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SIF datasets (50 m) and demos of network structure designs on the study of the STP-SIF issue
<p>The SIF datasets (i.e., SIF2019 and SIF2020) accompany the paper "Regional-Scale Cotton Yield Forecast via Data-Driven Spatio-Temporal Prediction (STP) of Solar-Induced Chlorophyll Fluorescence (SIF)" that was published in <a href="https://www.sciencedirect.com/science/article/abs/pii/S0034425723004121">Remote Sensing of Environment</a> on October 20, 2023. They have a spatial resolution of about 50 m and a monthly temporal resolution. Each of them has seven bands, corresponding to April to October. Please refer to our previous work, "Downscaling solar-induced chlorophyll fluorescence for field-scale cotton yield estimation by a two-step convolutional neural network", which was published in <a href="https://www.sciencedirect.com/science/article/pii/S0168169922005737">Computers and Electronics in Agriculture</a> on August 14, 2022, for the development and detailed description.</p><p>The geographic reference (ESPG: 4326 (WGS_1984)) is the same for the two dataset, conforming to that in the geotiff file.</p><p><strong>Citation</strong>:</p><p>[1] Kang, X., Huang, C., Zhang, L., Wang, H., Zhang, Z., Lv, X., 2023. Regional-scale cotton yield forecast via data-driven spatio-temporal prediction (STP) of solar-induced chlorophyll fluorescence (SIF). Remote Sensing of Environment 299, 113861. doi:10.1016/j.rse.2023.113861</p><p>[2] Kang, X., Huang, C., Zhang, L., Zhang, Z., Lv, X., 2022. Downscaling solar-induced chlorophyll fluorescence for field-scale cotton yield estimation by a two-step convolutional neural network. Computers and Electronics in Agriculture 201, 107260. doi:10.1016/j.compag.2022.107260</p><p>[3] Kang, X., Huang, C., Chen, J.M., Lv, X., Wang, J., Zhong, T., Wang, H., Fan, X., Ma, Y., Yi, X., Zhang, Z., Zhang, L., Tong, Q., 2023. The 10-m cotton maps in Xinjiang, China during 2018-2021. Sci Data 10, 688. doi:10.1038/s41597-023-02584-3</p><p>[4] Lang, P., Zhang, L., Huang, C., Chen, J., Kang, X., Zhang, Z., Tong, Q., 2023. Integrating environmental and satellite data to estimate county-level cotton yield in Xinjiang Province. Frontiers in Plant Science 13, 1048479. doi:10.3389/fpls.2022.1048479</p>
- Fore wing with 2m–cu apical to rs–m (a); mesosternum yellow (b) ………………………11 in A review of the Afrotropical Rhyssinae (Hymenoptera: Ichneumonidae) with the descriptions of five new species
- Fore wing with 2m–cu apical to rs–m (a); mesosternum yellow (b) ………………………11
Reproduction Package for the paper "The early evolution of young massive clusters. II. The kinematic history of NGC 6618 / M 17"
<h2>Reproduction package for the paper "The early evolution of young massive clusters. II. The kinematic history of NGC 6618 / M 17".</h2><ul><li>This reproduction package aims for open science, with the internal API designation of 'Gold'</li><li>Authors: M. Stoop, A. Derkink, L. Kaper, A. de Koter, C. Rogers, M.C. Ramírez-Tannus, D. Guo, N. Azatyan</li><li>Paper DOI: https://doi.org/10.1051/0004-6361/202347383</li><li>Arxiv DOI: https://doi.org/10.48550/arXiv.2311.04174</li><li>Zenodo DOI: http://doi.org/10.5281/zenodo.8120575</li><li>Accepted for publication in Astronomy and Astrophysics (date of acceptance: 2023/10/27)</li></ul><h2>Raw Data</h2><ul><li>./gaia_files/ gives the ADQL queries on how to obtain the raw data from the Gaia database along with the raw Gaia data itself.</li><li>./other_files/ gives the other raw data obtained from other sources. For example the literature, Aladin, parsec isochrones.</li><li>We have cited the relevant references in the paper for raw data taken from the literature.</li><li>Optical VLT, SALT, WHT spectra (raw data, software and end-products) described in Appendix A can be requested from Annelotte Derkink.</li></ul><h2>Software</h2><ul><li>MacOS Big Sur 11.6</li><li>Jupyter Notebook (6.3.0)</li><li>Programming languages used: Python (3.9.7)</li><li>Python packages used: numpy (1.22.3), math (comes with Python), pandas (1.2.1), matplotlib (3.3.3), scipy (1.6.0), os (comes with Python), zero_point (0.0.1) (https://gitlab.com/icc-ub/public/gaiadr3_zeropoint), pyUPMASK (requires os, time, multiprocessing, astropy, pathlib) (https://github.com/msolpera/pyUPMASK)</li><li>Aladin Desktop (Version 11.0) (https://aladin.u-strasbg.fr/AladinDesktop/), will need java installed</li><li>Aladin Lite (https://aladin.cds.unistra.fr/AladinLite/)</li></ul><h2>Figures and Tables</h2><ul><li>Figures can be reproduced from the ./figures/ folder.</li><li>All material and data used are available either in the Raw Data or in the Intermediate Data</li><li>Jupyter notebooks (.ipynb files) provide the option to execute all code as ready-made to produce the figures: 'Cell' -> 'Run All'.</li><li>The figures shown in the paper will be saved in ./figures/figures_paper/ folder.</li><li>Tables can be reproduced from the ./tables/ folder similar to the figures.</li><li>Tables are saved in .csv files so they can easily be used. Tables are also saved in _latex.csv form for easy implementation in latex.</li></ul><h2>Intermediate data products</h2><ul><li>Intermediate data, such as the determined members of NGC6618 or found runaway stars, can be found in the /output_files/ folder.</li><li>The 'raw' data is also given here, because small corrections have been applied, which are recommended by the Gaia Consortium. This 'raw' data file is not the same as the one given in the ./gaia_files/ folder! This is to ensure that both the raw and intermediate data products are available.</li><li>./modules/ is part of the Python pyUPMASK module.</li></ul><h2>End-to-End analysis scripts</h2><ul><li>The end-to-end analysis to determine the members is given in the Jupyter Notebook 'membership_pyupmask.ipynb'.</li><li>The end-to-end analysis to search for runaways is given in the Jupyter Notebook 'runaways.ipynb'.</li><li>Minor 'end-to-end' analysis is done in the relevant figure or table Jupyter Notebook.</li></ul>
FIGURES 30-31 in Reid, C.A.M. & Beatson, M. (2010) Revision of the Australo-Papuan genus Macrolema Baly (Coleoptera: Chrysomelidae: Spilopyrinae), with description of a new genus. Zootaxa, 2486, 1-60.
FIGURES 30-31. Face of Macrolema species: 30, M. ventralis (Lea); 31, M. vittata Baly.
Fig. 8. Misgolas wayorum n in Trapdoor Spiders of the Genus Misgolas (Mygalomorphae: Idiopidae) in the Sydney Region, Australia, With Notes on Synonymies Attributed to M. rapax
Fig. 8. Misgolas wayorum n.sp. (A–D) Ƌ, holotype AM KS50047.
Fig. 7 in Trapdoor Spiders of the Genus Misgolas (Mygalomorphae: Idiopidae) in the Sydney Region, Australia, With Notes on Synonymies Attributed to M. rapax
Fig. 7. Misgolas maculosus. (A–C) ♀, AM KS69957. (A), tarsus
Figs 32–35. Sabaha spp., male genitalia. 32, 33 – S in New species of gelechiid moths of the genus Sabaha M. Omelko et N. Omelko, 2019 (Lepidoptera: Gelechiidae) from Borneo Island
Figs 32–35. Sabaha spp., male genitalia. 32, 33 – S. gemella sp. n., holotype: 32 – ventral
Figs 11–19. Sabaha spp., adults. 11, 12 – S in New species of gelechiid moths of the genus Sabaha M. Omelko et N. Omelko, 2019 (Lepidoptera: Gelechiidae) from Borneo Island
Figs 11–19. Sabaha spp., adults. 11, 12 – S. parda sp. n.: 11 – male, holotype; 12 –
Figs 1–10 in New species of gelechiid moths of the genus Sabaha M. Omelko et N. Omelko, 2019 (Lepidoptera: Gelechiidae) from Borneo Island
Figs 1–10. Sabaha spp., adults, morphostructures. 1–8 – S. spathulata, male, holotype:
Figs 44–52 in New species of gelechiid moths of the genus Sabaha M. Omelko et N. Omelko, 2019 (Lepidoptera: Gelechiidae) from Borneo Island
Figs 44–52. Sabaha spp., female genitalia, 7th abdominal segment. 44–46 – S. parda sp.
Figs 36–43 in New species of gelechiid moths of the genus Sabaha M. Omelko et N. Omelko, 2019 (Lepidoptera: Gelechiidae) from Borneo Island
Figs 36–43. Sabaha spp., male genitalia, basal abdominal segments. 36–39 – S.
Figs 20–23. Sabaha spp., adults. 20, 21 – S in New species of gelechiid moths of the genus Sabaha M. Omelko et N. Omelko, 2019 (Lepidoptera: Gelechiidae) from Borneo Island
Figs 20–23. Sabaha spp., adults. 20, 21 – S. pilosa sp. n.: 20 – male, holotype; 21 –
Dataset for "Takahama, M., Patil, A., Richey, G. et al. A pairwise cytokine code explains the organism-wide response to sepsis. Nat Immunol 25, 226–239 (2024)."
Open the record for dataset details and reuse information.
Figure 24. Localities where M. playa has been found. 1-2 in Maratus playa, a new peacock spider in the fimbriatus group from Australia (Araneae: Salticidae: Euophryini)
Figure 24. Localities where M. playa has been found. 1-2, Sturt National Park, New South Wales (reference 6 in Table 1). 3-5, Type locality near Mildura, Victoria (reference 2 in Table 1). 6, Flat plain near Lake Goongarrie in Western Australia (references 11-12 in Table 1). Photo credits: 3-5, Shannon Deale; 6, Mathew Hourston.
Figure 2. 1-2, J Maratus bubo Otto & Hill 2016. 3-4, J M in Maratus nambung, a new peacock spider from the southwestern corner of Australia (Araneae: Salticidae: Euophryini)
Figure 2. 1-2, J Maratus bubo Otto & Hill 2016. 3-4, J M. cf. bubo, undescribed. Except for the bright red colour of pigmented scales on the fan, this form appears to be closer to M. nambung than to M. bubo. 5-6, J M. nambung, new species. M. bubo has a large fringe on tibia-metatarsi III, with a fan that is more rounded at the rear and marked in that place by three red lines. The other two species lack this fringe, but have a shorter fringe of black setae. The carapace of M. nambung (5-6) has a cover of brown (rather than dull red) scales, and pigmented scales of the fan are orange to red-orange rather than bright red, with a distinct black spot at the center.
Fig. 1 in Resurrection Of The Genus Micherdzinskiiobovella Hirschmann, 1989, With The Description Of M. Petofii Sp. N. From Singapore (Acari: Mesostigmata: Urodinychidae)
Fig. 1. Micherdzinskiiobovella petofii sp. n., holotype, female, dorsal view
Fig. 2 in Resurrection Of The Genus Micherdzinskiiobovella Hirschmann, 1989, With The Description Of M. Petofii Sp. N. From Singapore (Acari: Mesostigmata: Urodinychidae)
Fig. 2. Micherdzinskiiobovella petofii sp. n., holotype, female, ventral view
Data presented in figures of "Measurement Report: Insights into the chemical composition and origin of molecular clusters and potential precursor molecules present in the free troposphere over the Southern Indian Ocean: observations from the Maïdo observatory (2150 m a.s.l., Reunion Island)"
<p>This dataset includes the data shown in the figures of "Measurement Report: Insights into the chemical composition and origin of molecular clusters present in the free troposphere over the Southern Indian Ocean: observations from the Maïdo observatory (2150 m a.s.l., Reunion Island)". Read me files containing information on the reported data can be found in the different folders. </p>
Data and code for replication of: "Global warming and heat extremes to exacerbate inflationary pressures. M. Kotz, F. Kuik, E. Liz, C. Nickel. Nature Communications Earth & Environment (2023)."
<p>This repository contains secondary data and code necessary to reproduce the results of the manuscript:</p><p>Global warming and heat extremes to exacerbate inflationary pressures.</p><p>M. Kotz, F. Kuik, E. Liz, C. Nickel. Nature Communications Earth & Environment (2023).</p><p> </p><p>For further information please contact: maxkotz@pik-potsdam.de</p><p> </p><p>This document contains:</p><p>1. An outline of the data included in the repository.</p><p>2. An outline of the code included in the repository.</p><p> </p><p>See the README for further details.</p><p> </p><p>Credit and thanks go to Miles Parker, Chiara Osbat and Emanuele Franceschi for compiling the inflation data which is used in this study. Inflation data provided here has been anonymised (countries shuffled and names replaced by random letter combinations) to enable reproduction of our results, while limiting further use. Moreover, inflation in terms of the change in the logarithm of prices is included, whereas the level of price indices are excluded. For full inflation data please see the forthcoming publication by Miles Parker, Chiara Osbat,and Emanuele Franceschi (contact Miles.Parker@ecb.europa.eu for further enquiries into the raw inflation data).</p>
Etruscan terracotta sarcophagus with lid M_Urn_A
Castello Banfi Collection (M_Urn A&B) Terracotta sarcophagus with lid. Pinkish orange clay with whitish slip and painted decoration in red, black, yellow. Mold made. Chest is decorated with figurative scene of hero fighting with a plow. Lid with reclining figure. On chest there used to be inscription painted in black, now almost completely invisible. Production of Chiusi. 1st c. BCE. Montalcino, Poggio alle Mura (Castello Banfi), n. inv. 94. Bischeri 2022: n. cat. 237. Processed in Reality Capture from 275 images. GDH ID M_urn A This project was done under the authority of the Soprintendenza Archeologia, belle arti e paesaggio per le province di Siena Grosseto e Arezzo in collaboration with Global Digital Heritage. We thank Elizabeth Koenig, Hospitality Project Director at Castello Banfi and President of the Scientific Committe of Fonadazione Banfi, for coordinating this project and making the collection accessible. The catalog of the Banfi collection was created by Dr. Matteo Bischeri (2022)." Source: Objaverse 1.0 / Sketchfab
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.