cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25318/1210017401-fra
title: Importations et exportations de marchandises, sur une base douanière, par l'accord de libre-échange et par produits

description: Données sur le commerce international de marchandises groupées par accord de libre-échange et produits. Les utilisateurs ont l'option de choisir les importations ou les exportations ainsi que de sélectionner les valeurs totales ou les valeurs d'une section, d'une division, ou d'un des groupes du Système de classification de produits de l'Amérique du Nord (SCPAN). Les utilisateurs peuvent également choisir l'un ou l'autre des groupes de pays avec lesquels le Canada a noué un accord de libre-échange ou avec lesquels il négocie ou discute actuellement la possibilité de passer un tel accord. Les données sont présentées sur une base douanière et non désaisonnalisées.
DOI: 10.5281/zenodo.7801238
title: Pest Sticky Traps: a dataset for Whitefly Pest Population Density Estimation in Chromotropic Sticky Traps

description: The dataset

The Pest Sticky Traps (PST) dataset is a collection of yellow chromotropic sticky trap pictures specifically designed for training/testing deep learning models to automatically count insects and estimate pest populations.

Images were manually annotated by some experts of the Department of Agriculture, Food and Environment of the University of Pisa (Italy) by putting a dot over the centroids of each identified insect. Specifically, we labeled insects as belonging to the category “whitefly” considering two different species, i.e., the sweet potato whitefly (Bemisia tabaci) (Gennadius) and the greenhouse whitefly (Trialeurodes vaporariorum) (Westwood).

The dataset comprises two subsets:- a subset we suggest using for the training/validation phases (contained in the `train/` folder)- a subset we suggest using for the test phase (contained in the `test/` folder)

Annotations of the two subsets are contained in `train/annotations.csv` and `test/annotations.csv`, respectively. They have the following columns:- *imageName* - filename of the image containing the whiteflies,- *X,Y* - 2D coordinates of the whitefly in the image space,- *class* - class index of the insect (always 0 in this dataset).

 

Citing our work

If you found this dataset useful, please cite the following paper



@inproceedings{CIAMPI2023102384,    title = {A deep learning-based pipeline for whitefly pest abundance estimation on chromotropic sticky traps},    journal = {Ecological Informatics},    volume = {78},    pages = {102384},    year = {2023},    issn = {1574-9541},
    doi = {10.1016/j.ecoinf.2023.102384},
    url = {https://www.sciencedirect.com/science/article/pii/S1574954123004132},
    year = 2023,
    author = {Luca Ciampi and Valeria Zeni and Luca Incrocci and Angelo Canale and Giovanni Benelli and Fabrizio Falchi and Giuseppe Amato and Stefano Chessa},
}



and this Zenodo Dataset



@dataset{ciampi_2023_7801239,
    author = {Luca Ciampi and Valeria Zeni and Luca Incrocci and Angelo Canale and Giovanni Benelli and Fabrizio Falchi and Giuseppe Amato and Stefano Chessa},
    title = {Pest Sticky Traps: a dataset for Whitefly Pest Population Density Estimation in Chromotropic Sticky Traps}},
    month = apr,
    year = 2023,
    publisher = {Zenodo},
    version = {1.0.0},
    doi = {10.5281/zenodo.7801239},
    url = {https://doi.org/10.5281/zenodo.6560823}
}



 

Contact Information

If you would like further information about the dataset or if you experience any issues downloading files, please contact us at luca.ciampi@isti.cnr.it
DOI: 10.5281/zenodo.7801239
title: Pest Sticky Traps: a dataset for Whitefly Pest Population Density Estimation in Chromotropic Sticky Traps

description: The dataset

The Pest Sticky Traps (PST) dataset is a collection of yellow chromotropic sticky trap pictures specifically designed for training/testing deep learning models to automatically count insects and estimate pest populations.

Images were manually annotated by some experts of the Department of Agriculture, Food and Environment of the University of Pisa (Italy) by putting a dot over the centroids of each identified insect. Specifically, we labeled insects as belonging to the category “whitefly” considering two different species, i.e., the sweet potato whitefly (Bemisia tabaci) (Gennadius) and the greenhouse whitefly (Trialeurodes vaporariorum) (Westwood).

The dataset comprises two subsets:- a subset we suggest using for the training/validation phases (contained in the `train/` folder)- a subset we suggest using for the test phase (contained in the `test/` folder)

Annotations of the two subsets are contained in `train/annotations.csv` and `test/annotations.csv`, respectively. They have the following columns:- *imageName* - filename of the image containing the whiteflies,- *X,Y* - 2D coordinates of the whitefly in the image space,- *class* - class index of the insect (always 0 in this dataset).

 

Citing our work

If you found this dataset useful, please cite the following paper



@inproceedings{CIAMPI2023102384,    title = {A deep learning-based pipeline for whitefly pest abundance estimation on chromotropic sticky traps},    journal = {Ecological Informatics},    volume = {78},    pages = {102384},    year = {2023},    issn = {1574-9541},
    doi = {10.1016/j.ecoinf.2023.102384},
    url = {https://www.sciencedirect.com/science/article/pii/S1574954123004132},
    year = 2023,
    author = {Luca Ciampi and Valeria Zeni and Luca Incrocci and Angelo Canale and Giovanni Benelli and Fabrizio Falchi and Giuseppe Amato and Stefano Chessa},
}



and this Zenodo Dataset



@dataset{ciampi_2023_7801239,
    author = {Luca Ciampi and Valeria Zeni and Luca Incrocci and Angelo Canale and Giovanni Benelli and Fabrizio Falchi and Giuseppe Amato and Stefano Chessa},
    title = {Pest Sticky Traps: a dataset for Whitefly Pest Population Density Estimation in Chromotropic Sticky Traps}},
    month = apr,
    year = 2023,
    publisher = {Zenodo},
    version = {1.0.0},
    doi = {10.5281/zenodo.7801239},
    url = {https://doi.org/10.5281/zenodo.6560823}
}



 

Contact Information

If you would like further information about the dataset or if you experience any issues downloading files, please contact us at luca.ciampi@isti.cnr.it
DOI: 10.5517/ccdc.csd.cc2fp3s1
title: CCDC 2254623: Experimental Crystal Structure Determination

description: Related Article: Dexin Wang, Yanshu Liu, Guanjie Chen, Fangfang Gao, Guanyun Zhang, Guo Wang, Chen-Ho Tung, Yifeng Wang|2023|Inorg.Chem.|62|21074|doi:10.1021/acs.inorgchem.3c02876
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2frz8g
title: CCDC 2257366: Experimental Crystal Structure Determination

description: Related Article: Manisha Lamba, Prasoon Raj Singh, Shubham Bhatt, Avijit Goswami|2024|Green Chemistry|26|448|doi:10.1039/D3GC03702A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2frzfm
title: CCDC 2257371: Experimental Crystal Structure Determination

description: Related Article: Manisha Lamba, Prasoon Raj Singh, Shubham Bhatt, Avijit Goswami|2024|Green Chemistry|26|448|doi:10.1039/D3GC03702A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.24926469
title: Supplementary Material for: Posterior circulation mechanical thrombectomy through primitive trigeminal artery: A case report

description: Introduction: Primitive trigeminal artery (PTA) is a rare intracranial vascular malformation, and mechanical thrombectomy and revascularization via PTA are rarely reported. 
Case presentation: We reported a case of mechanical thrombectomy through PTA in a patient who presented with sudden slurred speech and had a National Institutes of Health Stroke Scale score of 12. Digital subtraction angiography of the cerebral vasculature showed PTA formation in the right internal carotid artery cavernous segment, with acute occlusion of the distal basilar artery at the PTA junction, and bilateral vertebral arteries and proximal basilar artery were underdeveloped. Therefore, we chose mechanical thrombectomy via PTA; but unfortunately, the vessel failed to recanalize. Follow-up at 1 month post-procedure indicated that the patient had passed away. We present the endovascular process and analyze and summarize the reasons for the failure to provide a reference for subsequent mechanical thrombectomy via PTA. 
Conclusions: PTA increases the risk of ischemic stroke and adds to the complexity of mechanical thrombectomy post-stroke. However, in certain situations, PTA can be used as a thrombectomy channel to increase the first-line possibility of timely endovascular treatment to save ischemic brain tissue.
DOI: 10.6084/m9.figshare.24926469.v1
title: Supplementary Material for: Posterior circulation mechanical thrombectomy through primitive trigeminal artery: A case report

description: Introduction: Primitive trigeminal artery (PTA) is a rare intracranial vascular malformation, and mechanical thrombectomy and revascularization via PTA are rarely reported. 
Case presentation: We reported a case of mechanical thrombectomy through PTA in a patient who presented with sudden slurred speech and had a National Institutes of Health Stroke Scale score of 12. Digital subtraction angiography of the cerebral vasculature showed PTA formation in the right internal carotid artery cavernous segment, with acute occlusion of the distal basilar artery at the PTA junction, and bilateral vertebral arteries and proximal basilar artery were underdeveloped. Therefore, we chose mechanical thrombectomy via PTA; but unfortunately, the vessel failed to recanalize. Follow-up at 1 month post-procedure indicated that the patient had passed away. We present the endovascular process and analyze and summarize the reasons for the failure to provide a reference for subsequent mechanical thrombectomy via PTA. 
Conclusions: PTA increases the risk of ischemic stroke and adds to the complexity of mechanical thrombectomy post-stroke. However, in certain situations, PTA can be used as a thrombectomy channel to increase the first-line possibility of timely endovascular treatment to save ischemic brain tissue.
DOI: 10.6084/m9.figshare.24646026
title: Data and code supporting "Impact of Fishery Observer Protocol on Estimated Bycatch Rates of Marine Mammals", K. Precoda, C.D. Orphanides, 2024, ICES Journal of Marine Science.

description: Zip file includes:-data on observed bycatch of grey seals, harbor seals, and harbor porpoise in gillnets in the US northwestern Atlantic, 1994-2019. The data includes species, animal condition, animal entanglement situation, month and year, soak duration in hours, average gillnet mesh size in inches, whether tiedowns were used, unique trip and haul identifiers, and observer protocol (C=fish-focused, L=mammal-focused). Codes for animal condition and entanglement are explained in the output file produced by the R code.-R code used to perform the analyses and create figures for the paper -Output of the R code
DOI: 10.6084/m9.figshare.24646026.v2
title: Data and code supporting "Impact of Fishery Observer Protocol on Estimated Bycatch Rates of Marine Mammals", K. Precoda, C.D. Orphanides, 2024, ICES Journal of Marine Science.

description: Zip file includes:-data on observed bycatch of grey seals, harbor seals, and harbor porpoise in gillnets in the US northwestern Atlantic, 1994-2019. The data includes species, animal condition, animal entanglement situation, month and year, soak duration in hours, average gillnet mesh size in inches, whether tiedowns were used, unique trip and haul identifiers, and observer protocol (C=fish-focused, L=mammal-focused). Codes for animal condition and entanglement are explained in the output file produced by the R code.-R code used to perform the analyses and create figures for the paper -Output of the R code
DOI: 10.6084/m9.figshare.24646026.v1
title: Data and code supporting "Impact of Fishery Observer Protocol on Estimated Bycatch Rates of Marine Mammals", K. Precoda, C.D. Orphanides, 2024, ICES Journal of Marine Science.

description: Zip file includes:-data on observed bycatch of grey seals, harbor seals, and harbor porpoise in gillnets in the US northwestern Atlantic, 1994-2019. The data includes species, animal condition, animal entanglement situation, month and year, soak duration in hours, average gillnet mesh size in inches, whether tiedowns were used, unique trip and haul identifiers, and observer protocol (C=fish-focused, L=mammal-focused). Codes for animal condition and entanglement are explained in the output file produced by the R code.-R code used to perform the analyses and create figures for the paper -Output of the R code
DOI: 10.5281/zenodo.10418005
title: Supplementary PyMOL session summarizing results of ABR variants docking to the human IL-22R1.
DOI: 10.5281/zenodo.10418004
title: Supplementary PyMOL session summarizing results of ABR variants docking to the human IL-22R1.
DOI: 10.6084/m9.figshare.24926493
title: S 1: RE

description: This is a project.
DOI: 10.6084/m9.figshare.24926493.v1
title: S 1: RE

description: This is a project.
DOI: 10.5281/zenodo.7689062
title: BinMov2023: Binaural Dataset for Source Position Estimation with Head Rotation and Moving Listeners

description: DESCRIPTION

BinMov2023: Binaural Dataset for Source Position Estimation with Head Rotation and Moving Listeners is a binaural dataset containing synthetic data of single source speech signals reverberated with simulated room impulse responses. The data allows for experiments related to audio tasks of sound source localization and sound distance estimation. The dataset consists of three subsets, related to three different scenarios:

- static: a static sound source and a static listener- rotation: a static sound source and a static listener with a head rotating in the azimuth plane- walking: a static sound source and a listener moving in space

Each sound file contains a unique combination of a simulated room and source and receiver positions. The walking scenario contains simulations of 2500 different rooms, whereas the static and rotation scenarios contain 5000 rooms. 

 

REPORT AND REFERENCE

A detailed description of the dataset and the data generation process can be found in:

D. A. Krause, G. García-Barrios, A. Politis and A. Mesaros, "Binaural Sound Source Distance Estimation and Localization for a Moving Listener," in IEEE/ACM Transactions on Audio, Speech, and Language Processing, doi: 10.1109/TASLP.2023.3346297.

available here. The supplementary material describing the data simulation is available under this link.

If you use the dataset, please consider citing the abovementioned paper. 

 

METADATA

Each sound file has a separate metadata file assigned. The information in the metadata comes per frame in the following format:

[nb_frame (int)], [x (float)], [y (float)], [z (float)], [a (float)], [b (float)], [c (float)], [d (float)]

Where nb_frame is the number of frame, {x, y, z} are the unnormalized Cartesian coordinates of the sound source and {a, b, c, d} are the quaternion values related to the rotation of the listener's head.

 

LICENSE

The database is published under a custom **open non-commercial with attribution** license. It can be found in the `LICENSE.txt` file that accompanies the data.
DOI: 10.5281/zenodo.7689063
title: BinMov2023: Binaural Dataset for Source Position Estimation with Head Rotation and Moving Listeners

description: DESCRIPTION

BinMov2023: Binaural Dataset for Source Position Estimation with Head Rotation and Moving Listeners is a binaural dataset containing synthetic data of single source speech signals reverberated with simulated room impulse responses. The data allows for experiments related to audio tasks of sound source localization and sound distance estimation. The dataset consists of three subsets, related to three different scenarios:

- static: a static sound source and a static listener- rotation: a static sound source and a static listener with a head rotating in the azimuth plane- walking: a static sound source and a listener moving in space

Each sound file contains a unique combination of a simulated room and source and receiver positions. The walking scenario contains simulations of 2500 different rooms, whereas the static and rotation scenarios contain 5000 rooms. 

 

REPORT AND REFERENCE

A detailed description of the dataset and the data generation process can be found in:

D. A. Krause, G. García-Barrios, A. Politis and A. Mesaros, "Binaural Sound Source Distance Estimation and Localization for a Moving Listener," in IEEE/ACM Transactions on Audio, Speech, and Language Processing, doi: 10.1109/TASLP.2023.3346297.

available here. The supplementary material describing the data simulation is available under this link.

If you use the dataset, please consider citing the abovementioned paper. 

 

METADATA

Each sound file has a separate metadata file assigned. The information in the metadata comes per frame in the following format:

[nb_frame (int)], [x (float)], [y (float)], [z (float)], [a (float)], [b (float)], [c (float)], [d (float)]

Where nb_frame is the number of frame, {x, y, z} are the unnormalized Cartesian coordinates of the sound source and {a, b, c, d} are the quaternion values related to the rotation of the listener's head.

 

LICENSE

The database is published under a custom **open non-commercial with attribution** license. It can be found in the `LICENSE.txt` file that accompanies the data.
DOI: 10.34740/kaggle/dsv/7256839
title: Páginas Web de Andalucía
DOI: 10.5281/zenodo.10451840
title: [Supplementary Material] Application of a Size Measurement Standard for Data Warehouse Projects
DOI: 10.5281/zenodo.10451841
title: [Supplementary Material] Application of a Size Measurement Standard for Data Warehouse Projects
DOI: 10.5066/p9uw0lr4
title: Compilation of topographic data, aerial imagery, and land cover classification, collected between 1984 to 2021 for an aeolian dune field near Lees Ferry, AZ

description: These data were compiled for assessing how geomorphic changes measured as topographic differences from repeat surveys represent measured and modelled estimates of aeolian sediment transport and dune mobility. Objective(s) of our study were to investigate whether topographic changes can serve as a proxy for aeolian transport and sediment mobility in dunefield environments. This was accomplished by relating topographic changes to modeled and observed estimates of sediment transport and dune mobility over months to decades within a partially vegetated dunefield starved of upwind sediment supplies. We specifically tested if topographic changes measured as net and total volume changes and topographic surface roughness differences provide evidence for intra-annual differences and decadal changes in sediment mobility for dune sand that is either currently bare, vegetated, or biocrust-covered. Lastly, these data were used as a framework for interpreting how aeolian transport and sediment mobility has changed for current land cover types over the preceding four decades. These data represent monthly topographic surveys and in-field sediment transport data collected between February 13, 2020 and December 16, 2020, piloted aerial imagery collected in 1984, 2002, 2009, 2013, and 2021, unoccupied aerial vehicle (UAV) imagery collected in March 2021, classification of land cover, and tabular summaries of topographic changes derived from these datasets. These data were collected between 1984 and 2021 within a small aeolian dunefield near the confluence of the Paria and Colorado Rivers, upstream of Grand Canyon National Park, Arizona. These data were collected by the U.S. Geological Survey. These data can be used to 1) to evaluate how dune surfaces with bare sand, sand with vegetated cover, and sand with biological soil crust cover (biocrust) change on a monthly time scale with differences in wind strength and 2) assess how the dunefield surface changed with vegetation loss and expansion over almost 4 decades. Additionally, these data could be used to assess detailed changes in landscape cover over monthly and decadal time scales.
DOI: 10.57967/hf/1557
title: coser
DOI: 10.6084/m9.figshare.24921954
title: Copy of 2420095_01-.xlsx

description: As a type of land degradation, desertification is a crucial phenomenon that negatively impacts the environment and threatens life on Earth. The desertification processes effectively influence the arid and semiarid regions and decrease the land's efficiency with increment speeds. Therefore, recognizing this phenomenon is very important. This study was conducted in The Sistan Plain, Iran, to monitor and forecast desertification using remote sensing-based indices, including land use and land cover (LULC) map, normalized differential vegetation index (NDVI), improved vegetation index (EVI), vegetation condition index (VCI), surface temperature condition index (TCI), modified normalized differential water level index (MNDWI) and salinity index (SI). In addition to satellite data, environmental indices, including standardized precipitation index (SPI) and streamflow drought index (SDI), were used. Satellite data of the study consisted of Landsat 5, and 8 images were taken in June each year over the ten years (1990-2020). Helping to comprehend the ecosystem's mechanisms and alterations before it gets irreversible, monitoring desertification is the most effective way to assess desertification processes. Because using different classification methods with the same data will yield different results, selecting an appropriate method for classifying remote sensing indices for monitoring desertification is necessary. Therefore, correct scientific assessment is required to classify the obtained results. On the other hand, the random forest (RF) method and the mixed model of automated cells and Markov chain (CA-Markov) were used to monitor desertification quantitatively and to predict the desertification condition in 2030, respectively.