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The pooled cross-sectional dataset contains 2643 observations including 51% manufacturing and 41% service firms. * The dataset includes data from wave 1 (2005-2007), wave 2 (2008-2010), and wave 3 (2016-2018) of the Nigerian business innovation surveys. * The year variable identifies the different survey waves. Wave 1 was completed in 2008, wave 2 in 2011, and wave 3 in 2019. * The service variable sorts the observations broadly into manufacturing and services. * The id variable identifies each unique firm. REPEATEDNESS WAS IGNORED BECAUSE REPEATED CASES ARE ONLY ABOUT 2.5%. * As much as possible, variables have been matched across the three waves. * Due to coding changes and some inconsistencies in the survey instrument, a few variables could not be matched. * Any variable that could not be matched is retained in its original form. * Some of the variables have notes attached to them. The notes are consistent with what is in the accompanying codebook (Excel document). * Item numbering on the questionnaire for the three waves is not consistent. Thus, rather than use question numbers for variable names as is commonly done, intuitive variable names and labels (defined in detail in the accompanying codebook) are used. * Definitions of main concepts can be found in the accompanying codebook. * It is strongly recommended that users thoroughly familiarize themselves with the accompanying codebook as well as the questionnaires for each of the waves before applying the dataset. This is crucial especially because of the skip patterns. While everything was done to ensure that the skip patterns were all correctly established, there can be no guarantee of perfection. * It is also strongly recommended that users be familiar with the nature of business innovation surveys as this will help in understanding how to treat the data for analysis. The Oslo Manual (third and fourth editions), which are freely available online, are very useful resources. To have a feel of the sectoral distribution of the sample, type in Stata: tab service year

The Replication Folder provides the data and codes to replicate the results in our paper. Please use the do file named "program. do" in the "replication package" and perform the following codes in Stata to reproduce the results of Tables 4-19 and Figure 2. Tables 1-3 are made by the SPSS software, and it is inconvenient to present their operation process by codes. As for the data of Figure1, please refer to "Figure1 data.xlsx" in the "replication package."

Le carbone noir est une petite particule d'aérosol (ou aérienne) de courte durée de vie liée au réchauffement climatique et aux effets nocifs sur la santé. Il est rejeté par la combustion incomplète de carburants à base de carbone (c.-à-d. les combustibles fossiles, les biocarburants ou le bois) sous la forme de matière particulaire très fine. Le carbone noir n'est pas rejeté seul, mais en tant que composante d'une matière particulaire d'un diamètre inférieur ou égal à 2,5 micromètres (PM2,5). En tant que membre du Conseil de l'Arctique, le Canada est engagé à produire un inventaire annuel des émissions de carbone noir. Ces données serviront à informer les Canadiens au sujet des émissions de carbone noir et à fournir des renseignements inestimables pour l'élaboration de stratégies de gestion de la qualité de l'air. Les données utilisées pour la compilation du rapport proviennent des sections de l'Inventaire des émissions de polluants atmosphériques (IEPA) en particulier pour les émissions de matières particulaires fines (PM2,5) provenant de sources liées à la combustion. Nous contacter : apei-iepa@ec.gc.ca Renseignements supplémentaires Pour un complément d'information sur l'Inventaire des émissions de carbone noir du Canada, consulter : https://Canada.ca/carbone-noir Pour les émissions canadiennes d'autres polluants atmosphériques, se reporter à l'Inventaire des émissions de polluants atmosphériques : https://www.canada.ca/fr/environnement-changement-climatique/services/polluants/inventaire-emissions-atmospheriques-apercu.html Outil d'interrogation interactif de l'IEPA et carbone noir : https://pollution-waste.canada.ca/air-emission-inventory/?GoCTemplateCulture=fr-CA Soutien aux projets : Inventaire des émissions de carbone noir au Canada 2013-2021 Black carbon is a short-lived, small aerosol (or airborne) particle linked to both climate warming and adverse health effects. It is emitted from incomplete combustion of carbon-based fuels (i.e., fossil fuels, biofuels, wood) in the form of very fine particulate matter. Black carbon is not emitted on its own, but as a component of particulate matter less than or equal to 2.5 micrometres in diameter (PM2.5). As a member of the Arctic Council, Canada has committed to producing an annual inventory of black carbon emissions. This data will serve to inform Canadians about black carbon emissions and provide valuable information for the development of air quality management strategies. The data used to compile the report originate from sections of the Air Pollutant Emission Inventory (APEI) specifically fine particulate matter (PM2.5) emissions from combustion-related sources. Contact us: apei-iepa@ec.gc.ca Supplemental Information For more information on Canada's Black Carbon Inventory, please visit: https://Canada.ca/black-carbon For Canada's emissions of other air pollutants, please reference the Air Pollutant Emission Inventory: https://Canada.ca/APEI APEI and Black Carbon Interactive Query Tool: https://pollution-waste.canada.ca/air-emission-inventory Supporting Projects: Canada's Black Carbon Inventory for 2013-2021

The scientific community reacted quickly to the Covid-19 pandemic in 2020, generating an unprecedented increase in publications. Many of these publications were released on preprint servers such as medRxiv and bioRxiv. It is unknown however how reliable these preprints are, and if they will eventually be published in scientific journals. In this study, we use crowdsourced human forecasts to predict publication outcomes and future citation counts for a sample of 400 preprints with high Altmetric scores. Most of these preprints were published within one year of upload on a preprint server (70%), and 46% of the published preprints appeared in a high-impact journal with a Journal Impact Factor of at least 10. On average, the preprints received 162 citations within the first year. We found that forecasters can predict if preprints will be published after one year and if the publishing journal has high impact. Forecasts are also informative with respect to preprints’ rankings in terms of Google Scholar citations within one year of upload on a preprint server. For both types of assessment, we found statistically significant positive correlations between forecasts and observed outcomes. While the forecasts can help to provide a preliminary assessment of preprints at a faster pace than the traditional peer-review process, it remains to be investigated if such an assessment is suited to identify methodological problems in pre-prints. The dataset consists of survey responses collected through Qualtrix. Data was formatted and stored as .csv, and analysed with R.

Rising numbers of wild ungulates in human-dominated landscapes of Europe can induce negative effects like damages to forests. Therefore, effective wildlife management, including harvesting through hunting is becoming increasingly important. However, current hunting practices often fail to diminish those negative effects, as many ungulate species retreat to areas unsuitable for hunting. This predator-avoidance behaviour makes it difficult to fulfill the demand of reducing population numbers. Thus, there is an urgent need for innovative and effective wildlife management tools to counteract this problem. Here we provide for the first time a hunting suitability model for wild ungulate management in mountainous landscapes to visualise hunting suitability objectively and realistically. Using red deer as a model species, we modelled hunting suitability with high spatial resolution (10 x 10 m), based on remote sensing information, field surveys, and expert knowledge of professional hunters. We analysed spatio-temporal habitat selection by radio-collared deer in relation to locations of varying hunting suitability. The suitability of various locations regarding hunting influenced the spatio-temporal habitat selection by this species, consistent with our hypothesis. Red deer avoided areas suitable for hunting during daylight hours in the hunting season, but not during the night. This species seems to perceive a landscape of heterogeneous anthropogenic predation risk, shaped by locations of various hunting suitability, as we modelled it. This confirms the empirical realism of the model. Concerning wild ungulate management, our hunting suitability model provides high-resolution predictions of where species like red deer will retreat when perceived anthropogenic predation risk increases. The model also yields useful insights regarding the hunting suitability of particular locations, which is valuable information especially for non-locals. Furthermore, the model can serve as planning tool to inform decisions about where particular hunting strategies can be performed most efficiently to manage wild ungulates and therefore minimize human-wildlife conflicts. See README_hunting_suitability_model.txt See manuscript

This document contains all graphs presented in chapter 1 of the World Development Report 2020. For references of the papers cited in this document please refer to the report itself. Please cite “World Development Report 2020: Trading for Development in the Age of Global Value Chains. Washington, DC: World Bank.”

Responsible Research and Innovation (RRI) seeks to align the research results with the needs of society to respond to current and future problems, also encompassing financial instruments, innovative public policies, and the distribution of resources. These policies should prioritize research in those areas with the greatest impact on society, and particularly in health research, this impact should be focused on responding to clinical problems of the population rather than economic impact. A methodology is proposed that uses information from open sources to identify discrepancies between the results of the most translational research of a region and its health needs, in order to provide useful information for the formulation of innovation policies.

Data derivatives from analysis of seabird tracking data. These data allow one to reproduce the results of the paper "Global political responsibility for the conservation of albatrosses and large petrels by Beal et al (in press). This communication reflects only the authors' view and the Research Executive Agency of the European Union is not responsible for any use that may be made of the information it contains.

A survey consisting of a combination of close-ended questions and open-ended questions was developed to examine use of species occurrence data and web-based species occurrence information systems by professionals in the United States. This dataset only includes responses to closed-ended questions of the survey. Most closed-ended questions were developed as individual Likert-type items. Selection of one response was allowed per closed-ended question except for those that included ‘select all that apply’. A list of names and emails of potential survey participants was compiled from websites of relevant academic departments in universities of the United States classified as doctoral universities of highest research activity in the 2015 Carnegie Classification of Institutions of Higher Education. Names and emails of potential government participants were compiled from websites of natural resource agencies in each of the fifty states of the United States and the federal government. The survey was administered online via Qualtrics. Invitations to participate in the online survey were sent from Qualtrics to academic and government professionals. A generic link to the survey was also provided to participants who had expressed interest in inviting other colleagues to participate in the survey. The online survey was open to all participants for one month in March 2017. The survey was opened a second time in June 2017 to invitees from a government agency that required clearance before staff could participate in the survey. The online survey was permanently closed on July 2017. Web-based information systems designed to increase access to species occurrence data for use in research and natural resource decision-making have become more prevalent over the past few decades. The effectiveness of these systems depends on their usability and extent of use by their intended audiences. We conducted an online survey of academics and government professionals in the United States to compare their species occurrence data needs and their perceptions and use of web-based species occurrence information systems. Our results indicate that although views and perceptions held by academics and government professionals about the importance, usefulness, and ease of use of these information systems tend to be similar, there were differences in their use of species occurrence data and web-based species occurrence information systems. The baseline information obtained in this study will help inform future directions for improvements in species occurrence information systems. survey_professionals_US_closedQ Dataset used in analyses about the use of species occurrence data and web-based species occurrence information systems by academics and government professionals in the United States. This dataset contains responses to closed-ended questions from an online survey conducted in 2017. survey_professionals_US_closedQ.csv README_survey_professionals_US_closedQ README file with information about methods and contents of the dataset. README_survey_professionals_US_closedQ.txt