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Influences on charitable giving for conservation: Online survey data of 1,331 respondents across the US, August 2017
This dataset records survey data collected from an online panel of 1,331 anonymous, nationwide U.S respondents. Data collection was both initiated and completed in August 2017. Survey data is of two major types. The first type, information about respondents, includes (1) select background and demographic information; (2) a brief version of a social desirability scale, measured to test and control for potential bias related to socially desirable responding; and (3) a scale developed to measure moral inclusivity, conceptualized as the breadth of an individual’s moral community (i.e., to what extent do different types of entities “count,” in a moral sense). The second type of data records information about an experimental message manipulation featured in the survey. The dataset includes one variable indicating which of seven manipulated textual messages each respondent viewed, along with several variables used as metrics of response to the messages, including (1) attitudes toward the message; (2) hypothetical willingness to donate for the cause promoted in the message; (3) perceived moral salience of the message (i.e., the extent to which it was perceived as a matter of moral concern); (4) manipulation checks, to test whether the manipulated elements of the messages were perceived as intended, and (5) a donation set-up, in which individuals were given the option to donate between $0 and $5 for a conservation organization, from an incentive fee provided by the researchers.
Residential housing segregation and urban tree canopy in 37 US Cities; data in support of Locke et al 2021 in npj Urban Sustainability
Our goal in this paper is to examine whether there are similar patterns in the distribution of tree canopy by Home Owners’ Loan Corporation (HOLC) graded neighborhoods across 37 cities. A pre-print of the paper can be found here: https://osf.io/preprints/socarxiv/97zcs This data packages contains: 1. City-specific file geodatabases with features classes of the HOLC polygons obtained from the Mapping Inequality Project https://dsl.richmond.edu/panorama/redlining/, and tables summarizing tree canopy, and in some cases other land cover classes. 2. An *.R script that replicates all of the analyses, graphs, and tables in the paper. Other double checks, exploratory, and miscellaneous outputs are created by the script too as a bonus. Everything in the paper can be done with the script; additional work outputs are also created. 3. A *.csv file containing city, the HOLC grade, and the percent tree canopy cover. This can be used to create the main findings of the paper and this flat file is provided as an alternative to running the R script to extract information from the geodatabases, combine, and analyze them. The intention is that this file is more widely accessible; the underlying information is the same. Redlining was a racially discriminatory housing policy established by the federal government’s Home Owners’ Loan Corporation (HOLC) during the 1930s. For decades, redlining limited access to homeownership and wealth creation among racial minorities, contributing to a host of adverse social outcomes, including high unemployment, poverty, and residential vacancy, that persist today. While the multigenerational socioeconomic impacts of redlining are increasingly understood, the impacts on urban environments and ecosystems remains unclear. To begin to address this gap, we investigated how the HOLC policy administered 80 years ago may relate to present-day tree canopy at the neighborhood level. Urban trees provide many ecosystem services, mitigate the urban heat island effect
Water flow velocity data, Shark River Slough (SRS) near Frog City, south of US 41, Everglades National Park (FCE LTER) from October 2006 to July 2009
Water velocity data measured every 5 or 15 minutes in Shark River Slough near Frog City jetty, Everglades National Park, using Sontek Agronaut water flow sampler.
LakeBeD-US: Ecology Edition - a benchmark dataset of lake water quality time series and vertical profiles
LakeBeD-US: Ecology Edition is a harmonized lake water quality dataset containing time series and vertical profiles of 21 lakes in the United States monitored by long-term monitoring institutions. These institutions include the North Temperate Lakes Long-Term Ecological Research program (NTL-LTER), Niwot Ridge Long-Term Ecological Research program (NWT-LTER), National Ecological Observatory Network (NEON), and the Carey Lab at Virginia Tech as part of the Virginia Reservoirs Long-Term Research in Environmental Biology (LTREB) site in collaboration with the Western Virginia Water Authority. The data include depth-discrete observations of 17 water quality variables including temperature, dissolved oxygen, chemical properties, Secchi depth, and more. Observations are divided into data collected by automated sensors at a relatively high temporal frequency and manually sampled data at a relatively low temporal frequency. All data were collected in situ. The data are available as Apache Parquet files, and the included R scripts give guidance on how to utilize and query the dataset in R. LakeBeD-US: Ecology Edition is an ecological science-oriented companion to LakeBeD-US: Computer Science Edition. The Computer Science Edition is available on the Hugging Face Hub.
Data for "What have biological records ever done for us? A systematic scoping review"
<p>These files contain data used in "What have biological records ever done for us? A systematic scoping review" (Gaul et al. 2020). </p>
INTRODUCTION OF COVID-NEWS-US-NNK AND COVID-NEWS-BD-NNK DATASET
<p>Introduction</p> <p>There are several works based on Natural Language Processing on newspaper reports. Mining opinions from headlines [ 1 ] using Standford NLP and SVM by Rameshbhaiet. Al.compared several algorithms on a small and large dataset. Rubinet. al., in their paper [ 2 ], created a mechanism to differentiate fake news from real ones by building a set of characteristics of news according to their types. The purpose was to contribute to the low resource data available for training machine learning algorithms. Doumitet. al.in [ 3 ] have implemented LDA, a topic modeling approach to study bias present in online news media.</p> <p>However, there are not many NLP research invested in studying COVID-19. Most applications include classification of chest X-rays and CT-scans to detect presence of pneumonia in lungs [ 4 ], a consequence of the virus. Other research areas include studying the genome sequence of the virus[ 5 ][ 6 ][ 7 ] and replicating its structure to fight and find a vaccine. This research is crucial in battling the pandemic. The few NLP based research publications are sentiment classification of online tweets by Samuel et el [ 8 ] to understand fear persisting in people due to the virus. Similar work has been done using the LSTM network to classify sentiments from online discussion forums by Jelodaret. al.[ 9 ]. NKK dataset is the first study on a comparatively larger dataset of a newspaper report on COVID-19, which contributed to the virus’s awareness to the best of our knowledge.</p> <p> </p> <p>2 Data-set Introduction</p> <p>2.1 Data Collection</p> <p>We accumulated 1000 online newspaper report from United States of America (USA) on COVID-19. The newspaper includes The Washington Post (USA) and StarTribune (USA). We have named it as “Covid-News-USA-NNK”. We also accumulated 50 online newspaper report from Bangladesh on the issue and named it “Covid-News-BD-NNK”. The newspaper includes The Daily Star (BD) and Prothom Alo (BD). All these newspapers are from the top provider and top read in the respective countries. The collection was done manually by 10 human data-collectors of age group 23- with university degrees. This approach was suitable compared to automation to ensure the news were highly relevant to the subject. The newspaper online sites had dynamic content with advertisements in no particular order. Therefore there were high chances of online scrappers to collect inaccurate news reports. One of the challenges while collecting the data is the requirement of subscription. Each newspaper required $1 per subscriptions. Some criteria in collecting the news reports provided as guideline to the human data-collectors were as follows:</p> <ul> <li>The headline must have one or more words directly or indirectly related to COVID-19.</li> <li>The content of each news must have 5 or more keywords directly or indirectly related to COVID-19.</li> <li>The genre of the news can be anything as long as it is relevant to the topic. Political, social, economical genres are to be more prioritized.</li> <li>Avoid taking duplicate reports.</li> <li>Maintain a time frame for the above mentioned newspapers.</li> </ul> <p>To collect these data we used a google form for USA and BD. We have two human editor to go through each entry to check any spam or troll entry.</p> <p>2.2 Data Pre-processing and Statistics</p> <p>Some pre-processing steps performed on the newspaper report dataset are as follows:</p> <ul> <li>Remove hyperlinks.</li> <li>Remove non-English alphanumeric characters.</li> <li>Remove stop words.</li> <li>Lemmatize text.</li> </ul> <p>While more pre-processing could have been applied, we tried to keep the data as much unchanged as possible since changing sentence structures could result us in valuable information loss. While this was done with help of a script, we also assigned same human collectors to cross check for any presence of the above mentioned criteria.</p> <p>The primary data statistics of the two dataset are shown in Table 1 and 2.</p> <pre><code>Table 1: Covid-News-USA-NNK data statistics </code></pre> <pre><code>No of words per headline </code></pre> <pre><code>7 to 20 </code></pre> <pre><code>No of words per body content </code></pre> <pre><code>150 to 2100 </code></pre> <pre><code>Table 2: Covid-News-BD-NNK data statistics No of words per headline </code></pre> <pre><code>10 to 20 </code></pre> <pre><code>No of words per body content </code></pre> <pre><code>100 to 1500 </code></pre> <p>2.3 Dataset Repository</p> <p>We used GitHub as our primary data repository in account name NKK^1. Here, we created two repositories USA-NKK^2 and BD-NNK^3. The dataset is available in both CSV and JSON format. We are regularly updating the CSV files and regenerating JSON using a py script. We provided a python script file for essential operation. We welcome all outside collaboration to enrich the dataset.</p> <p> </p> <p> </p> <p>3 Literature Review</p> <p>Natural Language Processing (NLP) deals with text (also known as categorical) data in computer science, utilizing numerous diverse methods like one-hot encoding, word embedding, etc., that transform text to machine language, which can be fed to multiple machine learning and deep learning algorithms.</p> <p>Some well-known applications of NLP includes fraud detection on online media sites[ 10 ], using authorship attribution in fallback authentication systems[ 11 ], intelligent conversational agents or chatbots[ 12 ] and machine translations used by Google Translate[ 13 ]. While these are all downstream tasks, several exciting developments have been made in the algorithm solely for Natural Language Processing tasks. The two most trending ones are BERT[ 14 ], which uses bidirectional encoder-decoder architecture to create the transformer model, that can do near-perfect classification tasks and next-word predictions for next generations, and GPT-3 models released by OpenAI[ 15 ] that can generate texts almost human-like. However, these are all pre-trained models since they carry huge computation cost. Information Extraction is a generalized concept of retrieving information from a dataset. Information extraction from an image could be retrieving vital feature spaces or targeted portions of an image; information extraction from speech could be retrieving information about names, places, etc[ 16 ]. Information extraction in texts could be identifying named entities and locations or essential data. Topic modeling is a sub-task of NLP and also a process of information extraction. It clusters words and phrases of the same context together into groups. Topic modeling is an unsupervised learning method that gives us a brief idea about a set of text. One commonly used topic modeling is Latent Dirichlet Allocation or LDA[17].</p> <p>Keyword extraction is a process of information extraction and sub-task of NLP to extract essential words and phrases from a text. TextRank [ 18 ] is an efficient keyword extraction technique that uses graphs to calculate the weight of each word and pick the words with more weight to it.</p> <p>Word clouds are a great visualization technique to understand the overall ’talk of the topic’. The clustered words give us a quick understanding of the content.</p> <p> </p> <p> </p> <p>4 Our experiments and Result analysis</p> <p>We used the wordcloud library^4 to create the word clouds. Figure 1 and 3 presents the word cloud of Covid-News-USA- NNK dataset by month from February to May. From the figures 1,2,3, we can point few information:</p> <ul> <li>In February, both the news paper have talked about China and source of the outbreak.</li> <li>StarTribune emphasized on Minnesota as the most concerned state. In April, it seemed to have been concerned more.</li> <li>Both the newspaper talked about the virus impacting the economy, i.e, bank, elections, administrations, markets.</li> <li>Washington Post discussed global issues more than StarTribune.</li> <li>StarTribune in February mentioned the first precautionary measurement: wearing masks, and the uncontrollable spread of the virus throughout the nation.</li> <li>While both the newspaper mentioned the outbreak in China in February, the weight of the spread in the United States are more highlighted through out March till May, displaying the critical impact caused by the virus.</li> </ul> <p>We used a script to extract all numbers related to certain keywords like ’Deaths’, ’Infected’, ’Died’ , ’Infections’, ’Quarantined’, Lock-down’, ’Diagnosed’ etc from the news reports and created a number of cases for both the newspaper. Figure 4 shows the statistics of this series. From this extraction technique, we can observe that April was the peak month for the covid cases as it gradually rose from February. Both the newspaper clearly shows us that the rise in covid cases from February to March was slower than the rise from March to April. This is an important indicator of possible recklessness in preparations to battle the virus. However, the steep fall from April to May also shows the positive response against the attack. We used Vader Sentiment Analysis to extract sentiment of the headlines and the body. On average, the sentiments were from -0.5 to -0.9. Vader Sentiment scale ranges from -1(highly negative to 1(highly positive). There were some cases</p> <p>where the sentiment scores of the headline and body contradicted each other,i.e., the sentiment of the headline was negative but the sentiment of the body was slightly positive. Overall, sentiment analysis can assist us sort the most concerning (most negative) news from the positive ones, from which we can learn more about the indicators related to COVID-19 and the serious impact caused by it. Moreover, sentiment analysis can also provide us information about how a state or country is reacting to the pandemic. We used PageRank algorithm to extract keywords from headlines as well as the body content. PageRank efficiently highlights important relevant keywords in the text. Some frequently occurring important keywords extracted from both the datasets are: ’China’, Government’, ’Masks’, ’Economy’, ’Crisis’, ’Theft’ , ’Stock market’ , ’Jobs’ , ’Election’, ’Missteps’, ’Health’, ’Response’. Keywords extraction acts as a filter allowing quick searches for indicators in case of locating situations of the economy, how states are defending against the pandemic, the condition of the health care system etc.</p> <p> </p> <p>5 Conclusion</p> <p>This dataset can demonstrate how news reports could speculate the situation differently based on the news source. The different types of experiments are possible to assert the importance of Natural Language Processing in newspaper report analysis. We are looking for more collaborators in GitHub to enrich the dataset, which will make it possible to run extensive deep learning experiments.</p> <p> </p> <p>References</p> <p>[1] Chaudhary Jashubhai Rameshbhai and Joy Paulose. Opinion mining on newspaper headlines using svm and nlp. International Journal of Electrical & Computer Engineering (2088-8708), 9(3), 2019. [2]Victoria L Rubin, Yimin Chen, and Nadia K Conroy. Deception detection for news: three types of fakes. Proceedings of the Association for Information Science and Technology, 52(1):1–4, 2015. [3]Sarjoun Doumit and Ali Minai. Online news media bias analysis using an lda-nlp approach. InInternational Conference on Complex Systems, 2011. [4]Md Manjurul Ahsan, Kishor Datta Gupta, Mohammad Maminur Islam, Sajib Sen, Md Rahman, Moham- mad Shakhawat Hossain, et al. Study of different deep learning approach with explainable ai for screening patients with covid-19 symptoms: Using ct scan and chest x-ray image dataset.arXiv preprint arXiv:2007.12525, 2020. [5]Gurjit S Randhawa, Maximillian PM Soltysiak, Hadi El Roz, Camila PE de Souza, Kathleen A Hill, and Lila Kari. Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: Covid-19 case study.Plos one, 15(4):e0232391, 2020.</p> <p>[6]Ahmad Alimadadi, Sachin Aryal, Ishan Manandhar, Patricia B Munroe, Bina Joe, and Xi Cheng. Artificial intelligence and machine learning to fight covid-19, 2020. [7]Shreshth Tuli, Shikhar Tuli, Rakesh Tuli, and Sukhpal Singh Gill. Predicting the growth and trend of covid- pandemic using machine learning and cloud computing.Internet of Things, page 100222, 2020. [8]Jim Samuel, GG Ali, Md Rahman, Ek Esawi, Yana Samuel, et al. Covid-19 public sentiment insights and machine learning for tweets classification.Information, 11(6):314, 2020. [9] Hamed Jelodar, Yongli Wang, Rita Orji, and Hucheng Huang. Deep sentiment classification and topic discovery on novel coronavirus or covid-19 online discussions: Nlp using lstm recurrent neural network approach.arXiv preprint arXiv:2004.11695, 2020.</p> <p>[10]Nafiz Sadman, Kishor Datta Gupta, Ariful Haque, Subash Poudyal, and Sajib Sen. Detect review manipulation by leveraging reviewer historical stylometrics in amazon, yelp, facebook and google reviews. InProceedings of the 2020 The 6th International Conference on E-Business and Applications, pages 42–47, 2020.</p> <p>[11]Nafiz Sadman, Kishor Datta Gupta, Ariful Haque, Subash Poudyal, and Sajib Sen. Stylometry as a reliable method for fallback authentication. InProceedings of the 2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2020.</p> <p>[12]Ethan Fast, Binbin Chen, Julia Mendelsohn, Jonathan Bassen, and Michael S Bernstein. Iris: A conversational agent for complex tasks. InProceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pages 1–12, 2018.</p> <p>[13] Philipp Koehn.Statistical machine translation. Cambridge University Press, 2009.</p> <p>[14]Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding.arXiv preprint arXiv:1810.04805, 2018.</p> <p>[15]Will Douglas Heavenarchive page. Openai’s new language generator gpt-3 is shockingly good—and completely mindless <a href="https://www.technologyreview.com/2020/07/20/1005454/openai-machine-learning-language-generator-">https://www.technologyreview.com/2020/07/20/1005454/openai-machine-learning-language-generator-</a> gpt-3-nlp/. Technical report.</p> <p>[16]Chin-Hui Lee and Sabato Marco Siniscalchi. An information-extraction approach to speech processing: Analysis, detection, verification, and recognition.Proceedings of the IEEE, 101(5):1089–1115, 2013.</p> <p>[17]Hamed Jelodar, Yongli Wang, Chi Yuan, Xia Feng, Xiahui Jiang, Yanchao Li, and Liang Zhao. Latent dirich- let allocation (lda) and topic modeling: models, applications, a survey. Multimedia Tools and Applications, 78(11):15169–15211, 2019.</p> <p>[18]Monica Bianchini, Marco Gori, and Franco Scarselli. Inside pagerank.ACM Transactions on Internet Technology (TOIT), 5(1):92–128, 2005.</p>
What do studies in wild mammals tell us about human emerging viral diseases in Mexico? database
<p>The database used in the article "<strong>What do studies in wild mammals tell us about human emerging viral diseases in Mexico?</strong>". It contains all available records of viral zoonotic and potential zoonotic species in Mexican wild mammals.</p> <p>The first file is a .csv file and the second one is .xls</p>
PsPM-VIS: SCR, ECG, respiration and eyetracker measurements in a delay fear conditioning task with visual CS and electrical US
<p>This dataset consists of a three-block experiment conducted with 29 healthy unmedicated participants (17 females and 12 males aged 25.3 +/- 3.7). The experiment contains a classical (Pavlovian) discriminant delay fear conditioning test. CSs are 2 full-screen fractals of approximate brightness, contrast, and spatial frequency. US is a train of electric square pulses delivered with a constant current stimulator on participants' dominant forearm through a pin-cathod/ring-anode configuration. SOA between the CS and US is 3.5 s. The first 2 blocks are fear acquisition, with 15 CS+US+, 15 CS+US-, and 30 CS- in each block, and the last block is an extinction phase with 20 CS- and 20 CS+ trials without US delivery. The order of trials in each block was randomized. No fixation cross was presented during CS. ITI is randomly determined on each trial to be an integer between 7 - 11 s. During ITI, a black fixation cross was presented in the center of a grey background (RGB 0.7, 0.7, 0.7). The blocks were recorded on the same day with a self-paced break. For all three blocks this dataset contains skin conductance responses (SCR), electrocardyogram (ECG), respiration, pupil size (PSR), and gaze coordinates measurements.</p>
US_Horror_Trailers_Data_Set
<p>This data set contains audio, colour, editing, and motion data for trailers for the fifty highest grossing horror films at the US box office from 2011 to 2015.</p> <p>All trailers were pre-processed to trim MPAA tag screens and YouTube channel promotional materials, and cropped to remove letterbox blanking.</p> <p>The sample is described in the file US_Horror_Trailers_Sample_Summary.csv, which contains the title of the film promoted, the URL for the trailer on YouTube (all URLs were correct as of 27 January 2021), the dimensions of the video file after pre-processing, the number of frames after pre-processing, the native frame rate, and the running time of a trailer after pre-processing in seconds.</p> <p>For each trailer the following information is available:</p> <ul> <li>Audio: the time contour of the normalized aggregated power envelope. One csv file per trailer with the naming convention: *_audio.csv</li> <li>Colour: three unnamed columns containing the average colour of each frame as an RGB triplet. One csv file per trailer with the naming convention: *_rgb.csv</li> <li>Motion: total magnitude and horizontal and vertical pixel displacement between consecutive frames. One csv file per trailer with the naming convention: *_motion.csv</li> <li>Editing: Shot length data in seconds. Shot length data is contained in the csv file US_Horror_Trailers_SL_Data.csv, with one column per data.</li> </ul> <p>All variables are time ordered from frame/shot/window 1 to <em>n</em> even when no explicit time variable is provided.</p> <p>A guide to visualising the data in this data set is available online here: <a href="https://rpubs.com/nr62_rp33/visualizing_trailers">https://rpubs.com/nr62_rp33/visualizing_trailers</a>.</p>
US Material Handling Equipment Market 2024 to 2033
<p><strong>Reports Description</strong></p> <p>According to current market research conducted by the CMI Team, the global <a href="https://www.custommarketinsights.com/report/us-material-handling-equipment-market/" target="_blank" rel="noopener"><strong>US Material Handling Equipment Market</strong></a> is expected to record a CAGR of <strong>7.89%</strong> from 2024 to 2033. In 2024, the market size is projected to reach a valuation of USD <strong>42.37 Billion</strong>. By 2033, the valuation is anticipated to reach USD <strong>83.92 Billion</strong><strong>.</strong></p> <p>Dynamic and evolving quickly, driven by technical improvement, the U.S. Material Handling Equipment market has many changes. This contains an assortment of devices composed of forklifts, conveyors, automated storage and retrieval systems, and robots to improve handling processes concerning materials in industries. The main trends shaping this space include automation and robotization, energy efficiency with a sustainability focus on solutions, and an increased level of e-commerce and distribution channels.</p> <p>DOWNLOAD FREE SAMPLE Now at <a href="https://www.custommarketinsights.com/request-for-free-sample/?reportid=59218" target="_blank" rel="noopener">https://www.custommarketinsights.com/request-for-free-sample/?reportid=59218</a></p>
Aggregated US Bank Dataset
<p>These two .csv files contain the US bank dataset for FETILDA, containing sections of 10-K reports submitted by US banks from 2006 to 2016. They are directly used by the Python scripts for training, validation, and testing. There are two files, one for Item 1A of the 10-K reports, and the other for Item 7/7A.</p>
Precipitation objects under the current and future climate: WRF 6-km hydroclimate simulation of the western US
<p>This folder includes the precipitation objects that are used in the following manuscript:</p> <p>Chen et al., Sharpening of Cold Season Storms over the Western US.</p> <p>It is generated using WRF V3.8 at PNNL. A historical simulation ("NARR") is done for 1981-2010, and five future simulations ("CanESM2", "CESM1-CAM5", "GFDL-ESM2M", "HadGEM2-ES", "MPI-ESM-MR") are done for 2041-2070 using the Pseudo Global Warming (PGW) approach. For the WRF model configuration and the simulation details, please refer to the abovementioned manuscript and Chen et al. (2018).</p> <p>This is the preliminary version of the dataset that contains the precipitation object features as analyzed in the manuscript. More data (including the WRF raw precipitation output) and the finalized scripts will be included here before the manuscript is published.</p> <p> </p> <p>Reference:</p> <p>Chen, X., L. R. Ruby, Y. Gao, Y. Liu, M. Wigmosta, and M. Richmond (2018), Predictability of Extreme Precipitation in Western U.S. Watersheds Based on Atmospheric River Occurrence, Intensity, and Duration, <em>Geophys. Res. Lett.</em> doi: <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2018GL079831">10.1029/2018GL079831</a></p> <p>Chen, X., L. R. Ruby, Y. Gao, Y. Liu, and M. Wigmosta (2023), Sharpening of Cold Season Storms over the Western US, Nat. Clim. Change. doi: <a href="https://www.nature.com/articles/s41558-022-01578-0">10.1038/s41558-022-01578-0</a> </p>
Public Dataset for "Did State-sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter"
<p>Dataset for the "Did State-sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter" paper. </p> <p>The full text of the paper can be found <a href="https://zenodo.org/record/4699959#.YngKatNBy3K">here</a>.</p> <p>The folder "Tweet_IDs" contains the complete list of the 152,514,929 tweet IDs (together with their timestamps) which we used for the analysis in the study: "Did State-sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter"</p> <p>by Nikos Salamanos, Michael J. Jensen, Costas Iordanou and Michael Sirivianos</p> <p>We have split the tweets into separate .zip files based on the date listed in their timestamps.</p> <p>The crawling took place from September 21 to November 7, 2016 (47 days; we did not collect data on 02/10/2016).</p> <p>Each "tweet_day_X.zip" file contains the file "tweet_day_X.csv", where X in [1,2,...,47]. For instance, the file "tweets_day_1.zip" contains the tweets of the 1st day: 09/21/2016.</p> <p>Please cite the paper in any published work that uses any of these resources. </p> <p>@misc{nikos_salamanos_2021_4699959,<br> author = {Nikos Salamanos and<br> Michael J. Jensen and<br> Costas Iordanou and<br> Michael Sirivianos},<br> title = {{Did State-sponsored Trolls Shape the 2016 US <br> Presidential Election Discourse? Quantifying<br> Influence on Twitter}},<br> month = apr,<br> year = 2021,<br> publisher = {Zenodo},<br> version = 3,<br> doi = {10.5281/zenodo.4699959},<br> url = {https://doi.org/10.5281/zenodo.4699959}<br> }</p>
Crop-specific salinity and irrigation data for river sub-basin water scarcity analyses in the US and AU
<p>This dataset contains observed monthly and annual salinity (EC) data for surface water (river) respectively groundwater, spatially averaged over sub-basins within the Central Valley, CA and the Murray Darling basin, AU, used for salinity-inclusive water scarcity assessments. The data also includes crop-specific irrigated area, irrigation withdrawals and salinity thresholds and other parameters specified, as well as example codes for analyses related to the manuscript: Thorslund et al., <em>Salinity impacts on irrigation water-scarcity in food bowl regions of the US and Australia.</em></p>
Elk hair trace minerals and treponeme-associated hoof disease surveillance metadata in the US Pacific Northwest
<p>This is the publicly accessible dataset reporting concentrations of thirteen analyzed minerals from hair using inductively coupled plasma mass spectrometry and relevant metadata from treponeme-associated hoof disease in Pacific Northwest elk. The data presented here were analyzed for the manuscript entitled "Associations between hair trace mineral concentrations and the occurrence of treponeme-associated hoof disease in elk (<em>Cervus canadensis</em>)."</p> <p>Please note that reported mineral concentrations are in their adjusted values and raw forms represented by the column name having ".Raw", (e.g., "Selenium" versus "Selenium.Raw"). Elk ecotype is represented by a four letter abbreviation for either Roosevelt (ROOS) or Rocky Mountain (ROMO). Unknown values for some variables (e.g., age class, county) are denoted with a "U."</p>
State level income distributions for net income deciles for the US for historical years (2011-2014) and projections for different SSP scenarios (2015-2100)
<p>This dataset is documented in this manuscript here- https://iopscience.iop.org/article/10.1088/1748-9326/acf9b8/meta</p> <p>Income distributions are a growing area of interest in the examination of equity impacts brought on by climate change and its responses. We project US state level income distributions using a PCA-based approach, applying a downscaled version of the approach employed by Narayan et al. (2022, in-prep). A state-level dataset had to be synthesized and projected based on existing sources. We apply a PC-based model to our derived state-level dataset, employing projected GINI’s from the SSP scenarios. We produce projected income distribution by income decile for three SSPs to year 2100. For the purpose of the projections, we developed a consistent set of tax adjusted net income deciles for all states from 2011 to 2014. This dataset was used for initialization of the projections and for validation.</p> <p>If/when using this dataset, please cite this paper- https://iopscience.iop.org/article/10.1088/1748-9326/acf9b8/meta</p>
Financial Metrics Dataset of US companies
<p>Financial Metrics Dataset of US companies obtained from 10-K filings (in XBRL format) from SEC. The dataset contains financial metric answers for 9,263 US companies and in total 800,714 metric answers related to 28 financial metrics.</p>
Atmospheric_river_land_hydrology_western_US_HUC8_datasets
<p>Dataset for the manuscript entitled "Impact of Atmospheric Rivers on Surface Hydrological Processes in Western U.S. Watersheds".</p> <p> </p> <p>It includes daily meteorological and surface hydrological data from western U.S. WRF simulation. Data is aggregated to 8-digit Hydrological Unit (HUC8) watersheds.</p> <p> </p> <p>To use this dataset, please cite the following two publications:</p> <p> </p> <p>Chen, X., Leung, L. R., Gao, Y., Liu, Y., Wigmosta, M., & Richmond, M. (2018). Predictability of extreme precipitation in western U.S. watersheds based on atmospheric river occurrence, intensity, and duration. <em>Geophysical Research Letters</em>, 45, 11,693–11,701. <a href="https://doi.org/10.1029/2018GL079831">https://doi.org/10.1029/2018GL079831</a></p> <p> </p> <p>Chen, X., Leung, L. R., Wigmosta, M., & Richmond, M. (2019). Impact of Atmospheric Rivers on Surface Hydrological Processes in Western U.S. Watersheds. <em>Journal of Geophysical Research: Atmospheres</em>, <a href="http://doi.org/10.1029/2019JD03468">https://doi.org/10.1029/2019JD03468</a></p>
Modified half-hourly FLUXNET dataset for 10 Boreal forest sites (CA-Obs,CA-Ojp,CA-Qfo,FI-Hyy,FI-Ken,FI-Let,FI-Sod,RU-Fyo,RU-Zot,US-Prr)
<p>This set contains half-hourly driving data and observations used in the simulations described in gmd-2018-313 (doi:10.5194/gmd-2018-313). Originally, this data is part of the FLUXNET2015 dataset (doi:10.17616/R36K9X). We have quality checked and gap-filled this data to suit the simulations.</p> <p>The upload contains site specific csv-files, a data header that is common to all files and a README. The actual data contains half-hourly values for:</p> <ul> <li>gross primary production (GPP, mol m<sup>-2 </sup>s<sup>-1</sup>)</li> <li>evapotranspiration (ET, kg m<sup>-2 </sup>s<sup>-1</sup>)</li> <li>air temperature (air_temp, degrees celcius)</li> <li>air pressure (air_pressure, Pa)</li> <li>precipitation (precip, kg m<sup>-2 </sup>s<sup>-1</sup>)</li> <li>specific humidity (qair, kg<sup> </sup>kg<sup>-1</sup>)</li> <li>wind speed (wspeed, m s<sup>-1</sup>)</li> <li>CO2 concentration (CO2, mol mol<sup>-1</sup>)</li> <li>shortwave radiation (shortwave, W m<sup>-2</sup>)</li> <li>longwave radiation (longwave, W m<sup>-2</sup>)</li> <li>potential shortwave radiation (mpot, W m<sup>-2</sup>)</li> </ul> <p>The sites (named by their FLUXNET identifier) and the years of data in this set are:</p> <ul> <li>CA-Obs (Saskatchewan) 1999-2006</li> <li>CA-Ojp (Saskatchewan) 2004-2006</li> <li>CA-Qfo (Quebec) 2003-2010</li> <li>FI-Hyy (Hyytiälä) 1999-2006</li> <li>FI-Ken (Kenttärova) 2003-2010</li> <li>FI-Let (Lettosuo) 2010-2012</li> <li>FI-Sod (Sodankylä) 2001-2008</li> <li>RU-Fyo (Fyodorkovskoye) 2002-2009</li> <li>RU-Zot (Zotino) 2002-2004</li> <li>US-Prr (Poker Flat) 2011-2013</li> </ul>
Keyword counts from US Presidential State of the Union Addresses and Presidential Budget Messages
<p>Keyword counts from US Presidential State of the Union Addresses and Presidential Budget Messages. This was done using the Python scripts provided under <a href="https://github.com/JeremySilver/KeywordCountsPresidentialMessages">https://github.com/JeremySilver/KeywordCountsPresidentialMessages</a>. The raw text data is from <a href="http://www.presidency.ucsb.edu/">The American Presidency Project</a> (<a href="http://www.ucsb.edu/">UCSB</a>), with some Presidential Budget Messages being extracted from US Federal Budget documents available through <a href="https://fraser.stlouisfed.org/">FRASER</a> (a digital library of U.S. economic, financial, and banking history) or, for the more recent documents the website of the <a href="https://www.whitehouse.gov/">White House</a>.</p> <p>The data headings are:</p> <ul> <li>pid: in most cases, this is the index for the text document as archived on <a href="http://www.presidency.ucsb.edu/">The American Presidency Project</a> website. In some cases, this was the filename of a plain-text file read directly.</li> <li>year: Year that the message was delivered.</li> <li>date: Date that the message was delivered.</li> <li>name: Name of the US President delivering the message.</li> <li>count_of_all_words: Count of all words in the document.</li> <li>count_of_keywords: Count of all keywords encountered in that document.</li> <li>Keyword specific columns - three per keyword. For example, for the 'energy' keyword, the 'energy' column gives the number of times the 'energy' keyword was counted in the message, 'energy_pct_of_keywords' gives this count as a percentage of all keywords, and 'energy_pct_of_all_words' gives this count as a percentage of all words</li> </ul> <p>Below is the list of keywords that match when the search is applied to a dictionary file containing over 99,000 US English words.</p> <ul> <li>energy: 'energy'</li> <li>tax: 'nontaxable', 'overtax', 'overtaxed', 'overtaxes', 'overtaxing', 'surtax', 'surtaxed', 'surtaxes', 'surtaxing', 'surtaxs', 'tax', 'taxable', 'taxation', 'taxations', 'taxed', 'taxes', 'taxing', 'taxpayer', 'taxpayers', 'taxs'</li> <li>defense: 'defend', 'defense'</li> <li>education: 'education'</li> <li>employment: 'employ', 'employable', 'employe', 'employed', 'employee', 'employees', 'employer', 'employers', 'employes', 'employing', 'employment', 'employments', 'employs', 'underemployed', 'unemployable', 'unemployed', 'unemployeds', 'unemployment', 'unemployments'</li> <li>research: 'research', 'researched', 'researcher', 'researchers', 'researches', 'researching', 'researchs'</li> <li>shooting: 'shooting'</li> <li>space: 'space'</li> <li>nuclear: 'nuclear'</li> <li>natural resources: 'natural resources'</li> <li>racism: 'racism', 'civil rights'</li> <li>crime: 'crime', 'crimes', 'criminal', 'criminally', 'criminals', 'decriminalization', 'decriminalizations', 'decriminalize', 'decriminalized', 'decriminalizes', 'decriminalizing'</li> <li>environment: 'environment', 'environmental', 'environmentalism', 'environmentalisms', 'environmentalist', 'environmentalists', 'environmentally', 'environments'</li> <li>religion: 'faith', 'god', 'prayer', 'religion'</li> <li>health: 'health', 'healthful', 'healthfully', 'healthfulness', 'healthfulnesss', 'healthier', 'healthiest', 'healthily', 'healthiness', 'healthinesss', 'healths', 'healthy', 'unhealthful', 'unhealthier', 'unhealthiest', 'unhealthy'</li> <li>terror: 'terror', 'terrorism', 'terrorisms', 'terrorist', 'terrorists', 'terrorize', 'terrorized', 'terrorizes', 'terrorizing', 'terrors'</li> <li>war: 'war', 'warrior', 'warriors', 'wars'</li> <li>economy: 'economic', 'economical', 'economically', 'economics', 'economicss', 'economy', 'economys', 'microeconomics', 'microeconomicss', 'socioeconomic', 'uneconomic', 'uneconomical'</li> <li>jobs: 'jobs'</li> <li>business: 'agribusiness', 'agribusinesses', 'agribusinesss', 'business', 'businesses', 'businesslike', 'businessman', 'businessmans', 'businessmen', 'businesss', 'businesswoman', 'businesswomans', 'businesswomen'</li> <li>drugs: 'drugs', 'narcotics'</li> <li>inflation: 'inflation'</li> <li>climate: 'climate'</li> <li>science: 'science', 'sciences', 'scientific', 'scientifically', 'scientist', 'scientists'</li> <li>gun: 'gun', 'gunfire', 'gunman', 'guns', 'handgun', 'rifle', 'shotgun'</li> <li>tech: 'biotechnology', 'biotechnologys', 'technical', 'technological', 'technologically', 'technologies', 'technologist', 'technologists', 'technology', 'technologys'</li> <li>military: 'military'</li> <li>security: 'security'</li> <li>housing: 'housing'</li> <li>pollution: 'pollution'</li> </ul> <p>The dictionary file used is a standard file among Linux systems, and the version used was provided with version 7.1-1 of the Ubuntu 'wamerican' package. Two extra phrases, which do not appear in the dictionary file, are added to the list: 'civil rights' (under the 'racism' keyword) and 'natural resources' (under the 'natural resources' theme).</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.