cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8101649
title: Raw dataset for article "Nonmotor symptoms associated with progressive loss of dopaminergic neurons in a mouse model of Parkinson's disease"

description: Dataset from the project investigating the presence of nonmotor symptoms of Parkinson's disease in a mouse model of progressive loss of dopaminergic neurons (namely,TIF-IA<sup>DATCreERT2 </sup>strain). Mice were tested for executive and cognitive functions (males: Operant Sensation Seeking test, OSS; females: Probabilistic Reversal Learning Task in Intellicages), olfactory acuity (males: buried food test), saccharin preference (males and females), and motor performance (males and females: test using CatWalk apparatus). The dataset includes files used to perform statistical analyses but their names may vary from the ones used in the scripts. For the purpose of recreating our analyses, please refer to the GitHub page, where both scripts and input data file names are compliant: https://github.com/annaradli/tif-pd-behavior. <strong>Description of files:</strong> animals_info.csv - animals data: genotype, sex, age, Intellicage tag identifier catwalk_run_statistics_all_females.csv - data recorded in CatWalk apparatus for females catwalk_run_statistics_all_males.csv - data recorded in CatWalk apparatus for males females_weight_raw_data_revised.csv - females' body weight (revised for containing Polish words) intellicage_raw_data.csv - data recorded in IntelliCage exported to .csv format intellicage_raw_data_R.RData - data recorded in IntelliCage in .RData format males_weight_raw_data.csv - males' body weight olfactory_time_digging_raw_data.csv - time to start digging at the right place in the buried food test olfactory_time_retrieve_raw_data.csv- time to retrieve cracker in the buried food test oss_raw_data.csv - data recorded in the OSS test saccharin_preference_males_raw_data.csv - saccharin preference test results for males snvta_cells_count.csv - number of TH+ cells in SN and VTA in male mice (3+3) 14 weeks after tamoxifen treatment v2 contains the corrected 'animals_info.csv' file without an unnecessary column. v3 has a revised version of file containing females' weight measurements and also added a file with midbrain cell counts
DOI: 10.5281/zenodo.10104172
title: Raw dataset and additional data for article "Nonmotor symptoms associated with progressive loss of dopaminergic neurons in a mouse model of Parkinson's disease"

description: Dataset from the project investigating the presence of nonmotor symptoms of Parkinson's disease in a mouse model of progressive loss of dopaminergic neurons (namely,TIF-IADATCreERT2 strain). Mice were tested for executive and cognitive functions (males: Operant Sensation Seeking test, OSS; females: Probabilistic Reversal Learning Task in Intellicages), olfactory acuity (males: buried food test), saccharin preference (males and females), and motor performance (males and females: test using CatWalk apparatus).
The dataset includes files used to perform statistical analyses but their names may vary from the ones used in the scripts. For the purpose of recreating our analyses, please refer to the GitHub page, where both scripts and input data file names (in 'Raw data files' section) are compliant: https://github.com/annaradli/tif-pd-behavior.
Description of files:
Raw data files:

animals_info.csv - animals data: genotype, sex, age, Intellicage tag identifier
catwalk_run_statistics_all_females.csv - data recorded in CatWalk apparatus for females
catwalk_run_statistics_all_males.csv - data recorded in CatWalk apparatus for males
females_weight_raw_data_revised.csv - females' body weight  (revised for containing Polish words)
intellicage_raw_data.csv - data recorded in IntelliCage exported to .csv format
intellicage_raw_data_R.RData - data recorded in IntelliCage in .RData format
males_weight_raw_data.csv - males' body weight
olfactory_time_digging_raw_data.csv - time to start digging at the right place in the buried food test
olfactory_time_retrieve_raw_data.csv- time to retrieve cracker in the buried food test
oss_raw_data.csv - data recorded in the OSS test
saccharin_preference_males_raw_data.csv - saccharin preference test results for males
snvta_cells_count.csv - number of TH+ cells in SN and VTA in male mice (3+3) 14 weeks after tamoxifen treatment
Additional data files:

all_anova.xlsx - summary of two-way ANOVAs of all behavioral tests and weight measurements for males and females
catwalk_complete.xlsx - CatWalk complete dataset with datapoints
catwalk_correlation_between_paws.xlsx - correlation coefficients of CatWalk parameters between the left and right paws
catwalk_reduced.xlsx - CatWalk parameters used in linear regression model reduction of data
intelli.xlsx - IntelliCage data summarized in bins
oss.xlsx - operant sensation-seeking data
v2 contains the corrected 'animals_info.csv' file without an unnecessary column.
v3 has a revised version of file containing females' weight measurements and also added a file with midbrain cell counts
v4 has a whole section of 'Additional data files' added
DOI: 10.5281/zenodo.10104652
title: plethodon_ventralis_decontaminated_FSCR.fasta.gz

description: plethodon_ventralis_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.10101247
title: Simulated data sets with different SNRs and NCEs for PIPI2

description: The simulated data sets used in PIPI2 generated by AlphaPeptDeep.
Zeng, Wen-Feng, Xie-Xuan Zhou, Sander Willems, Constantin Ammar, Maria Wahle, Isabell Bludau, Eugenia Voytik, Maximillian T. Strauss, and Matthias Mann. "AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics." Nature Communications 13, no. 1 (2022): 7238.
DOI: 10.5281/zenodo.10101246
title: Simulated data sets with different SNRs and NCEs for PIPI2

description: The simulated data sets used in PIPI2 generated by AlphaPeptDeep.
Zeng, Wen-Feng, Xie-Xuan Zhou, Sander Willems, Constantin Ammar, Maria Wahle, Isabell Bludau, Eugenia Voytik, Maximillian T. Strauss, and Matthias Mann. "AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics." Nature Communications 13, no. 1 (2022): 7238.
DOI: 10.5281/zenodo.10104651
title: plethodon_ventralis_decontaminated_FSCR.fasta.gz

description: plethodon_ventralis_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.10102416
title: Event-based vision datasets for pedestrian detection in urban scenarios

description: Datasets supporting the Spiking Perception and processing for Intelligent Detection of pEdestrians on urban Roads (SPIDER) Project. The codebase is available on Gihub at https://github.com/th-nuernberg/spider.In Project SPIDER, we propose a novel solution for urban road surveillance using event-based neuromorphic cameras (i.e. Dynamic Vision Sensors – DVS), neural algorithms, and embedded neuromorphic computing platforms (i.e. Brainchip Akida). The solution is described by rapid detection and identification of abnormal activities, typically describing roadside pedestrian and bicyclist dynamics.In order to train the detection system, we decided to use DVS camera events. To also obtain ground truth information we added a traditional CMOS camera on the mount with a preset offset capturing the same field of view. The CMOS frames were only used as support in the labeling process and were not included in the training of the system, the testing, or the evaluation, respectively. We have chosen two locations with different properties of the road, the number of pedestrians and bicyclists, and overall different traffic properties. 
The SPIDER project is an award-winning edge solution for the TinyML Vision Zero San Jose Hackathon. The project placed 2nd among 29 teams in the world in the final. The final pitch is available at https://www.youtube.com/watch?v=ZhBCtfalcOk&t=2872s
 
Dataset archive content:
 
Dataset location 1
• 4 lanes (4 per direction) wide street
• Location: https://goo.gl/maps/JaYGwaTaBHj5H6SL9
• 50 kmh (urban) speed limit
• Near the university campus with Pedestrians (people walking) and bicyclists (people biking, scooting, rolling, etc.)
• Ideal Operating Environment 
Dataset location 2
• 8 lanes (4 per direction) wide street
• Location: https://goo.gl/maps/jar6AjysZiM2LP5S7
• 50 kmh (urban) speed limit
• Near the main train stations of the city and a location with Pedestrians (people walking, running, or jogging), and cyclists (people biking, scooting, rolling, etc.) 
Dataset location 3
• 6 lanes (3 per direction) wide street on the bridge
• Location: https://goo.gl/maps/SEEsmpgmLPcD8fG7A
• 50 kmh (urban) speed limit
• Near ring street of Munich and a location with Pedestrians (people walking, running, or jogging) and bicyclists (people biking, scooting, rolling, etc.)
• Night-time data acquisition
DOI: 10.5281/zenodo.10102415
title: Event-based vision datasets for pedestrian detection in urban scenarios

description: Datasets supporting the Spiking Perception and processing for Intelligent Detection of pEdestrians on urban Roads (SPIDER) Project. The codebase is available on Gihub at https://github.com/th-nuernberg/spider.In Project SPIDER, we propose a novel solution for urban road surveillance using event-based neuromorphic cameras (i.e. Dynamic Vision Sensors – DVS), neural algorithms, and embedded neuromorphic computing platforms (i.e. Brainchip Akida). The solution is described by rapid detection and identification of abnormal activities, typically describing roadside pedestrian and bicyclist dynamics.In order to train the detection system, we decided to use DVS camera events. To also obtain ground truth information we added a traditional CMOS camera on the mount with a preset offset capturing the same field of view. The CMOS frames were only used as support in the labeling process and were not included in the training of the system, the testing, or the evaluation, respectively. We have chosen two locations with different properties of the road, the number of pedestrians and bicyclists, and overall different traffic properties. 
The SPIDER project is an award-winning edge solution for the TinyML Vision Zero San Jose Hackathon. The project placed 2nd among 29 teams in the world in the final. The final pitch is available at https://www.youtube.com/watch?v=ZhBCtfalcOk&t=2872s
 
Dataset archive content:
 
Dataset location 1
• 4 lanes (4 per direction) wide street
• Location: https://goo.gl/maps/JaYGwaTaBHj5H6SL9
• 50 kmh (urban) speed limit
• Near the university campus with Pedestrians (people walking) and bicyclists (people biking, scooting, rolling, etc.)
• Ideal Operating Environment 
Dataset location 2
• 8 lanes (4 per direction) wide street
• Location: https://goo.gl/maps/jar6AjysZiM2LP5S7
• 50 kmh (urban) speed limit
• Near the main train stations of the city and a location with Pedestrians (people walking, running, or jogging), and cyclists (people biking, scooting, rolling, etc.) 
Dataset location 3
• 6 lanes (3 per direction) wide street on the bridge
• Location: https://goo.gl/maps/SEEsmpgmLPcD8fG7A
• 50 kmh (urban) speed limit
• Near ring street of Munich and a location with Pedestrians (people walking, running, or jogging) and bicyclists (people biking, scooting, rolling, etc.)
• Night-time data acquisition
DOI: 10.5255/ukda-sn-856767
title: Survey of plastic and general waste collectors serving off-grid neighbourhoods of Greater Accra, Ghana, 2022

description: This data set aims to assess how far informal collectors facilitate waste separation and recycling in Greater Accra, how waste value varies along waste collection chains, and the challenges facing plastic and general waste collectors. It comprises the results of a questionnaire survey with 182 general waste collectors, plastic waste main collectors, and plastic waste sub-collectors in Greater Accra, Ghana. The waste collector questionnaire survey data set is part of a convergent parallel mixed methods study of plastic and general waste collectors and sub-collectors within Greater Accra, Ghana. In a separate qualitative component, focus group discussions (FGDs) were held with these groups, with a questionnaire survey of waste collectors and sub-collectors administered almost simultaneously. This data set was therefore generated by the cross-sectional questionnaire survey, which covers respondent demographics, business histories, volume and types of waste collected, waste processing, onward fate of waste collected, business finances and challenges faced.
DOI: 10.34740/kaggle/dsv/6938618
title: MoviesDatasets
DOI: 10.34740/kaggle/dsv/6938643
title: Temperature and humidity
DOI: 10.5281/zenodo.10090518
title: WHY Toolkit plug-in tables for PRIMES and TIAM-ECN

description: Plug-in tables for the PRIMES and TIAM-ECN models, generated using the WHY Toolkit. These data represent estimates of energy consumption subdivided by energy service, energy carrier, country, building type, building age, renovation status and temporal unit. The outputs of the WHY toolkit are expressed as energy per building. For each service-carrier combination, the data delivered by the WHY toolkit represent the amount of carrier (in terms of kWh input) that would be needed if this carrier were to fulfill the whole service demand for each combination of building type, construction period and renovation status. A detailed description of the data and how they can be used in energy system models is available in deliverable D4.2 of the WHY project, which can be found in the following webpage: Articles and reports: WHY H2020 Project (why-h2020.eu
DOI: 10.5281/zenodo.10090519
title: WHY Toolkit plug-in tables for PRIMES and TIAM-ECN

description: Plug-in tables for the PRIMES and TIAM-ECN models, generated using the WHY Toolkit. These data represent estimates of energy consumption subdivided by energy service, energy carrier, country, building type, building age, renovation status and temporal unit. The outputs of the WHY toolkit are expressed as energy per building. For each service-carrier combination, the data delivered by the WHY toolkit represent the amount of carrier (in terms of kWh input) that would be needed if this carrier were to fulfill the whole service demand for each combination of building type, construction period and renovation status. A detailed description of the data and how they can be used in energy system models is available in deliverable D4.2 of the WHY project, which can be found in the following webpage: Articles and reports: WHY H2020 Project (why-h2020.eu
DOI: 10.57995/c82x-ty28
title: Chapter 4: OXPHOS protein content changes changes in adult female grey seal blubber during adipose tissue expansion and contraction

description: Seals accumulate large amount of fat in the subcutaneous adipose tissue, which undergoes massive changes during their breeding season. As blubber pO2 is associated with fatness,  this study aims to describe the differences in subcutaneous adipose tissue mitrochondrial protein complexes in relation to hypoxia and metabolism throughout depth during the characteristic blubber contraction that occur in adult female grey seal during the breeding season.

The data was collected as part of the PhD project "Characterising fat tissue structure and function in response to hypoxia in grey seals, an animal model of extreme fat deposition and mobilisation, using novel in vivo and in vitro approaches".
DOI: 10.5281/zenodo.10104839
title: INVESTIGATION OF BIOGAS PRODUCTION AT THE METANTENK INSTALLATION
DOI: 10.5281/zenodo.10104840
title: INVESTIGATION OF BIOGAS PRODUCTION AT THE METANTENK INSTALLATION
DOI: 10.5281/zenodo.10104769
title: poecilia_velifera_decontaminated_FSCR.fasta.gz

description: poecilia_velifera_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.10104770
title: poecilia_velifera_decontaminated_FSCR.fasta.gz

description: poecilia_velifera_decontaminated_FSCR.fasta.gz
DOI: 10.7284/131279
title: ADCP (Acoustic Doppler Current Profiler) data collected during research cruise AE1802 using a Hawaii UHDAS instrument system onboard the platform RV Atlantic Explorer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/131828
title: TSG (Thermosalinograph) data collected during research cruise AE1802 using a SeaBird SBE-21 instrument system onboard the platform RV Atlantic Explorer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.5281/zenodo.5508260
title: TransProteus, Predicting 3D shapes, masks, and properties of materials, liquids, and objects inside transparent containers from images

description: We present TransProteus, a dataset, for predicting the 3D structure and properties of materials, liquids, and objects inside transparent vessels from a single image without prior knowledge of the image source and camera parameters. Manipulating materials in transparent containers is essential in many fields and depends heavily on vision. This work supplies a new procedurally generated dataset consisting of 50k images of liquids and solid objects inside transparent containers. The image annotations include 3D models and material properties (color/transparency/roughness...) for the vessel and its content. The synthetic (CGI) part of the dataset was procedurally generated using 13k different objects, 500 different environments (HDRI), and 1450 material textures (PBR) combined with simulated liquids and procedurally generated vessels. In addition, we supply 104 real-world images of objects inside transparent vessels with depth maps of both the vessel and its content.


Note that there are two files here:


Transproteus_SimulatedLiquids2_New_No_Shift.7z


and



TranProteus2.7z , contain subset of the virtual CGI data set.


https://zenodo.org/api/files/12b013ca-36be-4156-afd4-c93b5fa22093/Tansproteus_SimulatedLiquids2_New_No_Shift.7z


TransProteus_RealSense_RealPhotos.7z : Contain real-world photos scanned with real sense with depth map of both the vessel and its content


See ReadMe file in side the downloaded files for more details


The full dataset (>100gb) can be found here:


https://e.pcloud.link/publink/show?code=kZfx55Zx1GOrl4aUwXDrifAHUPSt7QUAIfV


https://icedrive.net/1/6cZbP5dkNG


See:  https://arxiv.org/pdf/2109.07577.pdf for more details


**This dataset is complementary to LabPics dataset with 8k real images of materials in vessels in chemistry labs, medical labs, and other settings. The LabPics dataset can be downloaded from here:


https://zenodo.org/record/4736111#.YVOAx3tE1H4


 


************************************************************************************


Transproteus_SimulatedLiquids2_New_No_Shift.7z and TranProteus2.7z


The two folders contain relatively similar data styles.
The data in No_Shift contain images that were generated with no camera shift in the camera paramters. If you try to predict 3d model from an image as a depth map, this is easier to use (Otherwise, you need to adapt the image using the shift). For all other purposes, both folders are the same, and you can use either or both. In addition, a  real image dataset for testing is given in the RealSense file.
DOI: 10.5281/zenodo.5508261
title: TransProteus, Predicting 3D shapes, masks, and properties of materials, liquids, and objects inside transparent containers from images

description: We present TransProteus, a dataset, for predicting the 3D structure and properties of materials, liquids, and objects inside transparent vessels from a single image without prior knowledge of the image source and camera parameters. Manipulating materials in transparent containers is essential in many fields and depends heavily on vision. This work supplies a new procedurally generated dataset consisting of 50k images of liquids and solid objects inside transparent containers. The image annotations include 3D models and material properties (color/transparency/roughness...) for the vessel and its content. The synthetic (CGI) part of the dataset was procedurally generated using 13k different objects, 500 different environments (HDRI), and 1450 material textures (PBR) combined with simulated liquids and procedurally generated vessels. In addition, we supply 104 real-world images of objects inside transparent vessels with depth maps of both the vessel and its content.


Note that there two files here:
TranProteus2.7z , contain subset of the virtual CGI data set.


TransProteus_RealSense_RealPhotos.7z : Contain real-world photos scanned with real sense with depth map of both the vessel and its content


See ReadMe file in side the downloaded files for more details


The full dataset (>100gb) can be found here:


https://e.pcloud.link/publink/show?code=kZfx55Zx1GOrl4aUwXDrifAHUPSt7QUAIfV


https://icedrive.net/1/6cZbP5dkNG


See:  https://arxiv.org/pdf/2109.07577.pdf for more details


**This dataset is complementary to LabPics dataset with 8k real images of materials in vessels in chemistry labs, medical labs, and other settings. The LabPics dataset can be downloaded from here:


https://zenodo.org/record/4736111#.YVOAx3tE1H4
DOI: 10.5281/zenodo.5839045
title: TransProteus, Predicting 3D shapes, masks, and properties of materials, liquids, and objects inside transparent containers from images

description: We present TransProteus, a dataset, for predicting the 3D structure and properties of materials, liquids, and objects inside transparent vessels from a single image without prior knowledge of the image source and camera parameters. Manipulating materials in transparent containers is essential in many fields and depends heavily on vision. This work supplies a new procedurally generated dataset consisting of 50k images of liquids and solid objects inside transparent containers. The image annotations include 3D models and material properties (color/transparency/roughness...) for the vessel and its content. The synthetic (CGI) part of the dataset was procedurally generated using 13k different objects, 500 different environments (HDRI), and 1450 material textures (PBR) combined with simulated liquids and procedurally generated vessels. In addition, we supply 104 real-world images of objects inside transparent vessels with depth maps of both the vessel and its content. Note that there are two files here: Transproteus_SimulatedLiquids2_New_No_Shift.7z and <br> TranProteus2.7z , contain subset of the virtual CGI data set. https://zenodo.org/api/files/12b013ca-36be-4156-afd4-c93b5fa22093/Tansproteus_SimulatedLiquids2_New_No_Shift.7z TransProteus_RealSense_RealPhotos.7z : Contain real-world photos scanned with real sense with depth map of both the vessel and its content See ReadMe file in side the downloaded files for more details The full dataset (&gt;100gb) can be found here: https://e.pcloud.link/publink/show?code=kZfx55Zx1GOrl4aUwXDrifAHUPSt7QUAIfV https://icedrive.net/1/6cZbP5dkNG See: https://arxiv.org/pdf/2109.07577.pdf for more details <strong>**This dataset is complementary to LabPics dataset with 8k real images of materials in vessels in chemistry labs, medical labs, and other settings. The LabPics dataset can be downloaded from here:</strong> <strong>https://zenodo.org/record/4736111#.YVOAx3tE1H4</strong> <strong>************************************************************************************</strong> Transproteus_SimulatedLiquids2_New_No_Shift.7z and TranProteus2.7z The two folders contain relatively similar data styles.<br> The data in No_Shift contain images that were generated with no camera shift in the camera paramters. If you try to predict 3d model from an image as a depth map, this is easier to use (Otherwise, you need to adapt the image using the shift). For all other purposes, both folders are the same, and you can use either or both. In addition, a real image dataset for testing is given in the RealSense file.