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Fig. 11 in UCE Phylogenomics Resolves Major Relationships Among Ectaheteromorph Ants (Hymenoptera: Formicidae: Ectatomminae, Heteroponerinae): A New Classification For the Subfamilies and the Description of a New Genus

Fig. 11. Lateral view of propodeum, showing: A) Propodeal spiracle separated from declivity margin by a distance longer than its diameter (Typhlomyrmex lavra); B) Propodeal spiracle close to the declivous face of propodeum (Holcoponera relicta—USNMENT00412058). Photos by Gabriela Camacho (A) and Jeffrey Sosa-Calvo; available from www.antweb.org (Antweb 2021).

Fig. 10 in UCE Phylogenomics Resolves Major Relationships Among Ectaheteromorph Ants (Hymenoptera: Formicidae: Ectatomminae, Heteroponerinae): A New Classification For the Subfamilies and the Description of a New Genus

Fig. 10. Lateral view of gaster, showing: A) Second gastric segment (IV abdominal) relatively straight (Gnamptogenys acuminata—USNMENT00441095); B) Second gastric segment (IV abdominal) slightly arched ventrally (Poneracantha mecotyle—CASENT0281530). Photos by Jeffrey Sosa-Calvo (A) and Zach Lieberman (B); available from www.antweb.org (Antweb 2021).

Fig. 8 in UCE Phylogenomics Resolves Major Relationships Among Ectaheteromorph Ants (Hymenoptera: Formicidae: Ectatomminae, Heteroponerinae): A New Classification For the Subfamilies and the Description of a New Genus

Fig. 8. Lateral view of gaster, showing: A) Second gastral (IV abdominal) sternite not strongly reduced in relation to the tergite; dorsal profile of gaster gently convex, so that the apex of gaster is only discretely directed ventrally (Gnamptogenys acuminata—USNMENT00441095); B) Second gastral (IV abdominal) sternite strongly reduced in relation to the tergite; dorsal profile of gaster extremely convex, so that the gaster is strongly directed ventrally and anterad (Alfaria minuta—CASENT0281213). Photos by Jeffrey Sosa-Calvo (A) and Estella Ortega (B); available from www.antweb.org (Antweb 2021).

Fig. 5 in UCE Phylogenomics Resolves Major Relationships Among Ectaheteromorph Ants (Hymenoptera: Formicidae: Ectatomminae, Heteroponerinae): A New Classification For the Subfamilies and the Description of a New Genus

Fig. 5. Lateral view of pronotum, showing: A) Pronotal tubercles present; mesonotum prominent, separated from propodeum by a deep transversal suture (Ectatomma tuberculatum—CASENT0173380); B) Pronotal tubercles or projections absent; mesonotum not prominent, forming a continuous profile with propodeum (Holcoponera striatula—CASENT0173386). Photos by April Nobile; available from www.antweb.org (Antweb 2021).

Fig. 7 in UCE Phylogenomics Resolves Major Relationships Among Ectaheteromorph Ants (Hymenoptera: Formicidae: Ectatomminae, Heteroponerinae): A New Classification For the Subfamilies and the Description of a New Genus

Fig. 7. Frontal view of head, showing: A) Expanded frontal lobes (Alfaria falcifera—CASENT0179971); B) Occipital lobes absent (Gnamptogenys continua— CASENT0173383). Photos by Erin Prado (A) and April Nobile (B); available from www.antweb.org (Antweb 2021).

Fig. 1 in UCE Phylogenomics Resolves Major Relationships Among Ectaheteromorph Ants (Hymenoptera: Formicidae: Ectatomminae, Heteroponerinae): A New Classification For the Subfamilies and the Description of a New Genus

Fig. 1. In lateral view, workers of the Ectatomminae genera, showing the morphological diversity within the clade. (A) Acanthoponera mucronata (CASENT0173540), (B) Alfaria minuta (CASENT0281213), (C) Ectatomma planidens (CASENT0173379), (D) Gnamptogenys acuminata (USNMENT00441095), (E) Heteroponera panamensis (CASENT0106021), (F) Holcoponera ammophila (CASENT0281512), (G) Poneracantha mecotyle (CASENT0281530), (H) Rhytidoponera metallica (CASENT0172345), (I) Stictoponera biroi (CASENT0172380), (J) Typhlomyrmex rogenhoferi (CASENT0173390). See Fig. 3 for images of Boltonia microps. Images by April Nobile, Estella Ortega, Michael Branstetter, Zach Lieberman, and Jeffrey Sosa-Calvo; available from www.antweb.org (Antweb 2021).

Fig. 3 in UCE Phylogenomics Resolves Major Relationships Among Ectaheteromorph Ants (Hymenoptera: Formicidae: Ectatomminae, Heteroponerinae): A New Classification For the Subfamilies and the Description of a New Genus

Fig. 3. Worker of Boltonia microps in A) frontal view; B) dorsal view; and C) lateral view. Images by April Nobile (CASENT0173544); available from www.antweb. org (Antweb 2021).

Fig. 2 in UCE Phylogenomics Resolves Major Relationships Among Ectaheteromorph Ants (Hymenoptera: Formicidae: Ectatomminae, Heteroponerinae): A New Classification For the Subfamilies and the Description of a New Genus

Fig. 2. Phylogeny of the subfamily Ectatomminae based on phylogenomic analyses of the UCE 90% complete data set (150 taxa). Figure is based on IQ-Tree besttree searches with ultrafast bootstrap (UFB) frequencies of less than 100% mapped onto the respective nodes. UFB searches consisted of 1000 replicates.The eleven larger ectatommine lineages are indicated. Branch color indicates the biogeographical range of the species.Taxa marked with asterisk (*) were classified in Gnamptogenys prior to this revision and those with double asterisk (**) were included in Heteroponera prior to this revision. See Supplementary material for the 75% complete matrix (Supp Fig. S1 [online only]). Ant photos show heads in frontal view of, from top to bottom:Gnamptogenys acuminata (USNMENT00441095), Typhlomyrmex rogenhoferi (CASENT0004700), Holcoponera striatula (CASENT0106042), Alfaria simulans (CASENT0603729), Poneracantha rastrata (CASENT0281223), Stictoponera biroi (CASENT0281519), Rythidoponera metallica (CASENT0172345), Ectatomma lugens (USNMENT00445341), Heteroponera brounii (CASENT0172105), Acanthoponera mucronata (CASENT0173540), and Boltonia microps (CASENT0173544). Images by April Nobile, Jeffrey Sosa-Calvo, Zach Lieberman,Will Ericson, Michael Branstetter, and Estella Ortega; available from www.antweb.org (Antweb 2021).

Fig. 4 in UCE Phylogenomics Resolves Major Relationships Among Ectaheteromorph Ants (Hymenoptera: Formicidae: Ectatomminae, Heteroponerinae): A New Classification For the Subfamilies and the Description of a New Genus

Fig. 4. Dorsal view of head, showing: A) Cephalic median longitudinal carina present, extending from the anterior clypeal margin to the vertex (Acanthoponera minor—CASENT0178699); B) Cephalic median longitudinal carina not extending from the anterior clypeal margin to the vertex (Ectatomma tuberculatum— CASENT0173380); C) Cephalic median longitudinal carina absent (Holcoponera striatula—CASENT0173386). Photos by April Nobile; available from www. antweb.org (Antweb 2021).

Fig. 9 in UCE Phylogenomics Resolves Major Relationships Among Ectaheteromorph Ants (Hymenoptera: Formicidae: Ectatomminae, Heteroponerinae): A New Classification For the Subfamilies and the Description of a New Genus

Fig. 9. Dorsal view of mesosoma, showing: A) Promesonotal suture absent (Gnamptogenys acuminata—USNMENT00441095); B) Promesonotal suture feeble, never interrupting dorsal mesosomal sculpture (Poneracantha banksi—INBIOCRI001281007); C) Promesonotal suture well marked, totally interrupting dorsal mesosomal sculpture (Holcoponera moelleri—CASENT0173384). Photos by Jeffrey Sosa-Calvo (A), Estella Ortega (B), and April Nobile (C); available from www. antweb.org (Antweb 2021).

Fig. 6 in UCE Phylogenomics Resolves Major Relationships Among Ectaheteromorph Ants (Hymenoptera: Formicidae: Ectatomminae, Heteroponerinae): A New Classification For the Subfamilies and the Description of a New Genus

Fig. 6. Dorsal view of pronotum, showing: A) Pronotum and mesonotum separated by a distinct suture (Rhytidoponera abdominalis—CASENT0281333); B) Pronotum and mesonotum continuous with a discrete groove (Gnamptogenys stellae—CASENT0281227). Photos by Cerise Chen (A) and Estella Ortega (B) available from www.antweb.org (Antweb 2021).

mDRONES4rivers-project: Portfolios of classification results, UAV and gyrocopter data of project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River, Germany

Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project &bdquo;Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany“ (mDRONES4rivers) in cooperation of the German Federal Institute of Hydrology (BfG), Geocoptix GmbH, Hochschule Koblenz und JB Hyperspectral Devices.  Within the project period (2019-2022) data was collected at different sites situated in Germany along the Rivers Rhine and Oder. All published data produced within the project can be found by searching for the keyword ‘mDRONES4rivers‘.  In this dataset, the following portfolios of classifications, UAS and gyrocopter data of project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River are available for download: •             Multispectral orthophotos produced with the aid of UAS (PDF, Detailed description of sensors and data acquisition procedure; abbreviation: MS_ORTHO) •             RGB-orthophotos and digital surface models produced with the aid of UAS (PDF, Detailed description of sensors and data acquisition procedure; abbreviation: PH_SR_ORTHO_DSM) •             Multispectral orthophotos and Digital Surface Models produced with the aid of a gyrocopter (PDF, Detailed description of sensors and data acquisition procedure; abbreviation: PANX_ORTHO_DSM) •             Classification results based on UAV- and a gyrocopter data (PDF, Detailed description of processing procedure for different classification levels; abbreviation: CLASSIF_PROD) •             German translated version of all above mentioned product portfolios (PDF, abbreviation: product_portfolio_collection_ger)

mDRONES4rivers-project: Classification results based on UAV data of project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River, Germany

Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project &bdquo;Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany“ (mDRONES4rivers) in cooperation of the German Federal Institute of Hydrology (BfG), Geocoptix GmbH, Hochschule Koblenz und JB Hyperspectral Devices.  Within the project period (2019-2022) an object oriented image classification was conducted based on UAV and gyrocopter data for different sites situated in Germany along the Rivers Rhine and Oder. All published data produced within the project can be found by searching for the keyword ‘mDRONES4rivers‘.  In this dataset, the following classification results and metadata of the project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River, Germany is available for download: •             Basic & Vegetation Classification (ESRI Shapefile; abbreviation: lvl2_vegetation_units) •             Classification of dominant stands (ESRI Shapefile; abbreviation: lvl4_dominant_stands ) •             Classification of substrat types (ESRI Shapefile; abbreviation: lvl4_substrate_types) •             associated reports (PDF; statistical and additional information on the classifiaction results and workflow) The above-mentioned files are provided for download as dataset stored in one directory per projekt site and season (e.g. mDRONES4rivers_Niederwerth_2019_03_Summer_Classification.zip = projectname_projectsite_year_no.season_name.season_product). To provide an overview of all files and general background information plus data preview the following files are additionally provided:  •             Portfolios (PDF, Detailed description of classification products and classification workflow, 1x for basic surface types, 1x for classification of vegetation units, 1x for classification of dominant stands,  1x for classification of substrate types) •            Color Coding table for the visualization of the classifiaction units (.xlsx)

Gravity Spy Volunteer Classifications of LIGO Glitches from Observing Runs O1, O2, O3a, and O3b

This dataset contains machine learning and volunteer classifications from the <a href="https://www.zooniverse.org/projects/zooniverse/gravity-spy">Gravity Spy project</a>. It includes glitches from observing runs <a href="https://doi.org/10.7935/K57P8W9D">O1</a>, <a href="https://doi.org/10.7935/CA75-FM95">O2</a>, <a href="https://doi.org/10.7935/nfnt-hm34">O3a</a> and <a href="https://doi.org/10.7935/pr1e-j706">O3b</a> that received at least one classification from a registered volunteer in the project. It also indicates glitches that are nominally retired from the project using our default set of retirement parameters, which are described below. See more details in the <a href="https://ui.adsabs.harvard.edu/abs/2017CQGra..34f4003Z/abstract">Gravity Spy Methods paper</a>.  When a particular subject in a citizen science project (in this case, glitches from the LIGO datastream) is deemed to be classified sufficiently it is "retired" from the project. For the Gravity Spy project, retirement depends on a combination of both volunteer and machine learning classifications, and a number of parameterizations affect how quickly glitches get retired. For this dataset, we use a default set of retirement parameters, the most important of which are:  <ol> <li>A glitches must be classified by at least 2 registered volunteers</li> <li>Based on both the initial machine learning classification and volunteer classifications, the glitch has more than a 90% probability of residing in a particular class</li> <li>Each volunteer classification (weighted by that volunteer's confusion matrix) contains a weight equal to the initial machine learning score when determining the final probability</li> </ol> The choice of these and other parameterization will affect the accuracy of the retired dataset as well as the number of glitches that are retired, and will be explored in detail in an upcoming publication (Zevin et al. in prep).  The dataset can be read in using e.g. Pandas:  ``` import pandas as pd dataset = pd.read_hdf('<a href="https://zenodo.org/api/files/512bfa79-dfbc-4af0-b563-9fdd06edcb16/retired_fulldata_min2_max50_ret0p9.hdf5?versionId=7f568823-76d9-4452-8553-c1eee5993f81">retired_fulldata_min2_max50_ret0p9.hdf5</a>', key='image_db') ``` Each row in the dataframe contains information about a particular glitch in the Gravity Spy dataset.  Description of series in dataframe <ul> <li>['1080Lines', '1400Ripples', 'Air_Compressor', 'Blip', 'Chirp', 'Extremely_Loud', 'Helix', 'Koi_Fish', 'Light_Modulation', 'Low_Frequency_Burst', 'Low_Frequency_Lines', 'No_Glitch', 'None_of_the_Above', 'Paired_Doves', 'Power_Line', 'Repeating_Blips', 'Scattered_Light', 'Scratchy', 'Tomte', 'Violin_Mode', 'Wandering_Line', 'Whistle'] <ul> <li>Machine learning scores for each glitch class in the trained model, which for a particular glitch will sum to unity</li> </ul> </li> <li>['ml_confidence', 'ml_label'] <ul> <li>Highest machine learning confidence score across all classes for a particular glitch, and the class associated with this score</li> </ul> </li> <li>['gravityspy_id', 'id'] <ul> <li>Unique identified for each glitch on the Zooniverse platform ('gravityspy_id') and in the Gravity Spy project ('id'), which can be used to link a particular glitch to the <a href="https://zenodo.org/record/5649212#.YfLNjVjMLzc">full Gravity Spy dataset</a> (which contains GPS times among many other descriptors)</li> </ul> </li> <li>['retired'] <ul> <li>Marks whether the glitch is retired using our default set of retirement parameters (1=retired, 0=not retired)</li> </ul> </li> <li>['Nclassifications'] <ul> <li>The total number of classifications performed by registered volunteers on this glitch</li> </ul> </li> <li>['final_score', 'final_label'] <ul> <li>The final score (weighted combination of machine learning and volunteer classifications) and the most probable type of glitch</li> </ul> </li> <li>['tracks'] <ul> <li>Array of classification weights that were added to each glitch category due to each volunteer's classification</li> </ul> </li> </ul>   ``` For machine learning classifications on all glitches in O1, O2, O3a, and O3b, please see <a href="https://zenodo.org/record/5649212#.YfLNjVjMLzc">Gravity Spy Machine Learning Classifications</a> on Zenodo For the most recently uploaded training set used in Gravity Spy machine learning algorithms, please see <a href="https://zenodo.org/record/1486046#.YZfcar3MJqs">Gravity Spy Training Set</a> on Zenodo. For detailed information on the training set used for the original Gravity Spy machine learning paper, please see <a href="https://zenodo.org/record/1476156#.YZfchL3MJqs">Machine learning for Gravity Spy: Glitch classification and dataset</a> on Zenodo.

Classification of ChemCam LIBS targets from Glen Torridon, Gale crater, Mars

Point-by-point classification of all ChemCam LIBS targets in the Glen Torridon region (sols 2301 to 3007). The targets from the Greenheugh pediment are included for completeness, even though they are not studied in detail here (see Bedford et al., this issue). Columns 5 to 9 contain the geographic and stratigraphic locations of the targets. Columns 10 to 12 contain descriptions of the material that was sampled by the laser at each point, based on visual inspection of rover imagery. Column 13 indicates the quality of the data with respect to focus, based on the examination of the focus curves returned by the instrument each time an autofocus is performed (Peret et al., 2016): “poor” corresponds to a flat or noisy curve; “suboptimal” corresponds to a curve with a maximum at the edge of the range of distances scanned by the autofocus; and “uncertain” corresponds to points for which no focus curve is available, but that are likely out-of-focus given the local target topography visible in the RMI images of the target. Column 14 contains the spacecraft clock, which is unique to each row and enables identification and download of the associated spectra from the Planetary Data System (<a href="http://pds-geosciences.wustl.edu/missions/msl/">http://pds-geosciences.wustl.edu/missions/msl/</a>). Columns 15 to 42 contain the major-element oxide composition of each point, except for targets with poor focus or very high FeOT (e.g., iron meteorites), or located beyond 6 meters. Abundances are in wt%. The RMSEP (root mean squared error of prediction) reflects the model accuracy as detailed in Clegg et al. (2017). The “shots stdev” reflects the standard deviation across the laser shots (excluding the first 5). Columns 45 to 49 contain the corrected abundances for SiO2, Al2O3, Na2O and K2O, as well as the corrected sum of oxides, for targets beyond 3.5 m (Wiens et al., 2021). Column 50 contains the calculated value of the Chemical Index of Alteration. Abbreviations used: BH = Bloodstone hill; CB = Central butte; CBU = clay-bearing unit; LT = lateral traverse; MA = Mary Anning; TB = Tower butte; WB = Western butte.

Volunteer classifications of images from the Cropland Capture game

Each entry represents a single classification of a single image by a volunteer rater. The dataset contains six columns: imgid:     The unique identifier for each image used in the Cropland Capture campaign userid:    The unique identifier for each volunteer in the Cropland Capture campaign rating:    The answer provided; can be only one of the following:          1: yes cropland          2: no cropland          0: maybe  date:        Timestamp of the rating ratingid:    The unique identifier of the rating (this is different for each data row) platform:    What interface did the volunteer use to provide this rating?          1: iPhone5          2: iPhone, other models          3: iPad          4: Browser           >100: Android; different numbers indicate the screen size in pixels  For more information, please see the following publications: Salk, CF, T Sturn, L See, S Fritz (2017).  Limitations of majority agreement in crowdsourced image interpretation. Transactions in GIS, 21: 207–223. Salk, CF, T Sturn, L See, S  Fritz (2016).  Local knowledge and professional background have a minimal impact on volunteer citizen science performance in a land-cover classification task.  Remote Sensing, 8: 744. Salk, CF, T Sturn, L See, S Fritz and C Perger (2016).  Assessing quality of volunteer crowdsourcing contributions: Lessons from the Cropland Capture game.  International Journal of Digital Earth, 9(4): 410-426.

FIG. 15 in The Roman classification and nomenclature of aquatic animals: an annotated checklist (with a focus on ethnobiology)

FIG. 15. — Emys orbicularis Linnaeus, 1758 (young). Both the colour and the long pointed tail may explain the Roman name mus (lit. "mouse"). Photo credit: Katya (CC BY-SA 2.0).

FIG. 11 in The Roman classification and nomenclature of aquatic animals: an annotated checklist (with a focus on ethnobiology)

FIG. 11. — Some of the most popular ostrea (externally shelled molluscs). A, a date mussel (balanus); B, a mussel (musculus – also myax or mitulus); C, a scallop (pectunculus); D, a spiny dye murex (murex or purpura). Detail from a Roman mosaic from Pompeii, 1st century CE (Museo Archeologico Nazionale, Napoli; photo credit: A. Guasparri).

FIG. 7 in The Roman classification and nomenclature of aquatic animals: an annotated checklist (with a focus on ethnobiology)

FIG. 7. — Two mollia (cephalopods), i.e. a sepia (bottom, left) and a polypus (center). Detail from a mosaic in Herculaneum (female thermae floor), Ist century CE. Photo credit: A. Guasparri.

FIG. 10 in The Roman classification and nomenclature of aquatic animals: an annotated checklist (with a focus on ethnobiology)

FIG. 10. — The Roman folk-taxonomy of conchylium2 (i.e. mostly, our externally shelled molluscs). Abbreviations: LF, life-form; LF1+, sublife-form exceeding LF level by one more level; LF2+, sublife-form exceeding LF level by two more levels; LF3+, sublife-form exceeding LF level by three more levels; INT, intermediate;FG, folkgeneric; FS, folk-specific. Symbols: *, prototypical; /, synonymy; (…), ethnotaxonomic ascription only presumed, due to lack of explicit statements in the sources; + (superscript), multiple ethnotaxonomic ascription due to different statements in the sources;?, presumed folk taxon. See each entry in Appendix 1 for details.