cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7575945
title: The ocean tide model CIAM2 for the Canary Islands region (Spain)

description: Data set of Ocean tide Model for harmonic M2, in the Canary Islands region (Spain). This ocean tide model has been used in the paper "Anelastic response of the Earth's crust underneath the Canary Islands revealed from ocean tide loading observations", by Jose Arnoso, Machiel S. Bos, Maite Benavent, Nigel T. Penna, Sergio Sainz-Maza, submitted to Geophysical Journal International, 2022."<br>
description: {"references": ["Benavent, M., 2011. Estudio metodol\u00f3gico del efecto oce\u00e1nico indirecto y desarrollo de modelos de carga oce\u00e1nica. Aplicaciones geod\u00e9sicas para la Pen\u00ednsula Ib\u00e9rica y Canarias. Ph.D. Thesis., Universidad Complutense de Madrid, Spain. ISBN: 978-84-694-3115-3.", "Arnoso, J., Benavent, M., Ducarme, B., Montesinos, F. G., 2006. A new ocean tide loading model in the Canary Islands region, J. Geodyn., 41 (1\u20133), 100\u2013111. https://doi.org/10.1016/j.jog.2005.08.034"]}
DOI: 10.5281/zenodo.7575944
title: The ocean tide model CIAM2 for the Canary Islands region (Spain)

description: Data set of Ocean tide Model for harmonic M2, in the Canary Islands region (Spain). This ocean tide model has been used in the paper "Anelastic response of the Earth's crust underneath the Canary Islands revealed from ocean tide loading observations", by Jose Arnoso, Machiel S. Bos, Maite Benavent, Nigel T. Penna, Sergio Sainz-Maza, submitted to Geophysical Journal International, 2022."<br>
description: {"references": ["Benavent, M., 2011. Estudio metodol\u00f3gico del efecto oce\u00e1nico indirecto y desarrollo de modelos de carga oce\u00e1nica. Aplicaciones geod\u00e9sicas para la Pen\u00ednsula Ib\u00e9rica y Canarias. Ph.D. Thesis., Universidad Complutense de Madrid, Spain. ISBN: 978-84-694-3115-3.", "Arnoso, J., Benavent, M., Ducarme, B., Montesinos, F. G., 2006. A new ocean tide loading model in the Canary Islands region, J. Geodyn., 41 (1\u20133), 100\u2013111. https://doi.org/10.1016/j.jog.2005.08.034"]}
DOI: 10.5281/zenodo.7324718
title: GPS data set used in the paper "Anelastic response of the Earth's crust underneath the Canary Islands revealed from ocean tide loading observations"

description: Data set of continuous GPS observations at CVAN site in Gran Canaria (Canary Islands, Spain). The period of observation spans from July 3, 2013 to November 30, 2015. Data was acquired during the execution of the research project GCL2011-25494 of the Spanish Research Agency. This data set belongs to the Research Group ‘Geodesia’ of the University Complutense of Madrid, Spain, and has been used in the paper "Anelastic response of the Earth's crust underneath the Canary Islands revealed from ocean tide loading observations", by Jose Arnoso, Machiel S. Bos, Maite Benavent, Nigel T. Penna, Sergio Sainz-Maza, submitted to Geophysical Journal International, 2022.
DOI: 10.5281/zenodo.7324717
title: GPS data set used in the paper "Anelastic response of the Earth's crust underneath the Canary Islands revealed from ocean tide loading observations"

description: Data set of continuous GPS observations at CVAN site in Gran Canaria (Canary Islands, Spain). The period of observation spans from July 3, 2013 to November 30, 2015. Data was acquired during the execution of the research project GCL2011-25494 of the Spanish Research Agency. This data set belongs to the Research Group ‘Geodesia’ of the University Complutense of Madrid, Spain, and has been used in the paper "Anelastic response of the Earth's crust underneath the Canary Islands revealed from ocean tide loading observations", by Jose Arnoso, Machiel S. Bos, Maite Benavent, Nigel T. Penna, Sergio Sainz-Maza, submitted to Geophysical Journal International, 2022.
DOI: 10.25919/vyg7-7n73
title: Parkes observations for project P885 semester 2022OCTS_09

description: We request a continued modest monitoring cadence of once every 10 days for two radio magnetars, to track their rotation, flux density, and polarisation. 1E 1547.0-5408 has just suffered a 'hiccup' in radiative properties as well as in its rotation, and the near-term response to this event remains unclear. Swift J1818.0-1607 remains quite dynamic, and the unique UWL capabilities promise new opportunities to probe still mysterious radio emission mechanisms in magnetars. We will observe XTE J1810-197 and PSR J1622-4950, which has just ceased emission, less frequently. The overall request is 18 hours.
DOI: 10.4227/05/589007bda7528
title: Seasonal seed dispersal of Callitris verrucosa

description: The data set contains distance measures of primary (wind-borne) and secondary (on ground) seed dispersal during spring, summer and autumn, using empirical observations and detailed measurement of wind characteristics. Seeds were collected from populations of C. verrucosa within the reserve. was placed parallel to and 100 m from the burn edge within the burnt site.

For the empirical observation of seed dispersal we chose six release locations, three locations in each of two sites about 6 km apart that had both recently undergone a planned burn, one in spring 2009 and the other in autumn 2011. Within those two sites the three release locations were positioned 800 m apart from each other along a transect that was placed parallel to and 100 m from the burn edge within the burnt site.

To assess  primary (wind-borne) seed dispersal 20 randomly chosen seeds were released from each of three different heights (1 m, 2 m and 3 m) at each of the six sites, giving a total of 360 seeds released per season. Seeds were only released within a horizontal wind speed range of 8 - 25 km/h. At lower wind speeds seeds would not take-off and at higher wind speeds seeds could not be relocated. This data set could be reused in a similar study carried out for the same species in a different location.

To understand the effect of standing vegetation on the secondary (on-ground) seed dispersal, we established groups of 10 seeds on the ground within 10 m of each of the six previous release locations. Seed were left for 4 days before relocated and distances to the starting point were measured. This was repeated during all 3 seasons. Out of the 180 seeds released,161 (89%) seeds could be relocated. This data set could be reused in a similar study carried out for the same species in a different location.

Wind measurements were taken on a sand dune crest in the site that was burned during autumn 2011 using an ultrasonic anemometer (Model WindMaster (Part 1590-PK-020), Gill Instruments Ltd, Lymington, UK). Measurements continued for two weeks in spring, summer and autumn. The anemometer measured horizontal wind speed, horizontal wind direction, and vertical wind speed every 0.1 s, producing a dynamic, three dimensional wind speed vector. Measurements were taken at 2 m height. This data set could be reused in models requiring wind data from this area during spring, summer and autumn.
DOI: 10.4227/05/59bf42700b048
title: Archaeal community responses to a decade of ecological restoration: OTU raw data matrix

description: We generated a total of 2,313,977 16S archaeal raw reads across the 36 replicates (64,277 _? 23,335 SD per replicate).  A total of 2,299,955 archaeal sequences (63,888 _? 23,473 SD per replicate) and 1,937 archaeal OTUs (54 _? 20 SD per replicate) remained for further analysis after quality filtering.
DOI: 10.4227/05/589421f9cd281
title: Highly Important Papers in Ecology (HIPE)

description: Three files are included:

VoteArticles.final.csv : a comma-delimited text file with the vote assessments on the relative quality of the submitted papers (Top 10, Between 11-25, Between 26-100 or Not in the top "100") and an indication of how well each voter knew the paper (Read it, Know it or Don't know it)

HIP.refs.txt : tab-delimited text file with all paper bibliographic information

citation.csv : a comma-delimited text file with the citation data (Google Scholar, Web of Knowledge) for each paper and each journal (Impact Factor)
DOI: 10.5281/zenodo.7581076
title: MUSES Leaf Area Index (LAI) 8-Day Global 1km SIN Grid in 2010

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global LAI product at 1km spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2010. <em>Please <strong>click here</strong> to download the MUSES LAI product<strong> in 2009</strong></em>, and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2011</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2010 Spatial Resolution: 1km Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7581075
title: MUSES Leaf Area Index (LAI) 8-Day Global 1km SIN Grid in 2010

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global LAI product at 1km spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2010. <em>Please <strong>click here</strong> to download the MUSES LAI product<strong> in 2009</strong></em>, and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2011</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2010 Spatial Resolution: 1km Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7566992
title: Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for CoastTrain/5-class segmentation of RGB 768x768 NAIP images

description: <em><strong>Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for CoastTrain 5-class segmentation of RGB 768x768 NAIP images</strong></em> These Residual-UNet model data are based on Coast Train images and associated labels. https://coasttrain.github.io/CoastTrain/docs/Version%201:%20March%202022/data Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.1038/s41597-023-01929-2 Image size used by model: 768 x 768 x 3 pixels <em>classes:</em> water whitewater sediment other_bare_natural_terrain other_terrain <em>File descriptions</em> For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU <em>References</em><br> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym **Buscombe, D., Wernette, P., Fitzpatrick, S. <em>et al.</em> A 1.2 Billion Pixel Human-Labeled Dataset for Data-Driven Classification of Coastal Environments. <em>Sci Data</em> <strong>10</strong>, 46 (2023). https://doi.org/10.1038/s41597-023-01929-2
DOI: 10.5281/zenodo.7566991
title: Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for CoastTrain/5-class segmentation of RGB 768x768 NAIP images

description: <em><strong>Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for CoastTrain 5-class segmentation of RGB 768x768 NAIP images</strong></em> These Residual-UNet model data are based on Coast Train images and associated labels. https://coasttrain.github.io/CoastTrain/docs/Version%201:%20March%202022/data Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.1038/s41597-023-01929-2 Image size used by model: 768 x 768 x 3 pixels <em>classes:</em> water whitewater sediment other_bare_natural_terrain other_terrain <em>File descriptions</em> For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU <em>References</em><br> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym **Buscombe, D., Wernette, P., Fitzpatrick, S. <em>et al.</em> A 1.2 Billion Pixel Human-Labeled Dataset for Data-Driven Classification of Coastal Environments. <em>Sci Data</em> <strong>10</strong>, 46 (2023). https://doi.org/10.1038/s41597-023-01929-2
DOI: 10.4227/05/58b7cc10409b1
title: AEKOS Subset: Australian Plant Frequency and Cover, 2017

description: The data package was generated by TERN in response to a request by Jens Oldeland, Hamburg to investigate methodological differences in diversity indices using different measures for the parameters. It contains raw data mostly on frequency and % cover of Australian plant species stored in TERN's AEKOS as at 23 February 2017. There is some basal area data too.The data includes plant records for the following datasets: Australian Ground Cover Reference Sites Database, Biological Survey of South Australia - Vegetation Survey - Biological Database of South Australia, Atlas of NSW database: VIS flora survey module, Queensland CORVEG Database, TERN AusPlots Rangelands, Transects for Environmental Monitoring and Decision Making (TREND) (2013-present) and TREND-Biome of Australia Soil Environments (BASE) _? Soil samples for physical structure and chemical analysis (14 sites) throughout Australia (2013), AusCover Supersites SLATS Star Transects, Biological Survey of the Ravensthorpe Range (Western Australia), Biological Survey of South Australia - Roadside Vegetation Survey, Biological Database of South Australia, South-Western Australian Transitional Transect (SWATT), Koonamore Vegetation Monitoring Project (1925-present), Desert Ecology Research Group Plots (1990-2011) and Long Term Ecological Research Network (2012-2015), Simpson Desert, Western Queensland, Australia (plants only) and TERN AusPlots Forest Monitoring Network - Large Tree Survey - 2012-2015. 97,035 sites were extracted and downloaded for individual and population levels. The download package contains site location files, separate data files for individual and population levels, citation details for individual surveys and notes on how to interpret the download.
DOI: 10.5281/zenodo.7582421
title: cellsig: a Bayesian sparse random-effect model and large-scale human bulk transcriptional catalogue support cell-type marker selection

description: <strong>Human Bulk Cell-type Catalogue (HBCC): This database harmonises 1,435 samples of 67 cell types from 58 datasets which can be utilised in the cellsig method for estimating cell-type transcriptional profiles.</strong>
DOI: 10.5281/zenodo.7582420
title: cellsig: a Bayesian sparse random-effect model and large-scale human bulk transcriptional catalogue support cell-type marker selection

description: <strong>Human Bulk Cell-type Catalogue (HBCC): This database harmonises 1,435 samples of 67 cell types from 58 datasets which can be utilised in the cellsig method for estimating cell-type transcriptional profiles.</strong>
DOI: 10.58064/v0w5-yh68
title: OK_D2CreVGLUT3fx_Extinction

description: Extinction task for a mouse line unable to secrete glutamate from cholinergic interneurons. Male mice and their wild-type controls were evaluated at 4-6 months of age to assess the ability to extinguish a learned response.
DOI: 10.58064/p7pe-2j39
title: OK_D2CreVAChTfx_Extinction

description: Extinction task for a mouse line unable to secrete acetylcholine from cholinergic interneurons. Male mice and their wild-type controls were evaluated at 4-7 months of age for the ability to extinguish a learned response.
DOI: 10.58064/szbk-vq74
title: OK_ChATIRESVGLUT3fx_Extinction

description: Extinction task for a mouse line unable to secrete glutamate from cholinergic interneurons. Male mice and their wild-type controls were evaluated at 5-7 months of age to assess their ability to extinguish a learned response.
DOI: 10.58064/n2js-n235
title: OK_En1CreVAChTfx_Extinction

description: Extinction Task for a brainstem cholinergic-deficient mouse line. Male mice and their wild-type controls were evaluated at 9-12 months of age for their ability to extinguish a learned response.
DOI: 10.58064/6jpd-fs93
title: OK_VAChTfx_Virus_5Choice

description: 5-Choice Serial Reaction Time (5-CSRT) task for a striatum-specific cholinergic-deficient mouse line. Male mice received a viral injection of AAV-Cre or AAV-GFP in the dorsomedial striatum at 8-14 months of age and were evaluated for attention deficits.
DOI: 10.58064/h9ts-6z21
title: OK_VAChTfxControls_HeteroSequence

description: Heterogeneous sequence task for control VAChT^flox/flox mice. Male mice were evaluated at 5-8 months of age to assess sequential response learning and cue detection.
DOI: 10.58064/eh13-fv57
title: PVD M83 dTPR EXP 1

description: PVD for testing cognitive flexibility in M83 homo dTPR mice
DOI: 10.58064/5gmj-mv77
title: M83 dTPR PVD EXP-2

description: PVD for analysis of cognitive flexibility