cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/e0nr-m455
title: 6.66 kVA Solar Optimizer

description: This dataset is in support of my original research paper " 6.66kVA Solar Optimizer " submitted to "IEEE Transactions on Energy Conversion". (https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=60). Paper is under review.This is not connected to the grid. The other related dataset " " (https://dx.doi.org/10.21227/v7s8-pk56 ), where this will be connected to the inverter which in turn will be connected to grid.Though few similar kind papers are there, this paper is original and different from them as evident from the abstract and the transaction paper , and is based on the actual submitted project for funding. ( project didn't got any funding ; though from self finances, submission was acceptable !)Abstract of the above submitted paper
DOI: 10.6084/m9.figshare.13125170
title: Herring Gull Abundance in parking lots with/without automobile vehicles

description: A field experiment was conducted from October 15,2020 to October 17,2020 about how the prevalence of appearance of Herring Gulls in the parking lot is affected by the presence of cars in the parking lot. The three specific locations that were selected are big parking lots that have an open area located within Mississauga Heartland that are about 1km-2 km apart from each other. The optimal time at which the parking lots at the three different locations were empty from cars was between the time of 7 am – 8am and the time the parking lot was full of cars was between 3 pm-4pm. On October 15,2020 I would arrive at location #1 (43.616004N, -79.701848W) at 7 am and observe how many Herring Gulls I can see within my field of view at the parking lot for about 8-10 minutes. Then I would drive to location number two ( 43°34'46.7"N 79°44'02.2"W) and arrive by 7:15 am and I would observe how many Herring Gulls I can spot within my field of view at the parking lot for about 8-10 minutes. I would go to the third location (43.599588, -79.712981) and perform the same exact procedure I did for location one and two. Then later throughout the day, I would arrive at location one at 3pm, location two at 3:15 pm and location three at 3:30 pm and takes notes about how many Herring Gulls I spot at the parking lot within the 8-10 minutes I am there. I repeated the following procedure on October 16,2020 and October 17,2020 to get replicate measurement for data. Before performing the field study, perform a pilot study to make sure that all the parking lots chosen to contain a restaurant chain and a garbage can within the plaza. Also make sure that the 3-day period weather forecast is between 11 – 15 degree with no rain. A car was used to get from location one to location two and three.
DOI: 10.6084/m9.figshare.13125170.v3
title: Herring Gull Abundance in parking lots with/without automobile vehicles

description: A field experiment was conducted from October 15,2020 to October 17,2020 about how the prevalence of appearance of Herring Gulls in the parking lot is affected by the presence of cars in the parking lot. The three specific locations that were selected are big parking lots that have an open area located within Mississauga Heartland that are about 1km-2 km apart from each other. The optimal time at which the parking lots at the three different locations were empty from cars was between the time of 7 am – 8am and the time the parking lot was full of cars was between 3 pm-4pm. On October 15,2020 I would arrive at location #1 (43.616004N, -79.701848W) at 7 am and observe how many Herring Gulls I can see within my field of view at the parking lot for about 8-10 minutes. Then I would drive to location number two ( 43°34'46.7"N 79°44'02.2"W) and arrive by 7:15 am and I would observe how many Herring Gulls I can spot within my field of view at the parking lot for about 8-10 minutes. I would go to the third location (43.599588, -79.712981) and perform the same exact procedure I did for location one and two. Then later throughout the day, I would arrive at location one at 3pm, location two at 3:15 pm and location three at 3:30 pm and takes notes about how many Herring Gulls I spot at the parking lot within the 8-10 minutes I am there. I repeated the following procedure on October 16,2020 and October 17,2020 to get replicate measurement for data. Before performing the field study, perform a pilot study to make sure that all the parking lots chosen to contain a restaurant chain and a garbage can within the plaza. Also make sure that the 3-day period weather forecast is between 11 – 15 degree with no rain. A car was used to get from location one to location two and three.
DOI: 10.6084/m9.figshare.13125170.v2
title: Herring Gull Abundance in parking lots with/without automobile vehicles

description: A field experiment was conducted from October 15,2020 to October 17,2020 about how the prevalence of appearance of Herring Gulls in the parking lot is affected by the presence of cars in the parking lot. The three specific locations that were selected are big parking lots that have an open area located within Mississauga Heartland that are about 1km-2 km apart from each other. The optimal time at which the parking lots at the three different locations were empty from cars was between the time of 7 am – 8am and the time the parking lot was full of cars was between 3 pm-4pm. On October 15,2020 I would arrive at location #1 (43.616004N, -79.701848W) at 7 am and observe how many Herring Gulls I can see within my field of view at the parking lot for about 8-10 minutes. Then I would drive to location number two ( 43°34'46.7"N 79°44'02.2"W) and arrive by 7:15 am and I would observe how many Herring Gulls I can spot within my field of view at the parking lot for about 8-10 minutes. I would go to the third location (43.599588, -79.712981) and perform the same exact procedure I did for location one and two. Then later throughout the day, I would arrive at location one at 3pm, location two at 3:15 pm and location three at 3:30 pm and takes notes about how many Herring Gulls I spot at the parking lot within the 8-10 minutes I am there. I repeated the following procedure on October 16,2020 and October 17,2020 to get replicate measurement for data. Before performing the field study, perform a pilot study to make sure that all the parking lots chosen to contain a restaurant chain and a garbage can within the plaza. Also make sure that the 3-day period weather forecast is between 11 – 15 degree with no rain. A car was used to get from location one to location two and three.
DOI: 10.6084/m9.figshare.13227560.v1
title: All song strings for LSI analysis from Migratory insights from singing humpback whales recorded around central New Zealand

description: All transcribed song strings that were entered into LSI analysis
DOI: 10.6084/m9.figshare.13227554
title: Random Forest confusion matrix from Migratory insights from singing humpback whales recorded around central New Zealand

description: Confusion matrix arising from song unit classification using Random Forest
DOI: 10.6084/m9.figshare.13227566
title: Theme Similarity Matrix, songs from Migratory insights from singing humpback whales recorded around central New Zealand

description: Within-group and between-group similarities between song recordings
DOI: 10.6084/m9.figshare.13227554.v1
title: Random Forest confusion matrix from Migratory insights from singing humpback whales recorded around central New Zealand

description: Confusion matrix arising from song unit classification using Random Forest
DOI: 10.6084/m9.figshare.13227560
title: All song strings for LSI analysis from Migratory insights from singing humpback whales recorded around central New Zealand

description: All transcribed song strings that were entered into LSI analysis
DOI: 10.6084/m9.figshare.13227566.v1
title: Theme Similarity Matrix, songs from Migratory insights from singing humpback whales recorded around central New Zealand

description: Within-group and between-group similarities between song recordings
DOI: 10.6084/m9.figshare.13227569
title: All phrase strings for LSI analysis from Migratory insights from singing humpback whales recorded around central New Zealand

description: All transcribed phrase strings that were entered into LSI analysis
DOI: 10.6084/m9.figshare.13227581
title: Phrase information for Dice analysis from Migratory insights from singing humpback whales recorded around central New Zealand

description: Presence/absence information regarding phrase types per transcribed song for Dice analysis
DOI: 10.6084/m9.figshare.13227569.v1
title: All phrase strings for LSI analysis from Migratory insights from singing humpback whales recorded around central New Zealand

description: All transcribed phrase strings that were entered into LSI analysis
DOI: 10.6084/m9.figshare.13227575
title: Theme Similarity Matrix, phrases from Migratory insights from singing humpback whales recorded around central New Zealand

description: Within-group and between-group similarities between phrase types
DOI: 10.6084/m9.figshare.13227575.v1
title: Theme Similarity Matrix, phrases from Migratory insights from singing humpback whales recorded around central New Zealand

description: Within-group and between-group similarities between phrase types
DOI: 10.6084/m9.figshare.13227581.v1
title: Phrase information for Dice analysis from Migratory insights from singing humpback whales recorded around central New Zealand

description: Presence/absence information regarding phrase types per transcribed song for Dice analysis
DOI: 10.6084/m9.figshare.13240335
title: Additional file 4 of Transductomics: sequencing-based detection and analysis of transduced DNA in pure cultures and microbial communities

description: Additional file 4: Table S2.
DOI: 10.6084/m9.figshare.13240335.v1
title: Additional file 4 of Transductomics: sequencing-based detection and analysis of transduced DNA in pure cultures and microbial communities

description: Additional file 4: Table S2.
DOI: 10.21227/2cxa-mx64
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains IDs and sentiment scores of the geo-tagged tweets related to the COVID-19 pandemic. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location.The paper associated with this dataset is available here: Design and analysis of a large-scale COVID-19 tweets dataset-------------------------------------Related: Coronavirus (COVID-19) Tweets Sentiment Trend (Global), Coronavirus (COVID-19) Tweets Dataset and Tweets Originating from India During COVID-19 Lockdowns 1, 2, 3, 4-------------------------------------Below is the quick overview of this dataset.— Dataset name: GeoCOV19Tweets Dataset— Number of tweets : 262,895 tweets— Coverage : Global— Language : English (EN)— Dataset usage terms : By using this dataset, you agree to (i) use the content of this dataset and the data generated from the content of this dataset for non-commercial research only, (ii) remain in compliance with Twitter's Developer Policy and (iii) cite the following paper:Lamsal, R. Design and analysis of a large-scale COVID-19 tweets dataset. Applied Intelligence (2020). https://doi.org/10.1007/s10489-020-02029-z— Primary dataset : Coronavirus (COVID-19) Tweets Dataset (COV19Tweets Dataset)— Dataset updates : Everyday— Active keywords and hashtags: keywords.tsvPlease visit this page (primary dataset) for details regarding the collection date and time (and other notes) of each CSV file present in this dataset.
DOI: 10.21227/x5sr-hg38
title: 25 kVA Wind-Part 2

description: This dataset is in support of my 2 original Research Papers - (i) " -Connected Wind - Part 1 " and (ii) Part 2 ", submitted to "IEEE Transactions on Energy Conversion". (https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=60)These experiments are different from the exisiting approaches in the following ways(a) Simulation takes into account non-linearity viz, damper is non- linear, torque is not constant, backlash from the gears(b)(c)(d)(e)This dataset is related toThis simulation is designed taken into consideration the standards for the India, Delhi conditions, where this wind turbine will get conneced to the substation of the local DISCOM,viz., BSES (Bombay Suburban Electric Supply) Rajdhani Power Limited.Abstract of the above submitted paper
DOI: 10.6084/m9.figshare.13240586
title: MONK PARAKEET ENVIRONMENTAL DATA.xlsx

description: This table contains positivity rates to Cryptosporidium sp. and Giardia sp. in tested nestlings and environmental data registered at monk parakeet nesting sites.
DOI: 10.6084/m9.figshare.13240586.v1
title: MONK PARAKEET ENVIRONMENTAL DATA.xlsx

description: This table contains positivity rates to Cryptosporidium sp. and Giardia sp. in tested nestlings and environmental data registered at monk parakeet nesting sites.
DOI: 10.17632/ymx7mjwpkp.4
title: Data for: New Nanocarried Phenobarbital Formulation: Maintains Better Control of Pentylenetetrazole-Induced Seizures.

description: Other files were not accepted for upload, and a message appears: “Something went wrong”.
DOI: 10.6084/m9.figshare.12894536
title: Discerning Lunar Pyroclastic and Impact Glasses via Raman Spectroscopy

description: <b>The measured geochemical data and Raman spectra data of lunar glasses</b><br>
DOI: 10.6084/m9.figshare.12894536.v1
title: New diagnostic method to discern lunar pyroclastic and impact glasses via Raman spectroscopy

description: <b>The measured geochemical data and Raman spectra data of lunar glasses</b><br>
DOI: 10.6084/m9.figshare.13240338
title: Additional file 5 of Transductomics: sequencing-based detection and analysis of transduced DNA in pure cultures and microbial communities

description: Additional file 5: Table S3.
DOI: 10.6084/m9.figshare.13240332
title: Additional file 3 of Transductomics: sequencing-based detection and analysis of transduced DNA in pure cultures and microbial communities

description: Additional file 3: Table S1.
DOI: 10.6084/m9.figshare.13240338.v1
title: Additional file 5 of Transductomics: sequencing-based detection and analysis of transduced DNA in pure cultures and microbial communities

description: Additional file 5: Table S3.
DOI: 10.6084/m9.figshare.13240332.v1
title: Additional file 3 of Transductomics: sequencing-based detection and analysis of transduced DNA in pure cultures and microbial communities

description: Additional file 3: Table S1.
DOI: 10.6084/m9.figshare.13240727
title: rspb20202063supp2 from Species richness promotes ecosystem carbon storage: evidence from biodiversity-ecosystem functioning experiments

description: Plant diversity has a strong impact on a plethora of ecosystem functions and services, especially ecosystem carbon (C) storage. However, the potential context-dependency of biodiversity effects across ecosystem types, environmental conditions and carbon pools remains largely unknown. In this study, we performed a meta-analysis by collecting data from 95 biodiversity-ecosystem functioning (BEF) studies across 60 sites to explore the effects of plant diversity on different C pools, including aboveground and belowground plant biomass, soil microbial biomass C and soil C content across different ecosystem types. The results showed that ecosystem C storage was significantly enhanced by plant diversity, with stronger effects on aboveground biomass than on soil C content. Moreover, the response magnitudes of ecosystem C storage increased with the level of species richness and experimental duration across all ecosystems. The effects of plant diversity were more pronounced in grasslands than in forests. Furthermore, the effects of plant diversity on belowground plant biomass increased with aridity index in grasslands and forests, suggesting that climate change might modulate biodiversity effects, which are stronger under wetter conditions but weaker under more arid conditions. Taken together, these results provide novel insights into the important role of plant diversity in ecosystem C storage across critical C pools, ecosystem types and environmental contexts.
DOI: 10.6084/m9.figshare.13240727.v1
title: rspb20202063supp2 from Species richness promotes ecosystem carbon storage: evidence from biodiversity-ecosystem functioning experiments

description: Plant diversity has a strong impact on a plethora of ecosystem functions and services, especially ecosystem carbon (C) storage. However, the potential context-dependency of biodiversity effects across ecosystem types, environmental conditions and carbon pools remains largely unknown. In this study, we performed a meta-analysis by collecting data from 95 biodiversity-ecosystem functioning (BEF) studies across 60 sites to explore the effects of plant diversity on different C pools, including aboveground and belowground plant biomass, soil microbial biomass C and soil C content across different ecosystem types. The results showed that ecosystem C storage was significantly enhanced by plant diversity, with stronger effects on aboveground biomass than on soil C content. Moreover, the response magnitudes of ecosystem C storage increased with the level of species richness and experimental duration across all ecosystems. The effects of plant diversity were more pronounced in grasslands than in forests. Furthermore, the effects of plant diversity on belowground plant biomass increased with aridity index in grasslands and forests, suggesting that climate change might modulate biodiversity effects, which are stronger under wetter conditions but weaker under more arid conditions. Taken together, these results provide novel insights into the important role of plant diversity in ecosystem C storage across critical C pools, ecosystem types and environmental contexts.
DOI: 10.6084/m9.figshare.13240733
title: Data

description: The data was used for the major conclusion (table 4 and figure 2) in the paper.
DOI: 10.6084/m9.figshare.13240733.v1
title: Data

description: The data was used for the major conclusion (table 4 and figure 2) in the paper.
DOI: 10.6084/m9.figshare.13240730
title: Codebook of the analyzed data

description: The data was used for the major conclusion (table 4 and figure 2) in the paper.
DOI: 10.6084/m9.figshare.13240730.v1
title: Codebook of the analyzed data

description: The data was used for the major conclusion (table 4 and figure 2) in the paper.
DOI: 10.21227/tap2-vj38
title: Airplane product-passenger Dataset description

description: Airplane product-passenger Dataset
DOI: 10.21227/xvqe-1798
title: Airplane product-passenger Dataset description

description: Airplane product-passenger Dataset
DOI: 10.21227/n0my-wc25
title: Airplane product-passenger Dataset description

description: Airplane product-passenger Dataset
DOI: 10.7910/dvn/dnuef5
title: Bridge Crack Library

description: The bridge crack library (BCL) was an image dataset established to support the deep learning-based crack detection research. This dataset was based on 1200 raw images collected from inspection for in-service bridges. It contains 11000 pixel-wise labeled images with 256×256 resolutions. The dataset could be used to the training of deep neural networks for semantic segmentation of crack images.
DOI: 10.6084/m9.figshare.13240736
title: Methodological orientations, academic citations, and scientific collaboration in applied linguistics

description: Methodological approaches and methods used in 3992 articles published between 2009 and 2018 in 18 leading applied linguistics journal. Research approach, methodology, collaboration and a few other characteristics of the articles were coded manually.
DOI: 10.6084/m9.figshare.12622721
title: Supplementary files for miRNA expression profiles of pluripotent and somatic cells and identification of cell type-specific miRNAs in pigs

description: This dataset consists of the following files:<br>Dataset 1 - Sample description<br>Dataset 2 - Sequencing result<br>Dataset 3 - GO and Kegg analysis<br>Figure 1 - Quality control data of samples<br>Figure 2 - Normalization of samples and heatmaps for sample comparison<br>Figure 3 - Workflow of miRNA-sequencing and data analysis<br>Supplementary file 1 - Detailed protocols
DOI: 10.6084/m9.figshare.12622721.v3
title: Supplementary files for miRNA expression profiles of pluripotent and somatic cells and identification of cell type-specific miRNAs in pigs

description: This dataset consists of the following files:<br>Dataset 1 - Sample description<br>Dataset 2 - Sequencing result<br>Dataset 3 - GO and Kegg analysis<br>Figure 1 - Quality control data of samples<br>Figure 2 - Normalization of samples and heatmaps for sample comparison<br>Figure 3 - Workflow of miRNA-sequencing and data analysis<br>Supplementary file 1 - Detailed protocols
DOI: 10.6084/m9.figshare.12622721.v2
title: Supplementary files for miRNA expression profiles of pluripotent and somatic cells and identification of cell type-specific miRNAs in pigs

description: This dataset consists of the following files:<br>Dataset 1 - Sample description<br>Dataset 2 - Sequencing result<br>Dataset 3 - GO and Kegg analysis<br>Figure s1 - Quality control data of samples<br>Figure s2 - Normalization of samples and heatmaps for sample comparison<br>Figure s3 - Workflow of miRNA-sequencing and data analysis<br>Supplementary file 1 - Detailed protocols
DOI: 10.5281/zenodo.10652341
title: Intracellular neuromelanin from post-mortem human midbrain and pontine.

description: This data set includes different quantification parameters to identify the features of intracellular neuromelanin.
DOI: 10.26036/cnp0004561
title: raw Illumina sequencing data of Pseudourostyla cristata MAC genome and transcriptome

description: raw Illumina sequencing data of Pseudourostyla cristata MAC genome and transcriptome
DOI: 10.25582/data.2023-02.2739655/2295260
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2733658/2295261
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2733369/2295262
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2734113/2295263
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2738933/2295264
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2739489/2295265
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2732938/2295266
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2734130/2295267
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2738500/2295268
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2733519/2295269
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2739006/2295270
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2738245/2295271
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2738978/2295272
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2733821/2295273
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2739185/2295274
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2739312/2295275
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2738444/2295276
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2739463/2295277
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2739539/2295278
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2738410/2295279
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2738483/2295280
title: Data for EMSL Project 51287 from February 2023
DOI: 10.25582/data.2023-02.2737894/2295281
title: Data for EMSL Project 51287 from February 2023