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© The Author(s) 2018. Despite a long history of mosquito-borne virus epidemics in the Americas, the impact of the Zika virus (ZIKV) epidemic of 2015-2016 was unexpected. The need for scientifically informed decision-making is driving research to understand the emergence and spread of ZIKV. To support that research, we assembled a data set of key covariates for modeling ZIKV transmission dynamics in Colombia, where ZIKV transmission was widespread and the government made incidence data publically available. On a weekly basis between January 1, 2014 and October 1, 2016 at three administrative levels, we collated spatiotemporal Zika incidence data, nine environmental variables, and demographic data into a single downloadable database. These new datasets and those we identified, processed, and assembled at comparable spatial and temporal resolutions will save future researchers considerable time and effort in performing these data processing steps, enabling them to focus instead on extracting epidemiological insights from this important data set. Similar approaches could prove useful for filling data gaps to enable epidemiological analyses of future disease emergence events.

Cytoplasmic dynein 1 (dynein) is the predominant microtubule minus end-directed motor in animals and participates in a wide range of cellular processes, including membrane trafficking, nuclear migration, and cell division. Dynein's functional diversity depends on co-factors that regulate its subcellular localization, interaction with cargo, and motor activity. The ubiquitous co-factor nuclear distribution gene E (NudE) is implicated in many of dynein's functions, and mutations in NudE cause the brain developmental disease microcephaly. To identify genetic interactors of the Caenorhabditis elegans NudE homolog nud-2, we performed a genome-wide RNAi screen with the null allele nud-2(ok949), which compromises dynein function but leaves animals viable and fertile. Using bacterial feeding to deliver dsRNAs in a 96-well liquid format and a semi-automated fluorescence microscopy approach for counting parents and progeny, we screened 19762 bacterial clones and identified 38 genes whose inhibition caused enhanced lethality in nud-2(ok949) relative to the nud-2(+) control. Further study of these genes, many of which participate in cell division, promises to provide insight into the function and regulation of dynein. ; The authors acknowledge the support of the BioSciences Screening i3S Scientific Platform. Funding for this project was provided by the European Research Council under the European Union's Seventh Framework Programme (ERC grant agreement n o ERC-2013-StG-338410-DYNEINOME to R.G.), by the European Molecular Biology Organization (EMBO Installation Grant 2545 to R.G.), by the Fundacao para a Ciencia e a Tecnologia (IF/01015/2013/CP1157/CT0006 to R.G. and SFRH/BD/103495/2014 to H. R.), and by 'Norte-01-0145-FEDER-000029-Advancing cancer research: from basic knowledge to application', supported by the Norte Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement through the European Regional Development Fund (FEDER)

Electricity usage (demand) data are used by utilities, governments, and academics to model electric grids for a variety of planning (e.g., capacity expansion and system operation) purposes. The U.S. Energy Information Administration collects hourly demand data from all balancing authorities (BAs) in the contiguous United States. As of September 2019, we find 2.2% of the demand data in their database are missing. Additionally, 0.5% of reported quantities are either negative values or are otherwise identified as outliers. With the goal of attaining non-missing, continuous, and physically plausible demand data to facilitate analysis, we developed a screening process to identify anomalous values. We then applied a Multiple Imputation by Chained Equations (MICE) technique to impute replacements for missing and anomalous values. We conduct cross-validation on the MICE technique by marking subsets of plausible data as missing, and using the remaining data to predict this "missing" data. The mean absolute percentage error of imputed values is 3.5% across all BAs. The cleaned data are published and available open access: 10.5281/zenodo.3690240.

Land Use Information is a key dataset required to enable an understanding of the changing nature of our landscapes and the associated influences on natural resources and regional communities. The Victorian Land Use Information System (VLUIS) data product has been created within the State Government of Victoria to support land use assessments. The project began in 2007 using stakeholder engagement to establish product requirements such as format, classification, frequency and spatial resolution. Its genesis is significantly different to traditional methods, incorporating data from a range of jurisdictions to develop land use information designed for regular on-going creation and consistency. Covering the entire landmass of Victoria, the dataset separately describes land tenure, land use and land cover. These variables are co-registered to a common spatial base (cadastral parcels) across the state for the period 2006 to 2013; biennially for land tenure and land use, and annually for land cover. Data is produced as a spatial GIS feature class.

A cross-sectional survey of adolescents and heads of households was done in six urban and rural local government areas in Ebonyi state, Nigeria in August 2018. Modified cluster sampling technique was used to select households from which eligible adolescent boys and girls were recruited. This data article describes two datasets that, for the first time, expansively describe adolescents' sexual and reproductive behaviors in Nigeria. The datasets include variables on adolescents' demographic and socioeconomic characteristics; family relationships; sexual behaviors; awareness and use of contraceptives; access to sexual and reproductive health information and services; gender norms and ideology about adolescent sexuality; and potential strategies for reducing unwanted teenage pregnancies and unsafe abortions. This dataset would be useful to public health researchers and social scientists investigating drivers of adolescent sexual and reproductive behaviour, as well as programme managers seeking potential strategies for improving adolescent health outcomes. The datasets also provide a template that could be replicated for national or regional surveys on adolescent sexual and reproductive behaviours.

Cities and regions have become increasingly engaged in global climate change governance. They are pledging their own climate mitigation targets and participating in membership networks that typically are transnational in nature and engage thousands of subnational governments. Researching these growing trends in participation has been difficult due to the disparate and inconsistent nature of this self-reported data. To facilitate future analyses of these actors, we introduce ClimActor, the largest harmonized global dataset of more than 10,000 city and regional governments participating in networks like the Global Covenant of Mayors for Climate and Energy, C40 Cities for Climate Leadership, ICLEI Local Leaders for Sustainability, among others. We include key contextual information on each actor's population, geographic location, and administrative jurisdiction to facilitate disambiguation of potential overlaps in actions or emissions. We also provide a series of cleaning functions based on phonetic and fuzzy string matching algorithms within an open-source R package to make it easy for anyone to immediately use the ClimActor dataset with other relevant data.

Clinical case reports (CCRs) provide an important means of sharing clinical experiences about atypical disease phenotypes and new therapies. However, published case reports contain largely unstructured and heterogeneous clinical data, posing a challenge to mining relevant information. Current indexing approaches generally concern document-level features and have not been specifically designed for CCRs. To address this disparity, we developed a standardized metadata template and identified text corresponding to medical concepts within 3,100 curated CCRs spanning 15 disease groups and more than 750 reports of rare diseases. We also prepared a subset of metadata on reports on selected mitochondrial diseases and assigned ICD-10 diagnostic codes to each. The resulting resource, Metadata Acquired from Clinical Case Reports (MACCRs), contains text associated with high-level clinical concepts, including demographics, disease presentation, treatments, and outcomes for each report. Our template and MACCR set render CCRs more findable, accessible, interoperable, and reusable (FAIR) while serving as valuable resources for key user groups, including researchers, physician investigators, clinicians, data scientists, and those shaping government policies for clinical trials.

Human activities have increased atmospheric nitrogen and phosphorus deposition rates relative to pre-industrial background. In the Western U.S., anthropogenic nutrient deposition has increased nutrient concentrations and stimulated algal growth in at least some remote mountain lakes. The Georeferenced Lake Nutrient Chemistry (GLNC) Database was constructed to create a spatially-extensive lake chemistry database needed to assess atmospheric nutrient deposition effects on Western U.S. mountain lakes. The database includes nitrogen and phosphorus water chemistry data spanning 1964–2015, with 148,336 chemistry results from 51,048 samples collected across 3,602 lakes in the Western U.S. Data were obtained from public databases, government agencies, scientific literature, and researchers, and were formatted into a consistent table structure. All data are georeferenced to a modified version of the National Hydrography Dataset Plus version 2. The database is transparent and reproducible; R code and input files used to format data are provided in an appendix. The database will likely be useful to those assessing spatial patterns of lake nutrient chemistry associated with atmospheric deposition or other environmental stressors.

This database provides the daily time-series of COVID-19 cases, deaths, recovered people, tests, vaccinations, and hospitalizations, for more than 230 countries, 760 regions, and 12,000 lower-level administrative divisions. The geographical entities are associated with identifiers to match with hydrometeorological, geospatial, and mobility data. The database includes policy measures at the national and, when available, sub-national levels. The data acquisition pipeline is open-source and fully automated. As most governments revise the data retrospectively, the database always updates the complete time-series to mirror the original source. Vintage data, immutable snapshots of the data taken each day, are provided to ensure research reproducibility. The latest data are updated on an hourly basis, and the vintage data are available since April 14, 2020. All the data are available in CSV files or SQLite format. By unifying the access to the data, this work makes it possible to study the pandemic on a global scale with high resolution, taking into account within-country variations, nonpharmaceutical interventions, and environmental and exogenous variables.

Despite a long history of mosquito-borne virus epidemics in the Americas, the impact of the Zika virus (ZIKV) epidemic of 2015–2016 was unexpected. The need for scientifically informed decision-making is driving research to understand the emergence and spread of ZIKV. To support that research, we assembled a data set of key covariates for modeling ZIKV transmission dynamics in Colombia, where ZIKV transmission was widespread and the government made incidence data publically available. On a weekly basis between January 1, 2014 and October 1, 2016 at three administrative levels, we collated spatiotemporal Zika incidence data, nine environmental variables, and demographic data into a single downloadable database. These new datasets and those we identified, processed, and assembled at comparable spatial and temporal resolutions will save future researchers considerable time and effort in performing these data processing steps, enabling them to focus instead on extracting epidemiological insights from this important data set. Similar approaches could prove useful for filling data gaps to enable epidemiological analyses of future disease emergence events.

Scientists from many disciplines need historical administrative boundaries in order to analyse socio-economic data in space and time. In this paper, we present a set of historical data consisting of administrative unit boundaries and exemplary socio-economic attributes for Austrian Silesia, an historical region located in modern Czechia and Poland. The dataset covers nearly 700 administrative unit boundaries on the level of cadastral or political communes and their subparts and was acquired through manual vectorisation of historical maps (1:28,800) from the period 1837–1841. The local-level units can be easily joined into higher-level divisions such as court or political districts for the period 1837–1910. The data can then be combined with statistical data collected approximately every 10 years for a similar period. Within the quality assessment, the relations between cartographic and census data and their credibility are analysed. The present dataset provides many possibilities for joining a wide range of historical statistical data to better understand various demographic and economic processes based on advanced analyses, e.g., by using GIS.

This paper introduces the SEA database (acronym for Spanish Electoral Archive). SEA brings together the most complete public repository available to date on Spanish election outcomes. SEA holds all the results recorded from the electoral processes of General (1979–2019), Regional (1989–2021), Local (1979–2019) and European Parliamentary (1987–2019) elections held in Spain since the restoration of democracy in the late 70 s, in addition to other data sets with electoral content. The data are offered for free and is presented in a homogeneous and friendly format. Most of the databases are available for download with data from various electoral levels, including from the ballot box level. This paper details how the information is organized, what the main variables are on offer for each election, which processes were applied to the data for their homogenization, and discusses future areas of work. This data has many applications, for example, as inputs in election prediction models and in ecological inference algorithms, to study determinants of turnout or voting, or for defining marketing strategies.

The majority of the area contaminated by the Fukushima Daiichi Nuclear Power Plant accident is covered with forests. We developed a dataset for radiocaesium ((137)Cs) in trees, soil, and mushrooms measured at numerous forest sites. The (137)Cs activity concentration and inventory data reported in scientific journal papers written in English and Japanese, governmental reports, and governmental monitoring data on the web were collated. The ancillary information describing the forest stands were also collated, and further environmental information (e.g. climate) was derived from the other databases using longitude and latitude coordinates of the sampling locations. The database contains 8593, 4105, and 3189 entries of activity concentration data for trees, soil, and mushrooms, and 471 and 3521 entries of inventory data for trees and soil, respectively, which were collected from 2011 to 2017, and covers the entire Fukushima prefecture. The data can be used to document and understand the spatio-temporal dynamics of radiocaesium in the affected region and to aid the development and validation of models of radiocaesium dynamics in contaminated forests.

Over 60,000 utility-scale wind turbines are installed in the United States as of October, 2019, representing over 97 gigawatts of electric power capacity; US wind turbine installations continue to grow at a rapid pace. Yet, until April 2018, no publicly-available, regularly updated data source existed to describe those turbines and their locations. Under a cooperative research and development agreement, analysts from three organizations collaborated to develop and release the United States Wind Turbine Database (USWTDB) - a publicly available, continuously updated, spatially rectified data source of locations and attributes of utility-scale wind turbines in the United States. Technical specifications and wind facility data, incorporated from five sources, undergo rigorous quality control. The location of each turbine is visually verified using high-resolution aerial imagery. The quarterly-updated data are available in a variety of formats, including an interactive web application, comma-separated values (CSV), shapefile, and application programming interface (API). The data are used widely by academic researchers, engineers and developers from wind energy companies, government agencies, planners, educators, and the general public.

There have been many attempts to compile comprehensive lists of flame retardants. However, this goal has proven challenging due to the heterogeneity of compounds that can be used as flame retardants coupled with changes in formulation chemistry over time. Flame retardants have been the focus of many recent existing hazard, exposure, and risk assessments. These assessments have been class-based or for individual chemical substances. Here, diverse sets of publicly available data sources from governmental organizations and the open literature were compiled to develop an inventory of chemicals used as flame retardants and organohalogen flame retardants. The chemical substances from these data sources were mapped to appropriate chemical identifiers via manual curation and deduplicated. Despite different data sources containing a large number of overlapping chemical substances, compiling information from multiple data sources was found to increase the breadth of potential flame retardant chemistries. The flame retardant and organohalogen flame retardant inventories were developed as a resource for scientists interested in better understanding properties of flame retardant and organohalogen flame retardant classes.