Find research datasets worth reusing

opencc-by-4.0Jan 2019View details →

A Twitter Dataset for Spatial Infectious Disease Surveillance

Dengue is a mosquito-borne viral disease which infects millions of people every year, specially in developing countries. Some of the main challenges facing the disease are reporting risk indicators and rapidly detecting outbreaks. Traditional surveillance systems rely on passive reporting from health-care facilities, often ignoring human mobility and locating each individual by their home address. Yet, geolocated data are becoming commonplace in social media, which is widely used as means to discuss a large variety of health topics, including the users' health status. In this dataset paper, we make available two large collections of dengue related labeled Twitter data. One is a set of tweets available through the Streaming API using the keywords dengue and aedes from 2010 to 2016. The other is the set of all geolocated tweets in Brazil during the year of 2015 (available also through the Streaming API). We detail the process of collecting and labeling each tweet containing keywords related to dengue in one of 5 categories: personal experience, information, opinion, campaign, and joke. This dataset can be useful for the development of models for spatial disease surveillance, but also scenarios such as understanding health-related content in a language other than English, and studying human mobility.

opencc-by-4.0Aug 2024View details →

Dataset for: Infectious disease responses to human climate change adaptations

Original and derived data products referenced in the original manuscript are provided in the data package. <h3>Description of the data and file structure</h3> Original data: <code>Table_1_source_papers.csv</code>: Papers that met review criteria and which are summarized in Table 1 of the manuscript. <ol> <li>ID: The paper identification number</li> <li>Topic: The broad topic (i.e., each row of Table 1)</li> <li>Authors: The names of the authors of the paper</li> <li>Article Title: The title of the paper</li> <li>Source Title: The name of the journal in which the paper was published</li> <li>Abstract: The paper's abstract, retrieved from the Web of Science search</li> <li>study_type: Classification of the study methodology/approach. "A" = a designed study that shows effect ,"B" = a pre/post study, "C" = a comparison of health outcomes or pathogen risk relative to a 'control/comparison' area, "D" = some quantitative effect but no control, "E" = qualitative comments but little supporting evidence, and/or a qualitative review.</li> <li>pathogen_broad: Broad classification of the type of pathogen discussed in the paper.</li> <li>transmission_type: Categorization of indirect, direct, sexual, vector, or other transmission modes.</li> <li>pathogen_type: Categorization of bacteria, helminth, virus, protozoa, fungi, or other pathogen types.</li> <li>country: Country in which the study was performed or results discussed. When countries were not available, regions were used. NA values indicate papers in which a geographic region was not relevant to the study (i.e., a methods-based study).</li> </ol> Derived data: <code>change_livestock_country.csv:</code> A dataframe containing values used to generate Figure 4a in the manuscript. <ol> <li>County Name: The name of the county in Kenya</li> <li>Sheep and goats 1980: The estimated number of sheep and goats in 1980</li> <li>Sheep and goats 2016: The estimated number of sheep and goats in 2016</li> <li>pct_change_shoat: The percent change in sheep and goat numbers from 1980 to 2016</li> <li>Cattle 1980: The estimated number of cattle in 1980</li> <li>Cattle 2016: The estimated number of cattle in 2016</li> <li>pct_change_cattle: The percent change in cattle numbers from 1980 to 2016</li> <li>Camel 1980: The estimated number of camels in 1980</li> <li>Camel 2016: The estimated number of camels in 2016</li> <li>pct_change_camel: The percent change in camel numbers from 1980 to 2016</li> <li>human_pop 1980: The estimated human population in the county in 1980</li> <li>human_pop 2016: The estimated human population in the county in 1980</li> <li>pct_change_human: The percent change in the human population from 1980 to 2016</li> <li>area_sq_km: The land area of the county</li> <li>change_ind_per_sq_km_shoat: Absolute change in number of sheep and goats from 1980 to 2016</li> <li>change_ind_per_sq_km_cattle: Absolute change in number of cattle from 1980 to 2016</li> <li>change_ind_per_sq_km_camel: Absolute change in number of camels from 1980 to 2016</li> </ol> <code>country_avg_schist_wormy_world.csv</code>: A dataframe containing values used to generate Figure 3 in the manuscript. <ul> <li>Country: The country in which the schistosome prevalence studies were performed.</li> <li>Latitude: The latitute in decimal degrees</li> <li>Longitude: The longitute in decimal degrees</li> <li>Maximum.prevalence: The mean maximum schistosomiasis prevalence of studies conducted within each country.</li> </ul> <code>kenya_precip_change_1951_2020.csv</code>: A dataframe containing values used to generate Figure 4b in the manuscript. <ul> <li>Precipitation (mm): Binned annual precipitation values</li> <li>1951-1980: The density of observations for each annual precipitation value for the 1951-1980 period</li> <li>1971-2000: The density of observations for each annual precipitation value for the 1971-2000 period</li> <li>1991-2020: The density of observations for each annual precipitation value for the 1991-2020 period</li> </ul> <h3>Sharing/Access information</h3> Data were derived from the following sources: <ul> <li> Ogutu, J. O., Piepho, H.-P., Said, M. Y., Ojwang, G. O., Njino, L. W., Kifugo, S. C., & Wargute, P. W. (2016). Extreme wildlife declines and concurrent increase in livestock numbers in Kenya: What are the causes? PloS ONE, 11(9), e0163249. https://doi.org/10.1371/journal.pone.0163249 </li> <li> London Applied & Spatial Epidemiology Research Group (LASER). (2023). Global Atlas of Helminth Infections: STH and Schistosomiasis [dataset]. London School of Hygiene and Tropical Medicine. https://lshtm.maps.arcgis.com/apps/webappviewer/index.html?id=2e1bc70731114537a8504e3260b6fbc0 </li> <li> World Bank Group. (2023). Climate Data & Projections—Kenya. Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org/country/kenya/climate-data-projections </li> </ul>

opencc-by-4.0Aug 2022View details →

Data associated with "Developing a standardized but extendable framework to increase the findability of infectious disease datasets"

Data associated with "Developing a standardized but extendable framework to increase the findability of infectious disease datasets"   Includes: <ul> <li>NIAID Dataset schema</li> <li>NIAID ComputationalTool schema</li> <li>Crosswalk between NIAID schemas and common schemas</li> <li>Survey of Schema.org-compliant repositories</li> </ul> The open access movement and scientific reproducibility concerns have led the biomedical research community to embrace efforts to make scientific datasets openly accessible. While many datasets are now available, there are still challenges in ensuring that they are Findable, Accessible, Interoperable, and Reusable (FAIR). To improve the FAIRness of datasets, we evaluated dataset repositories for compliance with Schema.org standards – a collection of standards developed to increase metadata searchability across the internet. Adoption of the Schema.org Dataset standard was highly variable in biomedical research datasets, and the standard omitted many desirable metadata fields. We customized the Schema.org Dataset standard to catalog datasets collected across a Systems Biology research consortium consisting of 15 Centers. We developed a reusable process for creating a schema which is interoperable with other standards, but still extendable and customizable to a particular context. Here, we describe our process along with the associated gains in FAIRness, and discuss ongoing challenges with dataset discoverability – the first step to ensure that the vast amount of open data published by the research community is reused to its maximum value.

opencc-by-4.0Feb 2023View details →

Social contact patterns relevant for infectious disease transmission in Cambodia

Social contact data from a community-based survey conducted in Cambodia in 2012.

opencc-by-4.0Nov 2023View details →

A meta-analysis on global change drivers and the risk of infectious disease database and code

Data and code associated with the manuscript "A Meta-analysis on Global change drivers and the risk of infectious disease".

opencc-zeroJun 2024View details →

Impact of infectious diseases on wild bovidae populations in Thailand: Insights from population modelling and disease dynamics

The wildlife and livestock interface is vital for wildlife conservation and habitat management. Infectious diseases maintained by domestic species may impact threatened species such as Asian bovids, as they share natural resources and habitats. To predict the population impact of infectious diseases with different traits, we used stochastic mathematical models to simulate the population dynamics over 100 years for 100 times a model gaur (Bos gaurus) population with and without disease. We simulated repeated introductions from a reservoir, such as domestic cattle. We selected six bovine infectious diseases; anthrax, bovine tuberculosis, hemorrhagic septicaemia, lumpy skin disease, foot and mouth disease and brucellosis, all of which have caused outbreaks in wildlife populations. From a starting population of 300, the disease-free population increased by an average of 228% over 100 years. Brucellosis with frequency-dependent transmission showed the highest average population declines (-97%), with population extinction occurring 16% of the time. Foot and mouth disease with frequency-dependent transmission showed the lowest impact, with an average population increase of 200%. Overall, acute infections with very high or low fatality had the lowest impact, whereas chronic infections produced the greatest population decline. These results may help disease management and surveillance strategies support wildlife conservation.

opencc-by-4.0Feb 2021View details →

Infectious disease dataset

This dataset contains 12090+ of unique diseases and 600 different symptoms. Each of these disease contains a different amount of symptoms while a disease have less amount of symptoms the field will be a null value. This dataset contains a total of 17 columns. Disease: The disease name Symptom_1: The first symptom of the disease Symptom_2: The second symptom of the disease Symptom_3: The third symptom of the disease Symptom_4: The fourth symptom of the disease Symptom_5: The fifth symptom of the disease Symptom_6: The sixth symptom of the disease Symptom_7: The seventh symptom of the disease Symptom_8: The eighth symptom of the disease Symptom_9: The ninth symptom of the disease Symptom_10: The tenth symptom of the disease Symptom_11: The eleventh symptom of the disease Symptom_12: The twelfth symptom of the disease Symptom_13: The thirteenth symptom of the disease Symptom_14: The fourteenth symptom of the disease Symptom_15: The fifteenth symptom of the disease Symptom_16: The sixteenth symptom of the disease Symptom_17: The seventeenth symptom of the disease

opencc-zeroJan 2023View details →

Data and code for: Local infectious disease experience influences vaccine refusal rates: a natural experiment

Vaccination has been critical to the decline in infectious disease prevalence in recent centuries. Nonetheless, vaccine refusal has increased in recent years, with complacency associated with reductions in disease prevalence highlighted as an important contributor. We exploit a natural experiment in Glasgow at the beginning of the 20th century to investigate whether prior local experience of an infectious disease matters for vaccination decisions. Our study is based on smallpox surveillance data and administrative records of parental refusal to vaccinate their infants. We analyse variation between administrative units of Glasgow in cases and deaths from smallpox during two epidemics over the period 1900–1904, and vaccine refusal following its legalisation in Scotland in 1907 after a long period of compulsory vaccination. We find that lower local disease incidence and mortality during the epidemics were associated with higher rates of subsequent vaccine refusal. This finding indicates that complacency influenced vaccination decisions in periods of higher infectious disease risk, responding to local prior experience of the relevant disease, and has not emerged solely in the context of the generally low levels of infectious disease risk of recent decades. These results suggest that vaccine delivery strategies may benefit from information on local variation in incidence.

Fig. 5 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities

Fig. 5. Data sources according to epidemiological level and scale. Representation of the data sources (left column) used for each epidemiological level (central column), and the scale of the corresponding data sources (right). Colors of the left column correspond to general data-sources categories; for example, green corresponds to biological/biodiversity data sources (e.g., Biodiversity repositories and biological general source). Health-related sources are represented in purple (Health gov: governmental, init-program: initiative or programs). Using this broad categorization, most of the sources contribute with data related to the three epidemiological levels, although with an unpaired flow. For example, scientific literature has a lower contribution for hosts/ reservoirs, and biodiversity-biological sources have a minor contribution for pathogens. Most data sources have a global scale meanwhile governmental sources have a relevant contribution to pathogen data. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 3 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities

Fig. 3. Diseases explored in the studies according to the use of GBIF and the taxa class of the causal pathogen. In the right panel: positive studies (i.e., those studies that used GBIF-mediated data for at least one of the variables explored), negative studies in the left. Bars represent the number of studies exploring each disease, and filling colors represent the corresponding taxa class of the disease agent or causal pathogen (Purple scale, with lighter coloration for fungal diseases, followed by parasites, bacteria, and viruses with the darker purple). Abbreviations: the abbreviation Oth (Fungal Oth, Parasite Oth, Bacteria Oth, Virus Oth) represents a category with multiple species, merged to simplify the figure due to the low number of studies of each disease. Ricket-related: diseases related to Rickettsia species; Paras: parasites; Schistos: Schistosomiases; Leishm: Leishmaniases (both cutaneous and visceral); Dis: disease; Bact: bacteria; Fev: fever; V: virus; CoronaV: Coronavirus. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 2 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities

Fig. 2. Research areas identified in the studies. Research areas subcategories are represented in the left axis, and general research area groups in the right axis. Orange circles represent the number of positive studies, the blue circles the negatives, and the black lines between them represent the differences in the number of studies, in which larger lines represent larger differences between positive and negatives. Orange icons correspond to research areas with larger number of positive studies, i.e., positive studies were more related to Biology (Bio), Ecology (Ecol) and Other (Hum Soc: Human society; Phy Env Geo: Physical environmental geology; Earth Atm: Earth and atmospheric sciences). Negative studies were more frequent in research areas with blue icons, including Medical (Med) and Veterinary sciences (Vet: Veterinarian and agriculture). In the green icon (Eng Inf Mat: Engineering, informatics, and mathematics) there was no major differences between groups. Subcategories: Bio Zoo: Biology and zoology; Bio Evo Gen: Biology, evolution, and genetics; Bio Bioch: Biology and biochemistry; Env Mang: Environmental management and sciences; Eco App: Ecological applications; Math Stat: Mathematical statistics; Inf Comp: informatics and computing; Eng Geom: Engineer and geometrics; Microb: Microbiology; Pub Heal: Public health; Med Micro: Medical microbiology; Med Clin Heal: Medical clinical and health. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 1 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities

Fig. 1. General framework of analyses at study- and variable-levels. In the upper section (study-level, in grey), studies are divided in those that used GBIF-mediated data (positives, in orange) and those that did not (negatives, in blue). Positive studies were group according if GBIF was used as the only data source for all variables (2 studies), or if the variables were based on GBIF together with other data sources (105 studies). In the variable-level section (bottom, white background) the total 358 variables extracted from the positive and negative studies were categorized according to the specific use of GBIF, resulting in five types of variables, four of them extracted from the positive studies. Note that in those studies based on GBIF, the different variables could be based on GBIF alone (33 variables), GBIF together with other sources (85), or specific variables may not be based on GBIF-mediated data at all (81 variables). Finally, each variable was related to different epidemiological roles, resulting in a larger number hosts/reservoirs variables, mostly based on GBIF-mediated data, and a higher presence of pathogen species-variables not using GBIF. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 4 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities

Fig. 4. Variables according to taxon class (Y- axis) epidemiological level (bar colour) and the use of GBIF- mediated data. Bars represent the number of variables by each taxon class (Y-axis), separated in two panels according to the use of GBIF-mediated data. In the right panel, variables in which GBIF-mediated data was used (Used_GBIF), in the left panel variables in which data was not obtained from GBIF (NonGBIF). Next to the bars, the number of variables by each epidemi- ological level, and percentage in rela- tion to the total number of variables of each group (Used_GBIF: 120 and Non- GBIF: 238). Taxon classes are grouped by taxonomic associations (e.g., birds, primates, ticks, mosquitoes); however, some were merged to simplify the figure. For example, mamm/oth/var includes multiple mammal species which were sparsely mentioned; simi- larly, hosts/res var, vector other and path other grouped several species participating as hosts/reservoirs, vec- tors, and pathogens, respectively. Bar colors represent epidemiological levels (pathogens, vectors, hosts/reservoirs), and Other (in sienna) includes species participating as hosts' regulator, predators, among others. GBIF-mediated data was only used in three pathogen variables (purple), representing only 2.5% of the variables in which GBIF-mediated data was used, resulting in a remarkable difference with other sources (NonGBIF), in which pathogens represented a 59.7%. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

openmit-licenseMar 2023View details →

Celluloepidemiology: a novel paradigm for quantifying infectious disease dynamics through T-cell modelling on a population level

T-cell receptor sequencing (TCR-seq) was performed on enriched CD8+ T-cells. TCR clonotype annotation was performed using MiXCR v.3.0.13 with the default input parameters. Full origin and method description available in: Celluloepidemiology: a novel paradigm for quantifying infectious disease dynamics through T-cell modelling on a population level

opencc-zeroJul 2023View details →

Data for: The role of temperature in the start of seasonal infectious disease epidemics

Many infectious diseases display strong seasonal dynamics. When both hosts and parasites are influenced by seasonal variables, it is unclear if the start of an epidemic is limited by host or parasite factors or both. The Daphnia-Pasteuria host-parasite system exhibits seasonal epidemics. We aimed to ascertain how temperature contributes to the timing of P. ramosa epidemics in early spring. To this aim, we experimentally disentangled this effect from the effects of temperature on host development and phenology and from that of host traits on parasite time to visible infection. We hypothesized that the parasite is additionally directly limited by low temperatures beyond its need for available hosts. We found that parasite time to visible infection decreased with increasing temperature at a faster rate than host time to hatching and maturity did, consistent with this hypothesis. We also found that hosts hatched from sexual resting stages are less likely to become infected than those produced clonally and that hosts resistant to many known parasite strains are slower to show signs of visible infection compared to those susceptible to many. Together, these results imply that climate change could lead to earlier seasonal epidemics for this host-parasite system, which may also impact longer-term population dynamics.