About this dataset

Context

The dataset tabulates the Dinosaur population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Dinosaur across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Dinosaur was 246, a 1.23% increase year-by-year from 2022. Previously, in 2022, Dinosaur population was 243, an increase of 0.41% compared to a population of 242 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Dinosaur decreased by 68. In this period, the peak population was 339 in the year 2010. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Dinosaur is shown in this column.
• Year on Year Change: This column displays the change in Dinosaur population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Dinosaur Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Dinosaur population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Dinosaur. The dataset can be utilized to understand the population distribution of Dinosaur by age. For example, using this dataset, we can identify the largest age group in Dinosaur.

Key observations

The largest age group in Dinosaur, CO was for the group of age 10 to 14 years years with a population of 151 (24.59%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Dinosaur, CO was the Under 5 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Dinosaur is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Dinosaur total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Dinosaur Population by Age. You can refer the same here

Dataset Card for T-Rex-MC

  Dataset Description

Repository: Paper: https://arxiv.org/abs/2404.12957 Point of Contact:

  Dataset Summary

The T-Rex-MC dataset is designed to assess models’ factual knowledge. It is derived from the T-REx dataset, focusing on relations with at least 500 samples connected to 100 or more unique objects. For each sample, a multiple-choice list is generated, ensuring that instances with multiple correct objects do not include any… See the full description on the dataset page: https://huggingface.co/datasets/QinyuanWu/T-Rex-MC.

This is the best dataset.

Table S1Spreadsheet containing collected data, with literature sources indicated.Supplementary Material S1.csvTable S2Tables with correlation coefficients and p-values for 1) the complete sample of tridactyl dinosaur tracks, 2) theropod tracks, and 3) ornithischian tracks.Supplementary Material S2.pdfSupplementary Material S3Variable loadings of the PCA of the theropod subsample (Fig. 5A)Supplementary Material S4Variable loadings of the PCA of the ornithischian subsample (Fig. 5B)Supplementary Material S5Canonical coefficients of the CVA (Fig. 4A)

Fossilized dinosaur footprints enable us to study the behavior of individual dinosaurs as well as interactions between dinosaurs of the same or different species. There are two principal groups of three-toed dinosaurs, ornithopods and theropods. Determining if a footprint is from an ornithopod or a theropod is a challenging problem. Based on a data set of over 300 dinosaur footprints we train several machine learning models for classifying footprints as either ornithopods or theropods. The data are provided in the form of 20 landmarks for representing each footprint which are derived from images. Variable selection using logistic forward regression demonstrates that the selected landmarks are at locations that are intuitively expected to be especially informative locations, such as the top or the bottom of a footprint. Most models show good accuracy but the recall of ornithopods, of which fewer samples were contained in the data set, was generally lower than the recall of theropods. The Multi-Layer Perceptron (MLP) stands out as the model which did best at dealing with the class imbalance. Finally, we investigate which footprints were misclassified by the majority of models. We find that some misclassified samples exhibit features that are characteristic of the other class or have a compromised shape, for example, a middle toe that points to the left or the right rather than straight ahead.

Despite the increasing importance of molecular sequence data, morphology still makes an important contribution to resolving the phylogeny of many groups, and is the only source of data for most fossils. Most systematists sample morphological characters as broadly as possible on the principle of total evidence. However, it is not uncommon for sampling to be focussed on particular aspects of anatomy, either because characters therein are believed to be more informative, or because preservation biases restrict what is available. Empirically, the optimal trees from partitions of morphological data sets often represent significantly different hypotheses of relationships. Previous work on hard-part versus soft-part characters across animal phyla revealed significant differences in about a half of sampled studies. Similarly, studies of the craniodental versus postcranial characters of vertebrates revealed significantly different trees in about one third of cases, with the highest rates observed in non-avian dinosaurs. We test whether this is a generality here with a much larger sample of 81 published data matrices across all major dinosaur groups. Using the incongruence length difference (ILD) test and two variants of the incongruence relationship difference (IRD) test, we found significant incongruence in about 50% of cases. Incongruence is not uniformly distributed across major dinosaur clades, being highest (63%) in Theropoda and lowest (25%) in Thyreophora. As in previous studies, our partition tests show some sensitivity to matrix dimensions and the amount and distribution of missing entries. Levels of homomplasy and retained synapomorphy are similar between partitions, such that incongruence must partly reflect differences in patterns of homoplasy between partitions, which may itself be a function of modularity and mosaic evolution. Finally, we implement new tests to determine which partition yields trees most similar to those from the entire matrix. Despite no bias across dinosaurs overall, there are striking differences between major groups. The craniodental characters of Ornithischia and the postcranial characters of Saurischia yield trees most similar to the 'total evidence' trees derived from the entire matrix. Trees from these same character partitions also tend to be most stratigraphically congruent: a mutual consilience suggesting that those partitions yield more accurate trees.

Data include high-resolution thin-section images used in analyses in Cullen et al 2020 (Histological dataset for: osteohistological analyses reveal diverse strategies of theropod dinosaur body-size macroevolution). Please see publication for further details of specimens.

The data presented here support the research paper “Geochemistry-Based Provenance Modelling in Fossil Bones: A Machine Learning Study from the Upper Cretaceous Gobi Localities”, intended for a submission to "Cretaceous Research".

The dataset contains trace elements concentrations from fossil dinosaur bones from the Upper Cretaceous Nemegt and Djadokhta formations. For the analytical purposes, the dataset was divided into two subsets: the first one consisting of long bones (tibiae, femora, radii and humeri) and the other including trabecular bones (ribs and vertebrae) and metatarsals. Locality labels were used to train and evaluate several machine learning classifiers (logistic regression, random forest, AdaBoost, XGBoost) to assess the potential of bone geochemistry for provenance prediction. Feature selection was conducted on the best-performing models to identify the elements contributing the most to the model performance. These results were compared with those obtained using Linear Discriminant Analysis.

The data are provided in CSV format in the “Data” folder. The folder “Figures” contains the figures used in the manuscript. The folder “Supplementary files” contains interactive HTML plot ("Element profiles.html") showing the all the concentration profiles across each analysed sample, including the ones measured along several profiles, together with the concentration profiles presented in a PDF file ("All profiles.pdf").

Documenting the patterns and potential associated processes of ancient biotas has always been a central challenge in palaeontology. Over the last decades, intense debate has focused on the organisation of dinosaur–dominated communities, yet no general consensus has been reached on how these communities were organised in a spatial context and if primarily affected by abiotic or biotic agents. Here, we used analytical routines typically applied in metacommunity ecology to provide novel insights into dinosaurian distributions across the latest Cretaceous of North America. To do this, we combined fossil occurrences with functional, phylogenetic and palaeoenvironmental modelling, and adopted the perspective that more reasonable conclusions on palaeoecological reconstructions can be gained from studies that consider the organisation of biotas along ecological gradients at multiple spatial scales. Our results showed that dinosaurs were restricted in range to different parts of the Hell Creek Formation, prompting the recognition of discrete and compartmentalised faunal areas during the Maastrichtian at fine-grained scales, whereas taxa ranges formed quasi–nested groups when combining data from various geological formations across the Western Interior of North America. Although groups of dinosaurs had coincident range boundaries, their communities responded to multiple ecologically–important gradients when compensating for differences in sampling effort. Metacommunity structures of both ornithischians and theropods were correlated with climatic barriers and potential trophic relationships between herbivores and carnivores, thereby suggesting that dinosaurian faunas were shaped by physiological constraints and a combination of bottom-up and top-down forces across multiple spatial grains and extents.

Methods Dinosaur occurrences for the Maastrichtian of North America were retrieved from the Palaeobiology Database

Palaeoclimatic general circulation model. In this study, we used palaeoclimatic model outputs (here, near-surface [1.5 m] mean annual temperature (TempMean), near-surface [1.5 m] annual temperature standard deviation (TempSDann), annual average precipitation (PrecMean) and annual precipitation standard deviation (PrecSDann)) from the fully coupled atmosphere-ocean GCM HadCM3L v. 4.5 Atmospheric–Ocean General Circulation Model (Valdes et al. 2017). More specifically, we followed the nomenclature of Valdes et al. (2017) and applied the HadCM3BL–M2.1aE version of the model. The conditions of the model simulations for the Maastrichtian consist of an atmospheric CO2 concentration of 1120 ppmv, which is within the range of uncertainty provided by the recent proxy pCO2 reconstructions of Foster et al. (2017). The model simulations were run for a total of 1422 years, and the climate variables used in our analyses were an annual average of the last 30 years of these simulations. HadCM3L has contributed to the Coupled Mode Intercomparison Project experiments demonstrating skill when it comes to reproducing present-day climates (Collins et al. 2001; Valdes et al. 2017) and has also been used for an array of different palaeoclimate evaluations during the Eocene (Lunt et al. 2012), the Oligocene (Li et al. 2018) and the Miocene (Bradshaw et al. 2012). Detailed information on this palaeoclimatic model, including large–scale circulation (and associated energy and momentum fluxes) and temporal fluctuations, as well as the impacts of fine-scale orographic features on climate signals, are available elsewhere (e.g. Lunt et al. 2016; Chiarenza et al. 2019).

Palaeogeographical digital elevation models (DEMs). The Maastrichtian palaeogeography for this study is that of Scotese & Wright (2018), which has been compiled as a palaeo-digital elevation model to facilitate grid-based analyses. In brief, these maps were created from publicly available stratigraphic literature, supplemented by fieldwork, including lithology, palaeoenvironmental information and broad-scale facies identification. For large–scale analyses, these palaeogeographies were upscaled to the palaeoclimatic model resolution (3.75° x 2.5°). This means that topographic and bathymetric information was broadly conserved, as it was resolved at a lower resolution (see Chiarenza et al. 2019 for a similar approach).

Functional features. Each dinosaur taxon was classified into several functional guilds based on body mass (very small, small, medium, large and very large), locomotor mode (bipeds, facultative bipeds –capable of both quadrupedal and bipedal motion– and quadrupeds) and trophic habits (carnivores, omnivores and herbivores, and for the latter, low and high browsers).

Body mass is perhaps the single most important and meaningful functional trait for animals, as it ultimately affects many aspects of their biology including metabolic rates, mechanical constraints, ecological performance and lifestyle strategies related to feeding, locomotion and reproduction (Loeuille & Loreau 2006; Iossa et al. 2008). Here, we used body mass estimates (very small ≤ 10 kg; 10 kg < small ≤ 100 kg; 100 kg < medium ≤ 1000 kg; 1000 kg < large ≤ 10000 kg; very large > 10000 kg; Noto & Grossman 2010) based on adult representatives from the comprehensive dataset of Benson et al. (2014), which provides a wide list of dinosaur taxa using the scaling relationship of limb bone robustness (stylopodial circumference; Campione & Evans 2012). To obtain a more comprehensive understanding of body mass distributions in our data, we further applied an inflection point criterion based on the Barry & Hartigan (1993) product partition model with Markov chain Monte Carlo (MCMC). More specifically, this algorithm used the posterior probability of changes over 10000 MCMC iterations, excluding the first 1000 as burn in, to distinguish among different body mass categories in the latest Cretaceous dinosaurs of North America. Interestingly, this Bayesian analysis roughly identified most of the original body mass categories used in our study, with each category broadly representing an order of magnitude (García–Girón et al. 2020b, appendix S1, fig. S1).

Trophic habits refer to the food processing strategies and diet of an animal, and it generally includes three primary categories, i.e. carnivores, herbivores and omnivores. Further subdivisions depend on the biological knowledge of the morphology (e.g. teeth morphology and skull) and behaviour of the study organismal group.

Many inferences on the biology, behaviour and ecology of extinct vertebrates are based on the reconstruction of the musculature and rely considerably on its accuracy. Although the advent of digital reconstruction techniques has facilitated the creation and testing of musculoskeletal hypotheses in recent years, muscle strain capabilities have rarely been considered. Here, a digital modelling approach using the freely available visualization and animation software BLENDER is applied to estimate cranial muscle length changes and optimal and maximal possible gape in different theropod dinosaurs. Models of living archosaur taxa (Alligator mississippiensis, Buteo buteo) were used in an extant phylogenetically bracketed framework to validate the method. Results of this study demonstrate that Tyrannosaurus rex, Allosaurus fragilis and Erlikosaurus andrewsi show distinct differences in the recruitment of the jaw adductor musculature and resulting gape, confirming previous dietary and ecological ...

Dated phylogenies of fossil taxa allow palaeobiologists to estimate the timing of major divergences and placement of extinct lineages, and to test macroevolutionary hypotheses. Recently developed Bayesian ‘tip-dating’ methods simultaneously infer and date the branching relationships among fossil taxa, and infer putative ancestral relationships. Using a previously published dataset for extinct theropod dinosaurs, we contrast the dated relationships inferred by several tip-dating approaches and evaluate potential downstream effects on phylogenetic comparative methods. We also compare tip-dating analyses to maximum-parsimony trees time-scaled via alternative a posteriori approaches including via the probabilistic cal3 method. Among tip-dating analyses, we find opposing but strongly supported relationships, despite similarity in inferred ancestors. Overall, tip-dating methods infer divergence dates often millions (or tens of millions) of years older than the earliest stratigraphic appearance of that clade. Model-comparison analyses of the pattern of body-size evolution found that the support for evolutionary mode can vary across and between tree samples from cal3 and tip-dating approaches. These differences suggest that model and software choice in dating analyses can have a substantial impact on the dated phylogenies obtained and broader evolutionary inferences.

Usage Notes READMEREADME file describes the file type and contents of all files in this data repository.B2noSA_inputFileBEAST2 Input file for noSA analysisB2noSA_majrule_burn03Half-compatibility summary for noSA BEAST2 analysisB2noSA_mcc_burn03Maximum clade credibility summary for noSA BEAST2 analysisB2noSA_treelogPosterior sampled tree file from BEAST2 noSA analysisB2SA_inputFileBEAST2 Input file for SA analysisB2SA_majrule_burn03Half-compatibility summary for SA BEAST2 analysisB2SA_mcc_burn03Maximum clade credibility summary for SA BEAST2 analysisB2SA_treelogPosterior sampled tree file from BEAST2 SA analysisbirdtree_workspace_03-07-2016Saved R Environment from executing RMarkdown script.birdtreecomparison_03-07-16PDF created by executing RMarkdown script.birdtreecomparison_03-07-16RMarkDown script containing all post-inference analyses.figures_theropod_06-17-16R script which, if executed from within the saved R environment, produces all figures in the manuscript and supplemental.makeBEAST2_majRule_02-05-16An R script for obtaining half-compatibility trees with posterior probabilities on the edges from BEAST2 treelogs, albeit without branch lengths.mass_data_for_PCMs_BensonEtal14_10-27-14Datafile of mass estimates for taxa, as obtained from the dataset supplied by Benson et al., 2014.MrBayesSA_100treeSample_burn03The Posterior tree sample from MrBayes analysis.MrBayesSA_input_scriptNEXUS input with MrBayes block for our MrBayes analysis.MrBayesSA_majrule_burn03Half-compatibility summary for the MrBayes analysisMrBayesSA_mcc_burn03Maximum clade compatibility summary for the MrBayes analysistheropod_tree_LeeWorthy2011_RAXML_02-01-16NEXUS file containing an unscaled topology matching the RAXML tree presented in Lee & Worthy, 2011.theropod_tree_XuEtAll2011_FigS6_02-01-16NEXUS file containing an unscaled topology matching the tree presented in Figure S6 of Xu et al., 2011.timeList_sorted_asIs_theropods_DWB_11-05-14A timeList object for these taxa, structured as is necessary for use with paleotree, describing their stratigraphic ranges in discrete intervals.TNT_most_parsimonious_treesSample of 100 most parsimonious trees taken from our TNT analysis.

Code Images

Context

This is a subset of the Zenodo-ML Dinosaur Dataset [Github] that has been converted to small png files and organized in folders by the language so you can jump right in to using machine learning methods that assume image input.

Content

Included are .tar.gz files, each named based on a file extension, and when extracted, will produce a folder of the same name.

 tree -L 1
.
├── c
├── cc
├── cpp
├── cs
├── css
├── csv
├── cxx
├── data
├── f90
├── go
├── html
├── java
├── js
├── json
├── m
├── map
├── md
├── txt
└── xml

And we can peep inside a (somewhat smaller) of the set to see that the subfolders are zenodo identifiers. A zenodo identifier corresponds to a single Github repository, so it means that the png files produced are chunks of code of the extension type from a particular repository.

$ tree map -L 1
map
├── 1001104
├── 1001659
├── 1001793
├── 1008839
├── 1009700
├── 1033697
├── 1034342
...
├── 836482
├── 838329
├── 838961
├── 840877
├── 840881
├── 844050
├── 845960
├── 848163
├── 888395
├── 891478
└── 893858

154 directories, 0 files

Within each folder (zenodo id) the files are prefixed by the zenodo id, followed by the index into the original image set array that is provided with the full dinosaur dataset archive.

$ tree m/891531/ -L 1
m/891531/
├── 891531_0.png
├── 891531_10.png
├── 891531_11.png
├── 891531_12.png
├── 891531_13.png
├── 891531_14.png
├── 891531_15.png
├── 891531_16.png
├── 891531_17.png
├── 891531_18.png
├── 891531_19.png
├── 891531_1.png
├── 891531_20.png
├── 891531_21.png
├── 891531_22.png
├── 891531_23.png
├── 891531_24.png
├── 891531_25.png
├── 891531_26.png
├── 891531_27.png
├── 891531_28.png
├── 891531_29.png
├── 891531_2.png
├── 891531_30.png
├── 891531_3.png
├── 891531_4.png
├── 891531_5.png
├── 891531_6.png
├── 891531_7.png
├── 891531_8.png
└── 891531_9.png

0 directories, 31 files

So what's the difference?

The difference is that these files are organized by extension type, and provided as actual png images. The original data is provided as numpy data frames, and is organized by zenodo ID. Both are useful for different things - this particular version is cool because we can actually see what a code image looks like.

How many images total?

We can count the number of total images:

find "." -type f -name *.png | wc -l
3,026,993

Dataset Curation

The script to create the dataset is provided here. Essentially, we start with the top extensions as identified by this work (excluding actual images files) and then write each 80x80 image to an actual png image, organizing by extension then zenodo id (as shown above).

Saving the Image

I tested a few methods to write the single channel 80x80 data frames as png images, and wound up liking cv2's imwrite function because it would save and then load the exact same content.

import cv2
cv2.imwrite(image_path, image)

Loading the Image

Given the above, it's pretty easy to load an image! Here is an example using scipy, and then for newer Python (if you get a deprecation message) using imageio.

image_path = '/tmp/data1/data/csv/1009185/1009185_0.png'
from imageio import imread

image = imread(image_path)
array([[116, 105, 109, ..., 32, 32, 32],
    [ 48, 44, 48, ..., 32, 32, 32],
    [ 48, 46, 49, ..., 32, 32, 32],
    ..., 
    [ 32, 32, 32, ..., 32, 32, 32],
    [ 32, 32, 32, ..., 32, 32, 32],
    [ 32, 32, 32, ..., 32, 32, 32]], dtype=uint8)


image.shape
(80,80)


# Deprecated
from scipy import misc
misc.imread(image_path)

Image([[116, 105, 109, ..., 32, 32, 32],
    [ 48, 44, 48, ..., 32, 32, 32],
    [ 48, 46, 49, ..., 32, 32, 32],
    ..., 
    [ 32, 32, 32, ..., 32, 32, 32],
    [ 32, 32, 32, ..., 32, 32, 32],
    [ 32, 32, 32, ..., 32, 32, 32]], dtype=uint8)

Remember that the values in the data are characters that have been converted to ordinal. Can you guess what 32 is?

ord(' ')
32

# And thus if you wanted to convert it back...
chr(32)

So how t...

This dataset includes histological thin sections from long bones of six different Alvarezsaurian dinosaurs and labelled primary and secondary osteons.

Methods The dataset was collected based on specimens in the Institute of Vertebrate Paleontology and Paleonanthropology, Beijing, China. Six alvarezsaurian dinosaur taxa are included as following:

    Taxa


    Sampled bones


    Image number




    Bannykus wulantensis


    Fibula


    18




    Haplocheirus sollers


    Tibia


    28




    Qiupanykus zhangi


    Metatarsal


    36




    Shishugounykus inexpectus


    Fibula


    29




    Xixianykus zhangi


    Femur, Metatarsal


    28+22




    Xiyunykus pengi


    Fibula


    13

The image was taken under normal light microscope with resolution of 2576×1936. Labels of primary and secondary were manually labled by all authors.

Abstract: This proposal supports research on the Early Jurassic Hanson Formation vertebrate fauna of the Beardmore Glacier region of Antarctica. The project supports preparation and systematic and paleobiological research on four Antarctic dinosaurs, including two new species, collected in the Central Transantarctic Mountains. With the new material Cryolophosaurus will become one of the most complete Early Jurassic theropods known, and thus has the potential to become a keystone taxon for resolving the debated early evolutionary history of theropod dinosaurs, the group that gave rise to birds. Two new dinosaur specimens include a nearly complete articulated skeleton of a juvenile sauropodomorph, and the articulated hip region of another small individual. Both appear to be new taxa. The dinosaurs from the Hanson Formation represent some of the highest paleolatitude vertebrates known from the Jurassic. The PIs generated CT datasets for Cryolophosaurus and the more complete new sauropodomorph species to mine for phylogenetic trait information, and to investigate their comparative neuroanatomy and feeding behavior. Histological datasets have been generated from multiple skeletal elements for all four Mt. Kirkpatrick taxa to understand patterns of growth in different clades of polar dinosaurs and compare them to relatives from lower paleolatitudes. This paleohistological study of a relatively diverse sample of sauropodomorph taxa from Antarctica may contribute to determining whether and how these dinosaurs responded to contemporary climatic extremes. The PIs have established a successful undergraduate training program as part of previous research. Summer interns from Augustana are trained at the Field Museum in specimen preparation, curation, molding/casting, and histological sampling. They also participate in existing Field Museum REU programs, including a course on phylogenetic systematics. Four undergraduate internships and student research projects will be supported through this proposal. A postdoctoral researcher has also been supported on this project The PIs are developing a traveling exhibit on Antarctic Dinosaurs that they estimate will be seen by over 2 million people over the five-year tour (opening June 2018 at the Field Museum of Natural History).

Estimating past biodiversity using the fossil record is a central goal of palaeobiology. Because raw estimates of biodiversity are biased by variation in sampling intensity across time, space, environments, and taxonomic groups, sampling standardisation is routinely applied when estimating taxonomic diversity (e.g., species richness). However, sampling standardisation is less commonly used when estimating alternative currencies of biological diversity, such as morphological disparity. Here, we show the effects of standardising fossil time series of morphological disparity to equal sample completeness, or “coverage” of the underlying taxon-frequency distribution. We apply coverage-based standardisation to three published datasets of discrete morphological characters (echinoderms, ichthyosaurs, and ornithischian dinosaurs), and quantify disparity using two metrics: weighted mean pairwise dissimilarity (WMPD) and the sum of variance (SOV). We also compare the effects of coverage-based and sample-size-based standardisation. Our results show that coverage standardisation can yield estimates of disparity through time that dramatically deviate from raw estimates, both in magnitude and direction of changes. These findings demonstrate that future studies of morphological disparity should control for variation in sampling intensity to make more reliable inferences.

Abstract: This proposal requests support for research on Early Jurassic vertebrate fauna of the Beardmore Glacier region of Antarctica. The project will support preparation and systematic and paleobiological research on four Antarctic dinosaurs, including two new species, collected in the Central Transantarctic Mountains. With the new material Cryolophosaurus will become one of the most complete Early Jurassic theropods known, and thus has the potential to become a keystone taxon for resolving the debated early evolutionary history of theropod dinosaurs, the group that gave rise to birds. Two new dinosaur specimens include a nearly complete articulated skeleton of a juvenile sauropodomorph, and the articulated hip region of another small individual. Both appear to be new taxa. The dinosaurs from the Hanson Formation represent some of the highest paleolatitude vertebrates known from the Jurassic. The PIs will generate CT datasets for Cryolophosaurus and the more complete new sauropodomorph species to mine for phylogenetic trait information, and to investigate their comparative neuroanatomy and feeding behavior. Histological datasets will be generated from multiple skeletal elements for all four Mt. Kirkpatrick taxa to understand patterns of growth in different clades of polar dinosaurs and compare them to relatives from lower paleolatitudes. This paleohistological study of a relatively diverse sample of sauropodomorph taxa from Antarctica may contribute to determining whether and how these dinosaurs responded to contemporary climatic extremes.

The PIs have established a successful undergraduate training program as part of previous research. Summer interns from Augustana are trained at the Field Museum in specimen preparation, curation, molding/casting, and histological sampling. They also participate in existing Field Museum REU programs, including a course on phylogenetic systematics. Four undergraduate internships and student research projects will be supported through this proposal. The PIs will develop a traveling exhibit on Antarctic Mesozoic paleontology that they estimate will be seen by 2.5 million people over the five-year tour.

Branch lengths—measured in character changes—are an essential requirement of clock-based divergence estimation, regardless of whether the fossil calibrations used represent nodes or tips. However, a separate set of divergence time approaches are typically used to date palaeontological trees, which may lack such branch lengths. Among these methods, sophisticated probabilistic approaches have recently emerged, in contrast with simpler algorithms relying on minimum node ages. Here, using a novel phylogenetic hypothesis for Mesozoic dinosaurs, we apply two such approaches to estimate divergence times for: (i) Dinosauria, (ii) Avialae (the earliest birds) and (iii) Neornithes (crown birds). We find: (i) the plausibility of a Permian origin for dinosaurs to be dependent on whether Nyasasaurus is the oldest dinosaur, (ii) a Middle to Late Jurassic origin of avian flight regardless of whether Archaeopteryx or Aurornis is considered the first bird and (iii) a Late Cretaceous origin for Neornithes that is broadly congruent with other node- and tip-dating estimates. Demonstrating the feasibility of probabilistic time-scaling further opens up divergence estimation to the rich histories of extinct biodiversity in the fossil record, even in the absence of detailed character data.

Usage Notes dinosaur_MRP.tntThe MRP matrix submitted to TNT (Goloboff et al. 2008) for performing tree searches; in Hennig86/TNT format.dinosaur_tnt_1000_mpts.tre.zipThe 1,000 MPTs returned from the TNT tree searches; in Newick format (and zipped).dinosaur_str_1000_mpts.tre.zipThe 1,000 MPTs returned from the TNT tree searches with STR taxa reinserted; in Newick format (and zipped).dinosaur_dating_100_mpts.treThe 100 trees randomly sampled and taken forward for dating; in Newick format.consensus.treStrict consensus of the 1,000 MPTs of dinosaur_str_1000_mpts.tre.zip in Newick format.dinosaur_occurrences.xlsxThe dinosaur occurrences used for dating; in MS Excel format.dinosaur_occurrences.txtThe dinosaur occurrences used for dating; in plain text format.dinosaur_tipages.txtThe tip ages (hard lower bounds) used for dating; in plain text format.dinosaur_timelist.txtThe time list data used to generate the three rates used in the cal3 function; in plain text format.cal3_rates_code.RThe R code used to generate the three rates used in the cal3 dating code; in R code format.cal3_rates_code_11-11-16.Rcal3_dating_code.RThe R code used to perform the cal3 dating; in R code format.Hedman_code.RThe R code used to perform the Hedman dating; in R code format.Hedman_code.rcal3_dates.txtThe cal3 dates output from R; in plain text format.Hedman_dates.txtThe Hedman dates output from R; in plain text format.

1.The analysis of morphological diversity frequently relies on the use of multivariate methods for characterizing biological shape. However, many of these methods are intolerant of missing data, which can limit the use of rare taxa and hinder the study of broad patterns of ecological diversity and morphological evolution. This study applied a mutli-dataset approach to compare variation in missing data estimation and its effect on geometric morphometric analysis across taxonomically-variable groups, landmark position and sample sizes. 2.Missing morphometric landmark data was simulated from five real, complete datasets, including modern fish, primates and extinct theropod dinosaurs. Missing landmarks were then estimated using several standard approaches and a geometric-morphometric-specific method. The accuracy of missing data estimation was determined for each estimation method, landmark position, and morphological dataset. Procrustes superimposition was used to compare the eigenvectors and principal component scores of a geometric morphometric analysis of the original landmark data, to datasets with A) missing values estimated, or B) simulated incomplete specimens excluded, for varying levels of specimens incompleteness and sample sizes. 3.Standard estimation techniques were more reliable estimators and had lower impacts on morphometric analysis compared to a geometric-morphometric-specific estimator. For most datasets and estimation techniques, estimating missing data produced a better fit to the structure of the original data than exclusion of incomplete specimens, and this was maintained even at considerably reduced sample sizes. The impact of missing data on geometric morphometric analysis was disproportionately affected by the most fragmentary specimens. 4.Missing data estimation was influenced by variability of specific anatomical features, and may be improved by a better understanding of shape variation present in a dataset. Our results suggest that the inclusion of incomplete specimens through the use of effective missing data estimators better reflects the patterns of shape variation within a dataset than using only complete specimens, however the effectiveness of missing data estimation can be maximized by excluding only the most incomplete specimens. It is advised that missing data estimators be evaluated for each dataset and landmark independently, as the effectiveness of estimators can vary strongly and unpredictably between different taxa and structures.

The goal of this project is to develop an integrated Cyber Physical System (CPS) for early plant disease detection and control based on precise pathogen identification in transplant greenhouses. The system consists of three components: 1. a robotic platform to collect images and leaf DNA samples with possible pathogen infection. 2. a microfluidic device for on-board DNA sample extraction of all microbes, including the putative pathogens, and identification of pathogen by nanopore sequencing in near real-time. 3. a method of detection of disease spread using imaging and sequencing data, and to determine optimal control strategies. This dataset include tomato plants RGB images and depth data for 3D reconstruction using a robotic platform called Mini T-REX developed for this project.Who: Data were generated by a GRA from Virginia TechWhat: Multi-view RGBD images and longitudinal RGB images of plants (tomatoes, lettuce, and pennycress).Where: The experiments were conducted at a growth beds at Virginia Tech CRC with Mini T-REX.Why: To automate plant phenotyping, monitor plant growth, and collect data for 3D reconstruction and analysis. The goal is to develop affordable and readily available automation options for plant research.How: Using custom-built (Mini T-REX) and commercially available gantry-like robotic systems equipped with cameras and sensors.Experiment Setting:Location: The plants were grown in a controlled environment setting.Influential climatic conditions: Light cycles were controlled, with growth lights mimicking sunlight cycles (16 hours of light). Light intensity could be adjusted based on the crop species.Processing Methods and Equipment:Mini T-REX: Intel RealSense L515 LiDAR was used to capture RGB and depth (RGBD) images. SolidWorks and SW2URDF tool were used for CAD design and creating URDF files. ROS2 and MoveIt2 were used for motion planning and control.•Study Date(s) and Duration:The data for Mini T-REX was gathered over a span of 30 days in fall 2024The interval of image capturing was 4 hours every day.Study Spatial Scale:Mini T-REX: The gantry footprint is 1650mm x 1650mm. The manipulator has a working radius of 280mm.Level of True Replication:The study involved multiple plants (tomatoes).Sampling Precision:Mini T-REX: Captured multi-view images from 6 predefined poses for each plant.Level of Subsampling:Mini T-REX: RGBD images were captured from 6 different poses.File Descriptions:These files include time stamped images captured during the experimental process.