includes the creation, validation, and publishing of the CCFO and associated APIs and tools.
It starts with the creation of ASCT+B tables by experts with decades of expertise in surgery, pathology, anatomy, single-cell analysis, consulting textbooks, Wikipedia, and published experimental data. In parallel, professional medical illustrators create 3D spatial reference organs in support of tissue registration (e. g. , the resulting reference organs have anatomical structures that serve as landmarks when registering virtual tissue blocks and can be used to assign semantic tags). Last but not least, crosswalks are compiled that link all 3D reference objects to anatomical structures in the ASCT+B tables (see details in Börner et al. , 2021 4  ). Fig. 1b  details HRA usage-e. g. , the mapping of new experimental tissue data into the HRA so it can be compared across tissue donors, laboratories, assay types or by using the HRA to query for cell types and the genes they express in different locations of the 3D human body.
We show how tissue blocks are cut from human organs (and possibly further cut into tissue sections). The tissue blocks are then spatially registered into 3D reference organs using the Registration User Interface 13 , shown on right (and discussed in the Applications section), that assigns anatomical structure annotations (Uberon IDs and labels) via 3D object collision detection (see papers  14, 15  and the Applications section); Assigned anatomical structures and data in the ASCT+B tables are then used to predict cell types and gene expressions for a tissue block. HRA-predicted data can then be compared to experimental data and the HRA data is revised as needed to capture healthy human diversity. Note that only the highest quality experimental data is used for HRA construction; all CCFregistered data can be explored within the HRA.
Common Coordinate Framework Ontology. The CCF Ontology (CCFO) detailed in this paper formalizes and interlinks major classes that describe biological structure, spatial, and specimen information captured in the HRA. It thus provides a unifying vocabulary for HRA construction and usage-making it possible to ingest external data sources; supporting uniform tissue sample registration that includes the spatial positioning and semantic annotations within the 3D reference organs; and supporting user-formulated cross-domain queries over tissue donor properties, anatomical structures, cell types, biomarkers, and 3D space. The CCFO makes it possible to publish HRA data in the Linked Open Data (LOD) cloud 16 in a Findable, Accessible, Interoperable and Reusable (FAIR) 17 manner; the CCF API supports programmatic access to HRA data.
On June 23, 2022, the HRA had been successfully used across four consortia to register 350 tissue blocks. On September 1, 2022, the count had increased to 364 tissue blocks and these tissue blocks link to 5,000 biomolecular datasets derived from these tissue samples. Human Reference Atlas. To construct the HRA, it is necessary to standardize experimental specimen metadata and to have a common coordinate framework (CCF)  3  that provides a biological structure vocabulary linked to 3D spatial data for every organ, tissue, and cell in the human body  4. To construct the CCF for the HRA, we assume that (1) anatomical structures are composed of cells that have different types (i. e. , cell types are located in anatomical structures), combinations of biomarkers characterize different cell types, and experts are interested in querying for those entities (i.
Datasets for these tissue blocks (e. g. , cell-type name annotations computed using the Azimuth 22 tool or gene-expression values measured for cells in experiments) must be mapped to anatomical structures-so it becomes possible to show cell-type populations (i. e. , the number of cells per type within an anatomical structure) inside of each 3D anatomical structure. For each HRA entity, all data and code needed to recompile it must be tracked to ensure reproducibility. A new HRA is computed over the course of several days using massive storage and computing resources every six months. Upon each successful recompilation, baseline HRA statistics such as entity and relationship counts, celltype populations, or query-performance test results are computed and published. HRA usage. Some users are interested to understand in which 3D spatial locations or in what anatomical structures cells of a certain type (e.
g. , immune cells) can be found or what biomarkers are available or common for a certain cell type (e. g. , are there differences in gene expression values for a certain cell type across organs). Other users would like to compare their very own experimental data to the growing set of HRA compatible data (e. g. , to understand how diseased cell-type populations or biomarker-expression values differ from a healthy reference). A third set of users is interested in transferring cell-type annotations from the HRA to a new experimental dataset (e. g. , using the Azimuth  22  tool that automates the analysis, interpretation of single-cell RNA-seq experiment data and transfers cell annotations from the reference map to new experimental data). If new data is uploaded, it needs to be registered and analyzed at scale; 3D exploration requires that 3D reference organ files are loaded efficiently and spatial and other queries are run fast using specimen, biological structure, and/or spatial metadata.