5. Leslie A.B. & Mander L. (2023) Quantifying the complexity of plant reproductive structures reveals a history of morphological and functional integration, Dryad, Dataset: https://doi.org/10.5061/dryad.n02v6wx36
This dataset contains most of the data and character scorings used in the analyses reported in Leslie and Mander (2023), and includes adjacency matrix files representing interactions among the part types of each reproductive structure investigated in this paper, python scripts for analyzing them, and an additional readme file for running the analyses.
4. Mander L., Hang H., Bauer M. & Mio W. (2021) Living and fossil Ginkgo leaves, Dryad, Dataset, https://doi.org/10.5061/dryad.7h44j0zsj
This dataset is a collection of images of living and fossil Ginkgo leaves. Mature and fully expanded leaves were harvested from a reproductively immature Ginkgo biloba tree growing in partial shade as a specimen on the campus of The Open University, UK. A total of 468 leaves from a mixture of short-shoots and long-shoots were collected from the specimen. Twenty-two fossil leaves produced by evolutionary relatives of living Ginkgo biloba were extracted from the collections of the Natural History Museum in London, and two fossil leaves were extracted from the geology collections of the School of Environment, Earth and Ecosystem Sciences, The Open University.
3. Mander L., Parins-Fukuchi, C., Dick, C.W., Punyasena S.W. & Jaramillo C. (2020) Phylogenetic and ecological correlates of pollen morphological diversity in a neotropical rainforest, Dryad, Dataset, https://doi.org/10.5061/dryad.59zw3r25b
This dataset contains information on the pollination biology of plants growing on Barro Colorado Island (BCI) derived from the published literature, together with descriptions of the morphology of the pollen grains produced by these plants. Each plant in the dataset has been into one of three groups defined by pollination biology: abiotic pollination, mixed pollination and biotic pollination. The dataset also includes a Python script to measure pairwise morphological distances between the pollen grains in each pollination group and to randomly sub-sample each pollination group in order to account for sample size.
2. Mander L., Dekker S.C., Li M., Mio W., Punyasena S.W. & Lenton T.M. (2017) Data from: A morphometric analysis of vegetation patterns in dryland ecosystems, Dryad, Dataset, https://doi.org/10.5061/dryad.5d19r
This is a dataset of images of patterned vegetation produced by a computational model. These images are: (1) raw model output images, (2) images that have been cropped to 50x50 pixels, and (3) binary images derived from these cropped images. These images are all in .tif format. The images show patterns produced at 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3 and 1.4 mm per day.
1. Electron micrographs of modern grass pollen housed in the University of Illinois IDEALS repository. There are seven items in this repository that correspond to Datasets S1, S2 and S3 in the article Mander L., Li M., Mio W., Fowlkes C.C. & Punyasena, S.W. (2013) Classification of grass pollen through the quantitative analysis of surface ornamentation and texture. Proceedings of the Royal Society Series B, 280, 20131905.
Dataset S1 consists of raw SEM images and metadata from every specimen at x2,000, x6,000 and x12,000 magnification as provided by the instrument (a JEOL JSM-6060LV SEM running at 15kV). Dataset S1 has been split into three parts corresponding to:
Pooideae (Part 1) http://hdl.handle.net/2142/43359
Chloridoideae (Part 2) http://hdl.handle.net/2142/43362
Panicoideae (Part 3) http://hdl.handle.net/2142/43364
Dataset S2 consists of x6,000 x12,000 SEM images of each specimen of grass pollen cropped to 400x400 pixels. Dataset S2 has also been split into three parts:
Pooideae (Part 1) http://hdl.handle.net/2142/43360
Chloridoideae (Part 2) http://hdl.handle.net/2142/43361
Panicoideae (Part 3) http://hdl.handle.net/2142/43363
Dataset S3 consists of files used to test human ability to classify SEM images of grass pollen. These include a reference library, unclassified engraved images and master list of image classification: http://hdl.handle.net/2142/43365