Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
9,674
datasets available to search
ShareScore release 0.7.1
Dataset results
9,674 results for “COVID-19”
Base de datos estudio hábitos de lectura y educación durante el COVID-19
<p>Conjunto de datos del artículo: Análisis de los desafíos dentro del contexto de la educación y la lectura durante la pandemia, en la prensa internacional de ámbito hispánico.</p> <p>Revista Teknokultura</p> <p>Año de publicación: 2022</p> <p><a href="http://secure-web.cisco.com/12oWEcXObAwUQSnQdAHjWStGdmFhNayre6eVd46qfO7EhxyPerrdRancQUDY8Ks7lkAvu_PPK5dWva63Dm66RQJKtgpIILaanYWMmv-AoVNhpWI4Gh0XN9AJv7HPANVSiGiRYbbyiOxtxP--KJ7P5jSaYsEGPy54ZGnyweRBnMkmNNp8iASs4k34Qo5VjuhfBt8Tex2SOkGS4wZS0Vw03WIRx0MoukBBtXZBbGxxlPX8IHaFhJEedcl9142fbWuvVdt0MUbARjHQocIS6p-FvK9FVuq7HgupkIpEpqplI-E9wE7z5fi6XRy0Gt522gLU-KeYUoRO1VkZue77Vpno_phImMxVdOK2nlnMfifVFeTo/http%3A%2F%2Fdx.doi.org%2F10.5209%2FTEKN.77819">http://dx.doi.org/10.5209/TEKN.77819</a></p> <p> </p>
DATASET - Vaccination and Variants: retrospective model for the evoultion of Covid-19 in Italy
<p>DATASET of <a href="https://medrxiv.org/cgi/content/short/2022.02.27.22271593v1">https://medrxiv.org/cgi/content/short/2022.02.27.22271593v1</a></p>
Supplementary material, Confidence in a vaccine against Covid-19 among registered nurses in Barcelona, Spain across two time periods. Spanish/English
<p>Supplementary material, Confidence in a vaccine against Covid-19 among registered nurses in Barcelona, Spain across two time periods. For further information please refer to ext_dpalma@aspb.cat</p>
COVID-19 survey in Baja California, Mexico, in February of 2021
<p><span>Baja California is a State located in the north-western region of Mexico, which borders with the United States. This area is of particular importance in terms of epidemiological surveillance, given that the busiest port of entry to the U.S. is located in the Tijuana-San Diego region, which is also one of the busiest ports of entry in the world. During the first year of the COVID-19 pandemic, Baja California had the second mortality rate in Mexico due COVID-19. We conducted a cross-sectional study to know the prevalence of COVID-19 in the general population at the beginning of 2021, right after the second wave of COVID-19 cases in the State.</span></p> <p><span>We carried out a population-based survey in the 3 largest cities of Baja California, Mexico: Mexicali, Tijuana, and Ensenada. The selection of households followed a probabilistic, 3 stage approach, with a total of 1,126 individuals included, which were representative of a population of 2.7 million after weighting. Real Time Polymerase Chain Reaction (RT-qPCR) was used to assess SARS-CoV-2 infection in nasopharyngeal swabs and IgG seroprevalence was evaluated in finger stick blood samples. We also assessed the level of knowledge, attitudes, and practices on COVID-19 prevention in the general population.</span></p> <p><span>In general, Baja California had similar COVID-19 prevalence rate compared to other States in Mexico, and crossing the border with the U.S. was not associated to higher odds of infection. Ensenada had a higher prevalence of COVID-19 by RT-qPCR compared to Tijuana and Ensenada. The level of health literacy on COVID-19 shows important areas of opportunity, along with a considerable high rate of vaccine hesitancy. </span></p> <p><span>Our findings on this important area in the Mexico-United States border show that the dynamics of COVID-19 are complex, and prevalence rates can significantly differ even within the same region. More work needs to be done in terms of health promotion in the general population of Baja California in order to achieve better health outcomes in future public health emergencies of international concern.</span></p>
Gaseous elementary mercury and other air pollutants data during COVID-19
<p>This dataset contains gaseous elementary mercury, particulate ions, organics, and trace metals, and meteorological parameters measured at the Dianshan Lake site in Shanghai during the 2020 COVID-19 period.</p>
TopicTracker keywords and MeSH terms resulting from the analysis of papers on autonomy, equity, privacy, proportionality and trust in the context of Covid-19
<p>This dataset contains normalized keywords and MeSH terms contained in articles retrieved with 5 separate querioes on Covid-19 and autonomy, equity, privacy, proportionality, trust.</p>
TopicTracker Medline files and query logs generated retrieving papers on autonomy, equity, privacy, proportionality and trust in the context of Covid-19
<p>To determine the core areas of discussion about the interplay between the Core Five and the Covid-19 pandemic, we ran a set of five queries in the TopicTracker. Each query collects articles regarding Covid-19 and one of the Core Five Enduring Values, published between January 2019 and March 2022.</p>
The first COVID-19 related Lockdown Paris Lagrangian model Footprints - Publication dataset
<p>This repository contains the Lagrangian model (LPDM) footprints and concentrations time-series modelled over Paris and the surrounding region of Ile-de-France for the period between March 1 and June 1 of 2019 or the year prior to the first COVID-19 related lockdown, and 2020 the year during which the lockdown occured.</p>
The unequal impact of the COVID-19 pandemic in 2020 on life expectancy across urban areas in Chile: A cross-sectional demographic study
<p>Data accompanying the paper The unequal impact of the COVID-19 pandemic in 2020 on life expectancy across urban areas in Chile: A cross-sectional demographic study, currently at https://www.medrxiv.org/content/10.1101/2021.12.08.21267475v1</p>
A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave
<p><strong>Please cite the following paper when using this dataset:</strong></p> <p>N. Thakur, “A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave,” Journal of Data, vol. 7, no. 8, p. 109, Aug. 2022, doi: 10.3390/data7080109</p> <p><strong>Abstract</strong></p> <p>The COVID-19 Omicron variant, reported to be the most immune evasive variant of COVID-19, is resulting in a surge of COVID-19 cases globally. This has caused schools, colleges, and universities in different parts of the world to transition to online learning. As a result, social media platforms such as Twitter are seeing an increase in conversations, centered around information seeking and sharing, related to online learning. Mining such conversations, such as Tweets, to develop a dataset can serve as a data resource for interdisciplinary research related to the analysis of interest, views, opinions, perspectives, attitudes, and feedback towards online learning during the current surge of COVID-19 cases caused by the Omicron variant. Therefore this work presents a large-scale public Twitter dataset of conversations about online learning since the first detected case of the COVID-19 Omicron variant in November 2021. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter and the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.</p> <p><strong>Data Description</strong></p> <p>The dataset comprises a total of 52,984 Tweet IDs (that correspond to the same number of Tweets) about online learning that were posted on Twitter from 9th November 2021 to 13th July 2022. The earliest date was selected as 9th November 2021, as the Omicron variant was detected for the first time in a sample that was collected on this date. 13th July 2022 was the most recent date as per the time of data collection and publication of this dataset.</p> <p>The dataset consists of 9 .txt files. An overview of these dataset files along with the number of Tweet IDs and the date range of the associated tweets is as follows. Table 1 shows the list of all the synonyms or terms that were used for the dataset development. </p> <ul> <li>Filename: TweetIDs_November_2021.txt (No. of Tweet IDs: 1283, Date Range of the associated Tweet IDs: November 1, 2021 to November 30, 2021)</li> <li>Filename: TweetIDs_December_2021.txt (No. of Tweet IDs: 10545, Date Range of the associated Tweet IDs: December 1, 2021 to December 31, 2021)</li> <li>Filename: TweetIDs_January_2022.txt (No. of Tweet IDs: 23078, Date Range of the associated Tweet IDs: January 1, 2022 to January 31, 2022)</li> <li>Filename: TweetIDs_February_2022.txt (No. of Tweet IDs: 4751, Date Range of the associated Tweet IDs: February 1, 2022 to February 28, 2022)</li> <li>Filename: TweetIDs_March_2022.txt (No. of Tweet IDs: 3434, Date Range of the associated Tweet IDs: March 1, 2022 to March 31, 2022)</li> <li>Filename: TweetIDs_April_2022.txt (No. of Tweet IDs: 3355, Date Range of the associated Tweet IDs: April 1, 2022 to April 30, 2022)</li> <li>Filename: TweetIDs_May_2022.txt (No. of Tweet IDs: 3120, Date Range of the associated Tweet IDs: May 1, 2022 to May 31, 2022)</li> <li>Filename: TweetIDs_June_2022.txt (No. of Tweet IDs: 2361, Date Range of the associated Tweet IDs: June 1, 2022 to June 30, 2022)</li> <li>Filename: TweetIDs_July_2022.txt (No. of Tweet IDs: 1057, Date Range of the associated Tweet IDs: July 1, 2022 to July 13, 2022)</li> </ul> <p>The dataset contains only Tweet IDs in compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The Tweet IDs need to be hydrated to be used. For hydrating this dataset the Hydrator application (<a href="https://github.com/DocNow/hydrator/releases">link to download</a> and a <a href="https://towardsdatascience.com/learn-how-to-easily-hydrate-tweets-a0f393ed340e#:~:text=Hydrating%20Tweets">step-by-step tutorial</a> on how to use Hydrator) may be used.</p> <p><strong>Table 1</strong>. List of commonly used synonyms, terms, and phrases for online learning and COVID-19 that were used for the dataset development</p> <table> <tbody> <tr> <td> <p>Terminology</p> </td> <td> <p>List of synonyms and terms</p> </td> </tr> <tr> <td> <p>COVID-19</p> </td> <td> <p>Omicron, COVID, COVID19, coronavirus, coronaviruspandemic, COVID-19, corona, coronaoutbreak, omicron variant, SARS CoV-2, corona virus</p> </td> </tr> <tr> <td> <p>online learning</p> </td> <td> <p>online education, online learning, remote education, remote learning, e-learning, elearning, distance learning, distance education, virtual learning, virtual education, online teaching, remote teaching, virtual teaching, online class, online classes, remote class, remote classes, distance class, distance classes, virtual class, virtual classes, online course, online courses, remote course, remote courses, distance course, distance courses, virtual course, virtual courses, online school, virtual school, remote school, online college, online university, virtual college, virtual university, remote college, remote university, online lecture, virtual lecture, remote lecture, online lectures, virtual lectures, remote lectures</p> </td> </tr> </tbody> </table> <p> </p>
Twitter Conversations about the COVID-19 Omicron Variant: A Large Scale Dataset of more than 500,000 Tweets
<p><strong>Please cite the following paper when using this dataset:</strong></p> <p>N. Thakur and C.Y. Han, “An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection,” Journal of COVID, 2022, Volume 5, Issue 3, pp. 1026-1049</p> <p><strong>Abstract</strong></p> <p>This open-access dataset is one of the salient contributions of the above-mentioned paper. It presents a total of <strong>522,886</strong> Tweet IDs of the same number of <strong>Tweets about the SARS-CoV-2 Omicron Variant</strong> posted on Twitter since the first detected case of this variant on November 24, 2021. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter, as well as with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.</p> <p><strong>Data Description</strong></p> <p>The Tweet IDs are presented in 7 different .txt files based on the timelines of the associated tweets. The data collection followed a keyword-based approach and tweets comprising the "omicron" keyword were filtered, collected, and added to this dataset. The following is the description of these dataset files.</p> <ul> <li>Filename: TweetIDs_November.txt (No. of Tweet IDs: 16471, Date Range of the Tweet IDs: November 24, 2021 to November 30, 2021)</li> <li>Filename: TweetIDs_December.txt (No. of Tweet IDs: 99288, Date Range of the Tweet IDs: December 1, 2021 to December 31, 2021)</li> <li>Filename: TweetIDs_January.txt (No. of Tweet IDs: 92860, Date Range of the Tweet IDs: January 1, 2022 to January 31, 2022)</li> <li>Filename: TweetIDs_February.txt (No. of Tweet IDs: 89080, Date Range of the Tweet IDs: February 1, 2022 to February 28, 2022)</li> <li>Filename: TweetIDs_March.txt (No. of Tweet IDs: 97844, Date Range of the Tweet IDs: March 1, 2022 to March 31, 2022)</li> <li>Filename: TweetIDs_April.txt (No. of Tweet IDs: 91587, Date Range of the Tweet IDs: April 1, 2022 to April 20, 2022)</li> <li>Filename: TweetIDs_May.txt (No. of Tweet IDs: 35756, Date Range of the Tweet IDs: May 1, 2022 to May 12, 2022)</li> </ul> <p>In the above table, the last date for May is May 12 as it was the most recent date at the time of data collection and dataset upload. The dataset would be updated soon to incorporate more recent tweets.</p> <p>The dataset contains only Tweet IDs in compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The Tweet IDs need to be hydrated to be used. For hydrating this dataset the Hydrator application (<a href="https://github.com/DocNow/hydrator/releases">link to download</a> and a <a href="https://towardsdatascience.com/learn-how-to-easily-hydrate-tweets-a0f393ed340e#:~:text=Hydrating%20Tweets">step-by-step tutorial</a> on how to use Hydrator) may be used.</p>
Result of ACS patients in the CCU who have and haven't Covid-19 for the last three months before the CCU admission
<p>Result of Atorvastatin and Rosuvastatin used in ACS with patients who have and have not COVID-19 for the last three months before CCU admission </p>
Dataset of "Telemedicine and its acceptance by patients with type 2 diabetes mellitus at a single care center during the COVID-19 emergency: A prospective observational study"
<p>Dataset of the article titled "Telemedicine and its acceptance by patients with type 2 diabetes mellitus at a single care center during the COVID-19 emergency: A prospective observational study”.</p> <p> </p> <p> </p>
La comunicación del riesgo en las redes sociales. Vacunación contra el Covid-19 en Colombia.
<p>La comunicación pública en entornos de riesgos es un tema que debe ser tratado por investigadores de diferentes áreas, pues esta tiene como propósito modular el comportamiento social e individual de las personas, y debe ser fundamental en la contención de pandemias o de otros riesgos. Así en esta comunicación se propone analizar algunos de los efectos en los usuarios de las redes sociales de la comunicación de riesgos en la etapa introductoria de vacunación en Colombia, para así incidir significativamente en el desarrollo de estrategias más efectivas de comunicación de crisis y riesgos. </p>
AfriDx D6.7 [Dataset v2]: AfriDx Clinical study of NAT in COVID-19
<p>This dataset underlies <a href="https://afridx.ceb.cam.ac.uk/files/afridx_deliverables_6.7_final.pdf">AfriDx D6.7</a> report on Clinical study of NAT in COVID-19.</p> <p><strong>Summary of Project</strong></p> <p>The AfriDx Project comprises nucleic acid testing (NAT) for COVID-19 using the PATHPOD system and compared with the RT-qPCR as the gold standard. KNUST, KCCR and NMIMR received PATHPOD and cartridges from the DTU (see D2.2). The cartridges for the testing were shipped in two (2) batches. Training on PATHPOD usage was done and used for testing covid-19 samples. Data obtained was compared with the gold standard RT-PCR.</p> <p>The overall the testing against RT-LAMP was circa 60% sensitive, but the specificity dropped from 80.0% in the first batch to 24% in the second batch. Analysis of the factors causing the drop in specificity is on-going.</p> <p>In the first batch a total of 1,947 tests were conducted, with 531 positive and 1416 negative results, producing 302 samples returning a false positive (FP) 133 samples giving a false negative (FN). The data showed that the FNs could be related to the copy number of virus in the sample, with a strong correlation with RT-PCR C<sub>T</sub> value and true positive/false negative LAMP outcome. The second batch of nucleic acid testing recorded a total of 1,612 test for N-gene in Ghana with 1208 positive and 404 negative tests. This showed an exceptionally high occurrence of false positive test results: 1134 (76.2%) samples returned a false positive (FP) compared with the RT-PCR and 49 (3.9%) samples gave a false negative (FN). Nevertheless, the correlation with C<sub>T</sub> remained, suggesting that the PATHPOD was functioning correctly and that the results were revealing some contamination or deterioration of reagents or sample.</p> <p>Some initial analysis of the raw data from PATHPOD revealed some characteristics of sample and/or reagent contamination as well as the outcome of a poorly sealed cartridge, that would result in an erroneous signal. Further analysis is needed to fully understand any design modifications that might be beneficial. The impact of shipping and storage on the cartridges also has potential impact and it is particularly noteworthy that the second batch of testing, using cartridges from the same manufacturing run as the first batch, performed less well.</p> <p><strong>Methodology</strong></p> <p>The PATHPOD equipment was placed on a clean and flat surface and switch on with the knob located at the back of equipment to turn on the equipment. The oropharyngeal sample in 300ul of PBS was heated at 95 <sup>0</sup>C for 5min to inactivate virus. The master mix room table was disinfected with suitable disinfectant against DNA/RNA contamination. Wearing appropriate gloves, the Pathpod cartridge was removed from the refrigerator and allow 15-30 min at room temperature for the cartridge to acclimatize, and place in the chip holder. The attached temporary sealer film covering the wells was removed and discarded.</p> <p>After short vortex of sample, 6 µl of sample and/or controls were added directly into the center of the well. the yellow paper from the PCR film was removed and placed over the film chip. The film was sealed properly to the chip using a soft roller ready for processing in the PATHPOD system.</p> <p>Using the Pathpod keyboard, sample ID was entered and COV assay was selected. The start bottom was pressed to heat the machine, thereafter the cartridge was inserted and start bottom was pressed again to run the program. When the assay was completed, result was read from both the screen and the LEDs next to the keyboard. Ensuring that the position on the machine matches the position on the chip. The following interpretation was inferred as results:</p> <p>GREEN LIGHT: NEGATIVE BLINKING RED LIGHT: POSITIVE YELLOW LIGHT: RE-TEST THE SAMPLE</p> <p><strong>Datasets</strong></p> <p>First Batch Data:</p> <table> <tbody> <tr> <td><strong>Dataset</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>PATHPOD ID 25.zip</td> <td>Raw data from all runs on PATHPOD Device ID 25 in Batch 1</td> </tr> <tr> <td>PATHPOD ID 26.zip</td> <td>Raw data from all runs on PATHPOD Device ID 26 in Batch 1</td> </tr> <tr> <td>PATHPOD ID 30.zip</td> <td>Raw data from all runs on PATHPOD Device ID 30 in Batch 1</td> </tr> <tr> <td>AfridX evaluation data_KNUST.V2.xlsx</td> <td>Comparison data for RT-PCR and PATHPOD performed at KNUST</td> </tr> <tr> <td>ALL tests_for DTU. V2.xlsx</td> <td>Comparison data for RT-PCR and PATHPOD performed at NMIMR</td> </tr> </tbody> </table> <p>Second Batch Data:</p> <table> <tbody> <tr> <td><strong>Dataset</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>PATHPOD ID 25 - BATCH 2.zip</td> <td>Raw data from all runs on PATHPOD Device ID 25 in Batch 2</td> </tr> <tr> <td>PATHPOD ID 26 - BATCH 2.zip</td> <td>Raw data from all runs on PATHPOD Device ID 26 in Batch 2</td> </tr> <tr> <td>AFRIDx DATA SECOND BATCH.xlsx</td> <td>Comparison data for RT-PCR and PATHPOD Second Batch</td> </tr> </tbody> </table> <p><br> </p> <p><br> </p> <p><br> </p> <p><br> </p> <p><br> </p> <p><br> </p>
The French Covid-19 vaccination policy did not solve vaccination inequities a nationwide study on 64.5 million people
<p>Data and code to reproduce the analysis of our article.</p> <p> </p> <p># Contents</p> <p>`code/`: Analysis code. <br> The main analysis file is `vaccination-indicators.Rmd`. <br> Some results are exported in `out*.RData` files. </p> <p>`data/`: Data used for the analysis. <br> The data are treated by the `code/0_INSEE_predictors.R` script, and saved as `code/data_indicators.RData`, which is the file used for analysis.</p> <p>`ms/`: Manuscript files; they are outdated (the ms was later modified with Word), but `ms.Rmd` contains code to reproduce the figures and some numerical values given in the text. </p> <p># Data Sources</p> <p>- Vaccination data from Assurance Maladie: <br> - EPCI: <https://datavaccin-covid.ameli.fr/explore/dataset/donnees-devaccination-par-epci/><br> - Paris, Marseille, Lyon: <https://datavaccin-covid.ameli.fr/explore/dataset/donnees-de-vaccination-parcommune/information/></p> <p>- Geographic information:<br> - EPCI: <https://datavaccin-covid.ameli.fr/explore/dataset/georef-france-epci/><br> - Paris, Marseille, Lyon: <https://datavaccin-covid.ameli.fr/explore/dataset/georef-france-commune-arrondissement-municipal/></p> <p>- Socio-economic indicators from INSEE: <https://www.insee.fr/fr/statistiques/5359146#consulter></p> <p>- 2017 Presidential election:<br> - <https://www.data.gouv.fr/fr/datasets/election-presidentielle-des-23-avril-et-7-mai-2017-resultats-definitifs-du-1er-tour-par-communes/#resource-d282e53a-d273-425d-95bb-8a0d7632c79a-header> <br> https://www.data.gouv.fr/fr/datasets/election-presidentielle-des-23-avril-et-7-mai-2017-resultats-du-2eme-tour-2/<br> - Paris: <https://opendata.paris.fr/explore/dataset/elections-presidentielles-2017-1ertour/export/?disjunctive.id_bvote&disjunctive.num_circ&disjunctive.num_quartier&disjunctive.num_arrond&sort=-num_arrond> <br> - Marseille: <https://trouver.datasud.fr/dataset/82a6d91c-c81d-423c-9a4a-3f76d121c8ce/resource/03e2ef07-c2d0-41dd-b503-26910ecb15c3/download/marseille_presidentielles2017_tour1.csv> <br> - Lyon: <https://www.interieur.gouv.fr/Elections/Les-resultats/Presidentielles/elecresult__presidentielle-2017/(path)/presidentielle-2017/084/069/069L.html></p>
Digital Economy, COVID-19 and Employment - Data and Commands
<p> The compressed file contains the data and commands for the paper. The data file named SampleData.dta contains the data set. The command file named Command.do contains all the execution commands. Please note that the commands interact with the data set.</p>
Seroprevalence of exposure to SARS-CoV-2 in domestic dogs and cats and its relationship with COVID-19 cases in the city of Villavicencio, Colombia
<p>Raw data from the article "Seroprevalence of exposure to SARS-CoV-2 in domestic dogs and cats and its relationship with COVID-19 cases in the city of Villavicencio, Colombia". It includes the consecutive number, name, sex, age, species, coordinates, commune, spectrophotometry results and Sample to Positive <em>Ratio</em> from the ELISA test performed in each dog and cat included in the study (435 tested animals). </p>
Dataset & Code related to article 'Bilateral Adaptive Graph Convolutional Network on CT based COVID-19 Diagnosis with Uncertainty-Aware Consensus-Assisted Multiple Instance Learning'
<p>This record contains the 7768 lung masks <strong>manual annotations, implementation code, and pre-trained models</strong> related to the article 'Bilateral Adaptive Graph Convolutional Network on CT based COVID-19 Diagnosis with Uncertainty-Aware Consensus-Assisted Multiple Instance Learning'</p> <p>Also we include the visualised, selected top D reliable CT slices for all COVID-19 patients in the test dataset for better understanding. </p> <p>For the detailed usage of the data and code, please refer to https://github.com/smallmax00/BAGCN-Covid19</p> <p> </p>
eBird data for: Avian behaviour changes in response to human activity during the COVID-19 lockdown in the United Kingdom
<p>Human activities may impact animal habitat and resource use, potentially influencing contemporary evolution in animals. In the United Kingdom (UK), COVID-19 lockdown restrictions resulted in sudden, drastic alterations to human activity. We hypothesized that short-term daily and long-term seasonal changes in human mobility might result in changes in bird habitat use, depending on the mobility type (home, parks, grocery) and the extent of change. Using Google human mobility data and 872 850 bird observations, we determined that during lockdown, human mobility changes resulted in altered habitat use in 80% (20/25) of our focal bird species. When humans spent more time at home, over half of affected species had lower counts, perhaps resulting from the disturbance of birds in garden habitats. Bird counts of some species (e.g. rooks, gulls) increased over the short-term as humans spent more time parks, possibly due to human-sourced food resources (e.g. picnic refuse), while counts of other species (e.g. tits and sparrows) decreased. All affected species increased counts when humans spent less time at grocery services. Avian species rapidly adjusted to the novel environmental conditions and demonstrated behavioural plasticity, but with diverse responses, reflecting the different interactions and pressures caused by human activity.</p>
ScienceDex guides
Understand access before you commit
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.