cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.ex9z5b
title: Occurrence Download

description: A dataset containing 3586 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Prunus phaeosticta (Hance) Maxim.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 3586 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022126-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21233/c6gp-a864
title: Assiniboine River ostracode surface sample dataset

description: Raw data for the Assiniboine River ostracode surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/q9h1-qs02
title: SEQU6 pollen surface sample dataset

description: Raw data for the SEQU6 pollen surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.15468/dl.gszmdf
title: Occurrence Download

description: A dataset containing 1653 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Chile",
 "Geometry POLYGON((-71.6855 -33.095,-71.24855 -33.095,-71.24855 -32.8134,-71.6855 -32.8134,-71.6855 -33.095))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Turdus falcklandii Quoy & Gaimard, 1824",
 "Year is 2019"
 ]
}

 The dataset includes 1653 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022127-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21233/7gts-3j65
title: Grannis Island vertebrate fauna dataset

description: Raw data for the Grannis Island vertebrate fauna dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/0amh-6k36
title: Lake Biwa, South Basin ostracode surface sample dataset

description: Raw data for the Lake Biwa, South Basin ostracode surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.5281/zenodo.15367305
title: Reddit Sentiment VS Stock Price

description: Overall, this project was meant test the relationship between social media posts and their short-term effect on stock prices. We decided to use Reddit posts from financial specific subreddit communities like r/wallstreetbets, r/investing, and r/stocks to see the changes in the market associated with a variety of posts made by users. This idea came to light because of the GameStop short squeeze that showed the power of social media in the market. Typically, stock prices should purely represent the total present value of all the future value of the company, but the question we are asking is whether social media can impact that intrinsic value. Our research question was known from the start and it was do Reddit posts for or against a certain stock provide insight into how the market will move in a short window. To solve this problem, we selected five large tech companies including Apple, Tesla, Amazon, Microsoft, and Google. These companies would likely give us more data in the subreddits and would have less volatility day to day allowing us to simulate an experiment easier. They trade at very high values so a change from a Reddit post would have to be significant giving us proof that there is an effect.

Next, we had to choose our data sources for to have data to test with. First, we tried to locate the Reddit data using a Reddit API, but due to circumstances regarding Reddit requiring approval to use their data we switched to a Kaggle dataset that contained metadata from Reddit. For our second data set we had planned to use Yahoo Finance through yfinance, but due to the large amount of data we were pulling from this public API our IP address was temporarily blocked. This caused us to switch our second data to pull from Alpha Vantage. While this was a large switch in the public it was a minor roadblock and fixing the Finance pulling section allowed for everything else to continue to work in succession. Once we had both of our datasets programmatically pulled into our local vs code, we implemented a pipeline to clean, merge, and analyze all the data. At the end, we implement a Snakemake workflow to ensure the project was easily reproducible. To continue, we utilized Textblob to label our Reddit posts with a sentiment value of positive, negative, or neutral and provide us with a correlation value to analyze with. We then matched the time frame of each post with the stock data and computed any possible changes, found a correlation coefficient, and graphed our findings.

To conclude the data analysis, we found that there is relatively small or no correlation between the total companies, but Microsoft and Google do show stronger correlations when analyzed on their own. However, this may be due to other circumstances like why the post was made or if the market had other trends on those dates already. A larger analysis with more data from other social media platforms would be needed to conclude for our hypothesis that there is a strong correlation.
DOI: 10.5281/zenodo.15367306
title: Reddit Sentiment VS Stock Price

description: Overall, this project was meant test the relationship between social media posts and their short-term effect on stock prices. We decided to use Reddit posts from financial specific subreddit communities like r/wallstreetbets, r/investing, and r/stocks to see the changes in the market associated with a variety of posts made by users. This idea came to light because of the GameStop short squeeze that showed the power of social media in the market. Typically, stock prices should purely represent the total present value of all the future value of the company, but the question we are asking is whether social media can impact that intrinsic value. Our research question was known from the start and it was do Reddit posts for or against a certain stock provide insight into how the market will move in a short window. To solve this problem, we selected five large tech companies including Apple, Tesla, Amazon, Microsoft, and Google. These companies would likely give us more data in the subreddits and would have less volatility day to day allowing us to simulate an experiment easier. They trade at very high values so a change from a Reddit post would have to be significant giving us proof that there is an effect.

Next, we had to choose our data sources for to have data to test with. First, we tried to locate the Reddit data using a Reddit API, but due to circumstances regarding Reddit requiring approval to use their data we switched to a Kaggle dataset that contained metadata from Reddit. For our second data set we had planned to use Yahoo Finance through yfinance, but due to the large amount of data we were pulling from this public API our IP address was temporarily blocked. This caused us to switch our second data to pull from Alpha Vantage. While this was a large switch in the public it was a minor roadblock and fixing the Finance pulling section allowed for everything else to continue to work in succession. Once we had both of our datasets programmatically pulled into our local vs code, we implemented a pipeline to clean, merge, and analyze all the data. At the end, we implement a Snakemake workflow to ensure the project was easily reproducible. To continue, we utilized Textblob to label our Reddit posts with a sentiment value of positive, negative, or neutral and provide us with a correlation value to analyze with. We then matched the time frame of each post with the stock data and computed any possible changes, found a correlation coefficient, and graphed our findings.

To conclude the data analysis, we found that there is relatively small or no correlation between the total companies, but Microsoft and Google do show stronger correlations when analyzed on their own. However, this may be due to other circumstances like why the post was made or if the market had other trends on those dates already. A larger analysis with more data from other social media platforms would be needed to conclude for our hypothesis that there is a strong correlation.
DOI: 10.21233/g3vj-vy74
title: Plaine testate amoebae surface sample dataset

description: Raw data for the Plaine testate amoebae surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.5281/zenodo.15367267
title: Conjunto de dados de intensificação na língua portuguesa por construções com unidades de medida, por Déborah Ribeiro de Alvarenga

description: A planilha oferece um conjunto de dados no português do Brasil em que construções intensificadoras, formadas por verbo + unidade de medida (correr quilômetros, chorar rios), foram usadas em textos disponíveis em mídia digital, retirados dos gerenciadores de corpora Sketch Engine e Corpus do Português. São, portanto, exemplos do uso real da língua portuguesa, sem qualquer correção ou adaptação da versão original. Além dos dados, a planilha conta como uma análise que leva em consideração fatores como: categorização dos verbos, animacidade do sujeito projetado pelo verbo, classificação desse sujeito e quantidade de intensificadores. 

Os dados foram usados no trabalho de Déborah Ribeiro de Alvarenga (2025), entitulado "BEYOND MEASURES: DISTANCE AND VOLUME IN INTENSIFYING CONSTRUCTIONS IN BRAZILIAN PORTUGUESE" ('ALÉM DAS MEDIDAS: DISTÂNCIA E VOLUME EM CONSTRUÇÕES INTENSIFICADORAS NO PORTUGUÊS BRASILEIRO'), publicado na revista Diadorim.  

 

The spreadsheet offers a set of data in Brazilian Portuguese in which intensifying constructions, made up of a verb + unit of measurement (run for kilometers, cry rivers), were used in texts available on digital media, taken from the corpora managers Sketch Engine and Corpus do Português. They are therefore examples of real Portuguese usage, without any correction or adaptation of the original version. In addition to the data, the spreadsheet includes an analysis that takes into account factors such as: verb categorization, the animacy of the subject projected by the verb, the classification of this subject and the number of intensifiers. 

The data was used in the work by Déborah Ribeiro de Alvarenga (2025), entitled "BEYOND MEASURES: DISTANCE AND VOLUME IN INTENSIFYING CONSTRUCTIONS IN BRAZILIAN PORTUGUESE" ('ALÉM DAS MEDIDAS: DISTÂNCIA E VOLUME EM CONSTRUÇÕES INTENSIFICADORAS NO PORTUGUÊS BRASILEIRO'), published in the journal Diadorim.  
description: The spreadsheet offers a set of data in Brazilian Portuguese in which intensifying constructions, made up of a verb + unit of measurement (run for kilometers, cry rivers), were used in texts available on digital media, taken from the corpora managers Sketch Engine and Corpus do Português. They are therefore examples of real Portuguese usage, without any correction or adaptation of the original version. In addition to the data, the spreadsheet includes an analysis that takes into account factors such as: verb categorization, the animacy of the subject projected by the verb, the classification of this subject and the number of intensifiers. 

The data was used in the work by Déborah Ribeiro de Alvarenga (2025), entitled "BEYOND MEASURES: DISTANCE AND VOLUME IN INTENSIFYING CONSTRUCTIONS IN BRAZILIAN PORTUGUESE" ('ALÉM DAS MEDIDAS: DISTÂNCIA E VOLUME EM CONSTRUÇÕES INTENSIFICADORAS NO PORTUGUÊS BRASILEIRO'), published in the journal Diadorim.
DOI: 10.5281/zenodo.15367268
title: Conjunto de dados de intensificação na língua portuguesa por construções com unidades de medida, por Déborah Ribeiro de Alvarenga

description: A planilha oferece um conjunto de dados no português do Brasil em que construções intensificadoras, formadas por verbo + unidade de medida (correr quilômetros, chorar rios), foram usadas em textos disponíveis em mídia digital, retirados dos gerenciadores de corpora Sketch Engine e Corpus do Português. São, portanto, exemplos do uso real da língua portuguesa, sem qualquer correção ou adaptação da versão original. Além dos dados, a planilha conta como uma análise que leva em consideração fatores como: categorização dos verbos, animacidade do sujeito projetado pelo verbo, classificação desse sujeito e quantidade de intensificadores. 

Os dados foram usados no trabalho de Déborah Ribeiro de Alvarenga (2025), entitulado "BEYOND MEASURES: DISTANCE AND VOLUME IN INTENSIFYING CONSTRUCTIONS IN BRAZILIAN PORTUGUESE" ('ALÉM DAS MEDIDAS: DISTÂNCIA E VOLUME EM CONSTRUÇÕES INTENSIFICADORAS NO PORTUGUÊS BRASILEIRO'), publicado na revista Diadorim.  

 

The spreadsheet offers a set of data in Brazilian Portuguese in which intensifying constructions, made up of a verb + unit of measurement (run for kilometers, cry rivers), were used in texts available on digital media, taken from the corpora managers Sketch Engine and Corpus do Português. They are therefore examples of real Portuguese usage, without any correction or adaptation of the original version. In addition to the data, the spreadsheet includes an analysis that takes into account factors such as: verb categorization, the animacy of the subject projected by the verb, the classification of this subject and the number of intensifiers. 

The data was used in the work by Déborah Ribeiro de Alvarenga (2025), entitled "BEYOND MEASURES: DISTANCE AND VOLUME IN INTENSIFYING CONSTRUCTIONS IN BRAZILIAN PORTUGUESE" ('ALÉM DAS MEDIDAS: DISTÂNCIA E VOLUME EM CONSTRUÇÕES INTENSIFICADORAS NO PORTUGUÊS BRASILEIRO'), published in the journal Diadorim.  
description: The spreadsheet offers a set of data in Brazilian Portuguese in which intensifying constructions, made up of a verb + unit of measurement (run for kilometers, cry rivers), were used in texts available on digital media, taken from the corpora managers Sketch Engine and Corpus do Português. They are therefore examples of real Portuguese usage, without any correction or adaptation of the original version. In addition to the data, the spreadsheet includes an analysis that takes into account factors such as: verb categorization, the animacy of the subject projected by the verb, the classification of this subject and the number of intensifiers. 

The data was used in the work by Déborah Ribeiro de Alvarenga (2025), entitled "BEYOND MEASURES: DISTANCE AND VOLUME IN INTENSIFYING CONSTRUCTIONS IN BRAZILIAN PORTUGUESE" ('ALÉM DAS MEDIDAS: DISTÂNCIA E VOLUME EM CONSTRUÇÕES INTENSIFICADORAS NO PORTUGUÊS BRASILEIRO'), published in the journal Diadorim.
DOI: 10.21233/btb2-x674
title: Antelope Lakes ostracode surface sample dataset

description: Raw data for the Antelope Lakes ostracode surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/kxpn-7t24
title: Telfer Lake diatom surface sample dataset

description: Raw data for the Telfer Lake diatom surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/7ae8-8z14
title: Monroe Lake water chemistry dataset

description: Raw data for the Monroe Lake water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/jsds-4777
title: Tanghongling testate amoebae surface sample dataset

description: Raw data for the Tanghongling testate amoebae surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/a364-1q03
title: Surmene Agacbasi Yaylasi testate amoebae surface sample dataset

description: Raw data for the Surmene Agacbasi Yaylasi testate amoebae surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.15468/dl.efpwj6
title: Occurrence Download

description: A dataset containing 79 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Prunus trichantha Koehne)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 79 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022128-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21233/8kqn-s191
title: LDDsite2292 ostracode surface sample dataset

description: Raw data for the LDDsite2292 ostracode surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.5281/zenodo.15367354
title: Spatial Dataset from a Nebraska Research Farm, 2022

description: Spatial dataset for an irrigated field at the University of Nebraska-Lincoln Eastern Nebraska Research and Extension Center near Mead, Nebraska.  Dataset includes maps of yield and planting density for the 2022 growing season.
description: This work is funded in part through the U.S. Department of Energy ARPA-E program under Cooperative Agreement DE-AR0001226, and is led by the Daugherty Water for Food Global Institute (DWFI) at the University of Nebraska.

Restricted Access description:

To gain access, users must accompany the request with written (1) justification to access the data, (2) agreement to acknowledge the Daugherty Water for Food Global Institute at the University of Nebraska (DWFI) and the Department of Energy ARPA-E program in any resulting publications, and (3) confirmation that the data will be held confidentially and will not be shared or published without written consent of DWFI.
DOI: 10.5281/zenodo.15367355
title: Spatial Dataset from a Nebraska Research Farm, 2022

description: Spatial dataset for an irrigated field at the University of Nebraska-Lincoln Eastern Nebraska Research and Extension Center near Mead, Nebraska.  Dataset includes maps of yield and planting density for the 2022 growing season.
description: This work is funded in part through the U.S. Department of Energy ARPA-E program under Cooperative Agreement DE-AR0001226, and is led by the Daugherty Water for Food Global Institute (DWFI) at the University of Nebraska.

Restricted Access description:

To gain access, users must accompany the request with written (1) justification to access the data, (2) agreement to acknowledge the Daugherty Water for Food Global Institute at the University of Nebraska (DWFI) and the Department of Energy ARPA-E program in any resulting publications, and (3) confirmation that the data will be held confidentially and will not be shared or published without written consent of DWFI.
DOI: 10.21233/8mtg-kh21
title: Swiss Trap A2 pollen surface sample dataset

description: Raw data for the Swiss Trap A2 pollen surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/n065-d624
title: Head of Little Cedar Locality [CR 107] vertebrate fauna dataset

description: Raw data for the Head of Little Cedar Locality [CR 107] vertebrate fauna dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.15468/dl.gkwd7p
title: Occurrence Download

description: A dataset containing 208181 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "TaxonKey is Icterus gularis (Wagler, 1829)"
 ]
}

 The dataset includes 208181 records from 84 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022117-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21233/366s-nk29
title: Bílé Karpaty 2 pollen surface sample dataset

description: Raw data for the Bílé Karpaty 2 pollen surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.15468/dl.wxh8vc
title: Occurrence Download

description: A dataset containing 1412 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Chile",
 "Geometry POLYGON((-71.6855 -33.095,-71.24855 -33.095,-71.24855 -32.8134,-71.6855 -32.8134,-71.6855 -33.095))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Turdus falcklandii Quoy & Gaimard, 1824",
 "Year is 2018"
 ]
}

 The dataset includes 1412 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022129-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.