Alternative Data Collection Methods Research Articles

A qualitative study of research participation for parents from minority ethnic groups in the UK

Background/Aims Under-representation of pregnant women, their partners and parents from minority ethnic backgrounds has been reported in research involving the perinatal period. This study's aim was to explore the experiences of parents from minority ethnic groups who participated in research during the perinatal period, focusing on barriers and facilitators to participation. Methods In-depth qualitative interviews were conducted via Microsoft Teams by all three researchers with a purposive sample of 16 parents. Data were analysed using thematic analysis. Results Seven overarching themes were identified: motivating factors; unique contributions; language, understanding and access; cultural values, beliefs and practices; lack of time and other commitments; facilitators for participation; and experience of the current study. Conclusions Understanding motivation for participation is essential to developing recruitment strategies that promote involvement of parents from minority ethnic backgrounds. A clear understanding of research goals and perceived benefits, and maximising access to studies, is likely to result in improved recruitment. Implications for practice Ways to encourage research participation from parents in minority ethnic groups include using a range of advertising techniques and venues, offering a choice of time and place for activities and alternative methods of data collection, and disseminating information on available studies in early pregnancy.

Використання великих даних для інформаційного забезпечення ринку праці

The challenges of collecting current labor market data amidst global changes and geopolitical fluctuations increasingly necessitate new approaches and alternative methods of data collection and analysis. This highlights the relevance of research aimed at developing and adapting labor market information provision methods to contemporary challenges. A promising approach in this context is the use of Big Data for labor market assessment, which involves collecting information from sources such as online job search and vacancy portals. This method allows for a deeper analysis of market trends and provides a more accurate and timely assessment of labor market needs and opportunities. The aim of this article is to discuss approaches to developing labor market information systems using Big Data, particularly online data from job vacancy websites. It examines the use of Big Data in labor market analysis based on a database containing over four million job vacancies posted on Ukrainian job search portals over the last five years, provided by the European Training Foundation (ETF). The effectiveness of these approaches in facilitating job search for all interested parties is evaluated, particularly through providing insights into the dynamics of supply and demand in the labor market based on data from these portals. The opportunities and limitations of using Big Data in this context are analyzed, including their impact on employment policy development and labor market planning. The potential benefits of Big Data in providing deeper and more accurate market condition analyses are outlined, along with technical aspects and challenges associated with their processing and interpretation. The article examines methodological approaches to data collection and analytical processing in the context of accelerated transformations, volatility, and limited access to traditional information resources. The scientific novelty of the article lies in the substantiation of the feasibility and appropriateness of using open data from online job portals for labor market information provision under current conditions. In conducting the research, methods of analysis, synthesis, and generalization were applied to identify the main contemporary issues of labor market information provision in Ukraine. The effectiveness of data collection methods based on web scraping and parsing techniques was evaluated, as well as the use of the integrated Snowflake platform to identify key trends and patterns in the labor market. The conclusions summarize the main points and substantiate directions for further research, highlighting the significance of Big Data in developing employment strategies and optimizing the labor market.

Identifying fit-for purpose methods for monitoring fish communities

Scientific monitoring is a fundamental basis of scientific advice. Among others, monitoring aims at contributing towards understanding the influence of anthropogenic use (e.g. fisheries), the health of a stock and individuum and effectiveness of management and conservation measures (e.g. MPAs). Monitoring of demersal and benthic fish communities is often based on invasive methods like bottom trawling, however in some cases less invasive methods might be available. The need for developing alternative and less invasive monitoring methods is supported by an increasing number of Marine Protected Areas and Windfarms where traditional methods such as trawls cannot be deployed due to conservational or technical and safety reasons. To support the development of new monitoring concepts, we conducted a literature review to identify limits and opportunities of methods that are already available. Furthermore, we present a fit-for purpose guide that can help identifying the appropriate method for individual purposes. We defined eight different methods which were analyzed using four different criteria and listed their advantages and disadvantages. We further apply this guide to monitoring in Marine Protected Areas in the Baltic Sea as a case study, indicating that besides traditional bottom trawling, alternative and less invasive methods could be sufficient for specific research purposes. We therefore, encourage scientists and managers to consider alternative data collection methods to minimize environmental impact of scientific sampling. However, our results also indicate that most of the methods still need further refinement especially regarding sampling design, standardization of methods and comparability with established survey methods.

REDCap as an accreditation tool for academic programs in clinical research: A case study

Abstract The Master of Science in Clinical Research Management program at Rutgers Biomedical Health Sciences underwent significant restructuring aligned with the Clinical and Translational Science Award funding parameters. This evolution necessitated formal evaluation through accreditation by the Commission on Accreditation of Allied Health Education Programs. The years-long accreditation process posed challenges, particularly regarding the collection of course learning outcomes data aligned with accreditation competency standards. The objective of this special communication is to report the rationale behind pursuing accreditation for clinical research degrees, the data collection challenges during the accreditation process, and a potential solution. In order to address existing university metric data gaps, Research Electronic Data Capture (REDCap) software was used to develop a data collection tool that streamlined the accreditation process and reduced the administrative burden. REDCap was effective in allowing faculty to self-report 3 years of course outcomes data for accreditation. There was an elevated level of user satisfaction compared to alternative data collection methods. A SWOT analysis identified the strengths and weaknesses of using REDCap, emphasizing strengths in functionality that include customizability, data validation, and compliance with regulatory standards. Overall, the advantages of leveraging REDCap for accreditation data collection, including customization, data security, and user-friendliness outweigh the key disadvantage of REDCap, which is its limited reporting capabilities.

EXPLORING THE WELL-BEING OF SPECIAL NEEDS CHILDREN IN MALAYSIA: A STUDY AT SEKOLAH KEBANGSAAN PENDIDIKAN KHAS KUANTAN

This study investigates the well-being of Malaysian Muslim children with special needs, focusing on various aspects such as social, physical, emotional, intellectual, and spiritual well-being. The research was conducted at Sekolah Kebangsaan Pendidikan Khas Kuantan in Indera Mahkota 2, involving 24 students with Developmental Disabilities (DD) and Learning Disabilities (LD), aged 8-12, and 20 teachers. The children's well-being was assessed through self-report surveys and teacher evaluations, complemented by direct observations. Methodologically, the study combined quantitative and qualitative approaches, using self-report questionnaires and informal interviews. The sample size comprised 41 teachers and 107 students, with a subset of 30 participants for detailed study. Analysis of Variance (ANOVA) and independent sample t-tests were employed to analyse the data. The results showed no significant differences in well-being between LD and DD children. However, disparities were observed in self and teacher ratings, particularly in social and physical well-being, with children rating themselves lower. Emotional, intellectual, and spiritual well-being showed alignment between children's and teachers' perceptions. The study highlights the unique challenges faced by special needs children, including social integration difficulties, emotional stress, and the need for tailored educational strategies. Despite its insights, the study faced limitations, including a small sample size and focus on specific disability categories, which may limit generalizability. Future research should consider larger, more diverse samples and alternative data collection methods. In conclusion, the study underscores the need for comprehensive support for special needs children in Malaysia, encompassing various dimensions of well-being. It calls for the involvement of families, educators, and the broader community in ensuring the holistic development and integration of these children into society..

Building urban predator-prey networks using camera traps

Loss of interspecific interactions often precedes extinction events. Therefore, knowledge of species interactions is important to inform conservation strategies aimed at maintaining biodiversity in a changing world. Collecting data on species interactions can, however, be logistically challenging and costly. Hence, alternative data collection and processing methods are needed, and camera traps potentially represent a cost-effective way to identify the predator-prey interactions needed to generate trophic networks. Here, we compare two types of datasets, derived from camera traps, and test their effectiveness to build bipartite predator-prey networks: one based on predation events (prey observed within a predator's jaws), and one based on potential encounter events (i.e., predator and prey species detected within a defined time threshold). Our analyses focused on two predators, coyotes (Canis latrans) and red foxes (Vulpes vulpes), in an urbanized area (Toronto, Canada). Our findings suggest the two datasets provided complementary information on the bipartite networks' structure: predation events underestimated large mammal prey, and potential encounter events underestimated small mammal prey. Together, the predator-prey interactions that we found reflected previous literature on dietary analysis. To improve the information provided by camera traps on trophic interactions, we suggest taking into account site-specific conditions (e.g., presence of birdfeeders or microhabitat differences), as these may alter both predation events and potential encounter events detected. Furthermore, to improve the accuracy of the predator-prey interactions obtained with potential encounter events, time interval threshold and detection sequence (i.e., prey or predator detected first) should be taken into account when defining a potential encounter event. Potential encounter events provide a promising complementary method for assessing predator-prey interactions from camera traps, in particular when predator photos from camera traps are not available. Future research should devise strategies to estimate and include predator-prey predation likelihood in bipartite networks derived from potential encounter events.

CUSTOMER ATTITUDES TOWARDS THE USE OF MOBILE BANKING APPLICATIONS OFFERED BY PRIVATE BANKS IN BANGLADESH

This qualitative study explores the attitudes of customers towards the use of mobile banking applications offered by private banks in Bangladesh. The study aims to investigate perceived usefulness, ease of use, trust and security concerns, and the benefits of using mobile banking applications. Data was collected through interviews with 40 participants, and the findings were analyzed to identify common themes and patterns. The results indicate that the majority of participants perceive mobile banking applications as useful in facilitating their banking transactions and find them easy to use. Participants also express trust in the security measures implemented by the banks. Additionally, customers value the benefits of using mobile banking applications, including convenience, time-saving, and accessibility. These findings align with previous research and highlight the importance of designing user-friendly applications that offer tangible benefits to customers. Trust and security are crucial factors that influence customer adoption and continuance intention towards mobile banking applications. The study emphasizes the need for strong security measures and transparent communication to build customer trust. The practical implications of the findings suggest that private banks should focus on designing user-friendly and secure mobile banking applications. Effective marketing and communication strategies should highlight the convenience and time-saving benefits of using these applications. Addressing technical issues, improving user experience, and strengthening security measures are essential to promote wider adoption and enhance customer satisfaction. It is important to acknowledge the limitations of this study, as the findings are specific to the context of private banks in Bangladesh. Future research should explore customer attitudes in different contexts and include non-users of mobile banking applications to gain a broader understanding of the factors influencing adoption. Alternative data collection methods could also provide more nuanced insights. This study provides valuable insights into the attitudes of customers towards mobile banking applications in the private banking industry of Bangladesh. The findings can inform the design and implementation of mobile banking applications and contribute to the growing body of literature on mobile banking adoption.

Using social media as a source of analysable material in phonetics and phonology – lenition in Spanish

Abstract The COVID-19 pandemic has shown that alternative methods of data collection are necessary to continue working in certain fields of linguistics. This is a challenge for (socio)phoneticians and phonologists who have to rely on good quality sound but cannot do fieldwork or gather recordings in a traditional manner. In this paper, I show that audio recordings made via social media can help alleviate this problem. To this end, I compared samples from five speakers of dialectal Spanish recorded in a laboratory setting and via a social media application (WhatsApp). The analysis of temporal and spectral characteristics of consonants in postvocalic position shows that recordings made via social media can be successfully used for at least some types of sociophonetic analysis. They also provide some additional advantages for researchers: ease of data collection, potentially large speech corpora, and access to authentic, naturalistic speech which is uninhibited by laboratory conditions or the presence of a researcher and a professional recording device.

A Joint Autoethnography on Collecting Qualitative Data Remotely: Insights From Doctoral Students’ Experiences

Researchers in general and those conducting English Language Teaching (ELT) studies in particular have been using alternative data collection methods and strategies due to recent technological advancements, natural events, as well as personal researchers’ predicaments. In this article, we present our research insights into remote qualitative data collection from the perspective of doctoral students. Using a joint-autoethnography research design, we share our reflections on collecting data from two different studies conducted in English as a Foreign Language (EFL) contexts. We support these reflections with similar literature about remote data collection. Our focus is on the use of remote observations and remote surveys in qualitative studies. In our settings, while collecting data through remote observation, the major challenges we faced were related to technological management for the use of in-class video-recordings. Consequently, the change to audio-recordings demonstrated to be appropriate in such contexts. Furthermore, the use of remote qualitative surveys for data collection taught us that despite a high rate of initial acceptance for participation in other studies, the response rate in our study was quite low. As a result of this challenge, additional data collection methods were used in the relevant study. These experiences reveal that there will be a need to improve our procedures for a successful collection of remote qualitative data, not only in our future studies, but also for those researchers intending to use these data collection methods in similar situations.

Impact of COVID-19 pandemic on censuses of agriculture. Global review

The Food and Agriculture Organization of the United Nations (FAO) coordinates the World Programme for the Census of Agriculture 2020 (WCA 2020). A census of agriculture is a nation-wide statistical operation, undertaken at least once every ten years, that collects data on the structure of agriculture at farm level. As a part of its monitoring role, the FAO receives regular updates from member countries on the effects of the COVID-19 pandemic on the ongoing agricultural census activities. This paper assesses the extent to which agricultural censuses were affected by the COVID-19 pandemic and proposes best practices for minimising its impact. The latest review of censuses in 155 countries and territories shows that COVID-19 has affected censuses of agriculture both in developed and developing countries. However, some countries coped with the pandemic better than others. Prior-to-the-pandemic improvements in national statistical systems, a wide range of information and communication technology (ICT) solutions, and the use of alternative data collection methods and administrative registers helped these countries to reduce significantly their reliance on face-to-face interactions, and instead made them favour online training and remote data collection. This has a potential to further accelerate the pace of innovation in census-taking. For other countries, the experience underscored the need to improve the ICT infrastructure, diversify data collection methods, adopt e-learning for training, and start using administrative registers in future censuses.

Large Surgical Databases with Direct Data Abstraction: VASQIP and ACS-NSQIP.

Direct data abstraction from a patient's chart by experienced medical professional data abstractors has been the historical gold standard for quality and accuracy in clinical medical research. The limiting challenge to population-wide studies for quality and public health purposes is the high personnel costs associated with very large-scale efforts of this type. Two historically related programs that are at least partially able to successfully circumvent this problem and provide high-quality data relating to surgical procedures and the early postoperative period are reviewed in this article. Both utilize similar data abstraction efforts by specially trained and qualified medical abstractors of a sample subset of the total procedures performed at participating hospitals.The Veterans Affairs Surgical Quality Improvement Program (VASQIP), detailed by Nicholas J. Giori, MD, PhD, in the first section of this article, makes use of trained abstractors and has undergone recent additions and updates, including the development of an associated total hip registry for the VA system. The data elements and data integrity provided by both of these programs establish important benchmarks for other "big data" efforts, which often attempt to use alternative less-expensive methods of data collection in order to achieve more widespread or even nationwide data collection.In the second section, Elizabeth B. Habermann, PhD, MPH, provides a detailed review of the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP), the data elements collected, and examples of the range of quality improvement and outcomes studies in orthopaedic surgery that it has made possible, along with information on data that have not been collected and the resulting limitations. The ACS NSQIP was actually modeled after the very similar earlier effort started by the United States Department of Veterans Affairs (VA).

Mediatisasi Politik dan Perilaku Pemilih: Penggunaan Google Trend Sebagai Metode Alternatif Memprediksi Kemenangan Kandidat

In 2020, Indonesia has held simultaneous regional head elections which held governor elections in 9 provinces, mayor elections in 37 cities and regent elections in 224 regencies. Candidates in general elections often use surveys as a reference in estimating their potential for victory, even though to conduct popularity, acceptability and electability surveys require a high level of validity and reliability, for this reason, a large survey team is needed in distributing questionnaires and large funds for operational needs, so it is necessary to develop alternative methods of data collection in predicting candidate winner, other than survey methods. Google Trend as a service from Google that provides site search statistics by displaying the popularity of keyword within a certain time range where statistical results can be displayed by region has the potential to be an alternative method to surveys. In several previous studies in the United States and Canada, Google Trend managed to correctly predict the winner of the presidential election in the US and the winner of the election in Canada. This research fills a gap in the Indonesian context to see the accuracy of Google Trend in predicting the winner of the governor election in 9 provinces in the simultaneous regional elections in 2020. As a result, Google Trend succeeded in predicting the victory of candidates in Jambi, Central Kalimantan, North Sulawesi, West Sumatra; while doubtful in Bengkulu and South Kalimantan; and Google Trend is not accurate in predicting in North Kalimantan, Riau Islands and Central Sulawesi. Google Trend's lack of accuracy in predicting the governor's candidate victory in Indonesia is allegedly because Google Trend's services in Indonesia have not reached the district/district level compared to the United States and Canada which have reached the district/sub-district level. In addition, as an alternative method, there are a number of important notes for Google Trend because it has not been able to distinguish the tone of news whether it is positive or negative because Google Trend is still focused on the number of keyword searches that are most sought after by users, even though the tone of the news is important to see public sentiment.

Camera traps and guard observations as an alternative to researcher observation for studying anthropogenic foraging.

Foraging by wildlife on anthropogenic foods can have negative impacts on both humans and wildlife. Addressing this issue requires reliable data on the patterns of anthropogenic foraging by wild animals, but while direct observation by researchers can be highly accurate, this method is also costly and labor‐intensive, making it impractical in the long‐term or over large spatial areas. Camera traps and observations by guards employed to deter animals from fields could be efficient alternative methods of data collection for understanding patterns of foraging by wildlife in crop fields. Here, we investigated how data on crop‐foraging by chacma baboons and vervet monkeys collected by camera traps and crop guards predicted data collected by researchers, on a commercial farm in South Africa. We found that data from camera traps and field guard observations predicted crop loss and the frequency of crop‐foraging events from researcher observations for crop‐foraging by baboons and to a lesser extent for vervets. The effectiveness of cameras at capturing crop‐foraging events was dependent on their position on the field edge. We believe that these alternatives to direct observation by researchers represent an efficient and low‐cost method for long‐term and large‐scale monitoring of foraging by wildlife on crops.

Maximising Abilities, Negotiating and Generating Exercise options (MANAGE) in people with multiple sclerosis: A feasibility randomised controlled trial

Objective To investigate the feasibility and preliminary efficacy of a group self-management exercise and education program in people with multiple sclerosis. Design Feasibility randomised controlled trial. Setting Outpatient rehabilitation facility. Subjects Twenty-three adults (age 48.6 (11.7) years) recruited from a Multiple Sclerosis Clinic register. Interventions The intervention group undertook a 12-week group program incorporating behaviour change education, exercise and community integration. This was compared with a waitlist control group. Main measures Feasibility was measured by recruitment, adherence and safety. Efficacy outcomes included measures of physical function (6-metre and 6-min walk, Functional Reach) and self-report questionnaires (fatigue, quality of life, exercise benefits and barriers) at baseline, 6, 12 and 24 weeks. Results Of 74 individuals identified through the register, 48 (65%) were contacted and deemed eligible, and 23 (48%) agreed to participate. There was high adherence for attendance at education (57 of 72, 79%) and exercise (135 of 174, 78%) sessions. No adverse safety events occurred within the intervention sessions. Missed attendances at assessment sessions was high (5 to 8 participants missed at each time point) predominately due to health issues. The intervention group demonstrated positive changes in walking endurance, Functional Reach and fatigue, whereas the control had some reductions in walking speed and more perceived exercise barriers. Conclusions The MANAGE program appears feasible and safe for people with mild-to-moderate multiple sclerosis, with high adherence to exercise and education sessions. Future trials should consider strategies such as flexible scheduling or alternative methods of data collection to improve follow-up assessment attendance.

Wound photography for evaluation of surgical site infection and wound healing after lower limb trauma.

Deep surgical site infection (SSI) is common after lower limb fracture. We compared the diagnosis of deep SSI using alternative methods of data collection and examined the agreement of clinical photography and in-person clinical assessment by the Centers for Disease Control and Prevention (CDC) criteria after lower limb fracture surgery. Data from two large, UK-based multicentre randomized controlled major trauma trials investigating SSI and wound healing after surgical repair of open lower limb fractures that could not be primarily closed (UK WOLLF), and surgical incisions for fractures that were primarily closed (UK WHiST), were examined. Trial interventions were standard wound care management and negative pressure wound therapy after initial surgical debridement. Wound outcomes were collected from 30 days to six weeks. We compared the level of agreement between wound photography and clinical assessment of CDC-defined SSI. We are also assessed the level of agreement between blinded independent assessors of the photographs. Rates of CDC-defined deep SSI were 7.6% (35/460) after open fracture and 6.3% (95/1519) after closed incisional repair. Photographs were obtained for 77% and 73% of WOLLF and WHiST cohorts respectively (all participants n = 1,478). Agreement between photographic-SSI and CDC-SSI was fair for open fracture wounds (83%; k = 0.27 (95% confidence interval (CI) 0.14 to 0.42)) and for closed incisional wounds (88%; k = 0.29 (95% CI 0.20 to 0.37)) although the rate of photographically detected deep SSIs was twice as high as CDC-SSI (12% vs 6%). Agreement between different assessors for photographic-SSI (WOLLF 88%, k = 0.63 (95% CI 0.52 to 0.72); WHiST 89%; k = 0.61 (95% CI 0.54 to 0.69)); and wound healing was good (WOLLF 90%; k = 0.80 (95% CI 0.73 to 0.86); WHiST 87%; k = 0.57 (95% CI 0.50 to 0.64)). Although wound photography was feasible within the research context and inter-rater assessor agreement substantial, digital photographs used in isolation overestimated deep SSI rates, when compared to CDC criteria. Wound photography should not replace clinical assessment in pragmatic trials but may be useful for screening purposes where surgical infection outcomes are paramount. Cite this article: Bone Joint J2021;103-B(12):1802-1808.

“A brutal disadvantage for my wife [living with dementia] That has set us back years”: Results of a quantitative survey of caring relatives of people living with dementia during the Covid‐19 pandemic in Germany

BackgroundThe Covid‐19 pandemic poses a major challenge to the entire population. People living with dementia and their caring relatives, mostly older people, are considered a high risk group for infection with Covid‐19. As people living with dementia have a particular need for consistent daily routines and care, the restrictions during the pandemic presented relatives with new burden. Here, we investigated how caring relatives experienced the time during the Covid‐19 pandemic.MethodWe planned to conduct focus groups with different stakeholders in dementia care. Due to contact restrictions, we carried out a quantitative survey with a semi‐structured questionnaire as an alternative method of data collection. Questionnaires (n = 45) including open and closed questions were sent by post to the leaders of four self‐help groups for relatives of people living with dementia and then distributed to the members by the group leaders. We asked about the current health status of the respondents, the limitations during the Covid‐19 pandemic and changes in the care of people living with dementia.ResultA total of 23 questionnaires were completed and returned to us. Participants had a mean age of 70.4 years (range 51 to 88 years). The majority of participants were female (69.6%). More than half of the participants assessed their health status as less good or bad (56.5%). Compared to the health status before the Covid‐19 pandemic, more than one third of respondents felt their own health was deteriorating (39.1%). Feelings of isolation and social exclusion increased in 39.1% of the participants during Covid‐19 pandemic. Most participants suffered from limitations of social contacts and leisure activities. The temporary closure of day care facilities for people living with dementia was a particular burden for caring relatives.ConclusionThe majority of respondents experienced the Covid‐19 pandemic as burdensome with regard to their health and their care responsibilities. The statements of relatives underline the importance of maintaining clear structures in everyday life and in care of people living with dementia even in periods of crisis.

Nursing Human Factor During COVID-19 Pandemic

This paper explores the effects of COVID-19 pandemic on nurses capacity of delivering services to infected patients with minimal risks. The role of nursing during the first four months since the outbreak of the COVID-19 is reviewed. The nursing, both preventive service and response preparedness are evaluated along with their human factor in such a crisis are evaluated. Then the researchers argue about the importance of human factor tools and how they could impact the different challenges and risks that the nurses are going through during the COVID-19 pandemic at various stages, till the time of this paper. A specific framework is exploited, and its implications and limitations are discussed. This research makes two main contributions. From a theoretical perspective, it sheds light on how specific dimensions of human factor could help to raise the capacity of the nurses and their availability, therefore their reliability during a dynamic and complex pandemic as COVID-19. From a practical implication, this study could help prepare nurses for the coming pandemics with better overall productivity effectiveness that would lead to less nurses suffering, beside minimisation of risks and most of all deaths. Future research may extend the present study and test the various propositions made, notably through alternative data collection methods.

Social media analytics in the construction industry comparison study between China and the United States

PurposeThe purpose of this paper is to discover similarities and differences in the construction industry in China and the United States by using data analytic tools on data crawled from social media platforms.Design/methodology/approachThe method comprised comprehensive data analytics using network link analysis and natural language processing tools to discover similarities and differences of social networks, topics of interests and sentiments and emotions on different social media platforms.FindingsFrom the research, it showed that all clusters (construction company, construction worker, construction media and construction union) shared similar trends on follower-following ratios and sentiment analysis in both social media platforms. The biggest difference between the two countries is that public accounts (e.g. company, media and union) on Twitter posted more on public interests, including safety and energy.Research limitations/implicationsThe research contributes to knowledge about an alternative method of data collection for both academia and industry practitioners. Statistical bias can be introduced by only using social media platform data. The analyzed four clusters can be further divided to reflect more fine-grained groups of construction industries. The results can be integrated into other analyses based on traditional methodologies of data collection such as questionnaire surveys or interviews.Originality/valueThe research provides a comparative study of the construction industries in China and the USA among four clusters using social media platform data.

Accuracy of estimates of milk production per lactation from limited test-day and recall data collected at smallholder dairy farms

Milk production per lactation (MPL) is a key metric of dairy farms. Accurate estimation of MPL requires regular recording, which is laborious and costly. In smallholder systems in the tropics, therefore, generally very few records are available to estimate MPL. Cross-sectional studies collect only one single record per lactation, and even longitudinal studies usually yield only a limited number of records per lactation. Such data recording methods, therefore, are sometimes extended with records recalled by farmers. The accuracy of MPL-estimates based on such limited and imperfect data, however, is unknown. The aim of the present study was to assess the accuracy of MPL-estimates from a single record and a limited number of records per lactation, obtained from smallholder dairy farms in Nakuru County, Kenya. Test-day records from a milk recording scheme for 114 smallholders were used to prepare three datasets with: i) a complete number of test-days (CTD, 5803 records), ii) a limited number of test-days (LTD, 1583 records), and iii) a single test-day (STD, 471 records). In addition, farmers’ recall data (i.e. farmers retrieve information from the past, through memory) from a survey of 29 farms with 56 lactations were used to prepare two datasets with: i) a limited number of recall moments per lactation (LRM, 200 records), and ii) a single recall moment per lactation (SRM, 56 records). These five datasets were used to derive MPL-estimates, at individual cow level or at herd level. The latter was done to mimic a situation without individual cow data, but only herd data (i.e. yield and size). MPL-estimates for CTD were set as a benchmark to quantify the accuracies, based on the relative mean absolute error (RMAE) and root mean square error (RMSE), of MPL-estimates for LTD and STD. As a benchmark dataset was absent for recall data, we computed a virtual benchmark to quantify the accuracies of MPL-estimates for LRM and SRM. At cow level, accuracy of MPL-estimates was highest for LTD (RMAE 15%), and lowest for SRM (RMAE 28%), while accuracies for STD and LRM were intermediate (RMAEs ~ 20%). At herd level, accuracy was higher for STD (RMAE 13%) than for SRM (RMAE 25%). We also showed that to detect a difference of, for example, 100 kg in MPL we need 3002 cows for CTD, and between 3620 and 5003 cows when using alternative data collection methods. Hence, depending on the study objective, alternative data recording methods provide labor-saving and cost-effective ways to estimate MPL in data-scarce smallholder dairy systems.

Integration of social spatial data to assess conservation opportunities and priorities

Effective wildlife conservation requires consideration of ecological and social factors, including social acceptability of conservation actions. Using the threatened koala (Phascolarctos cinereus) as a case study, we demonstrate a novel, socio-ecological approach for identifying conservation opportunity that spatially connects landscapes with community preferences to prioritize koala recovery strategies at a regional scale. We conceptualize conservation opportunity as the spatial integration of three sustainability criteria—ecological potential, social acceptability, and economic feasibility. The social acceptability criterion was assessed using a crowdsourced spatial survey that identified spatial preferences for koalas and land uses that impact koala conservation. As a novel approach, we addressed important research questions regarding the design, collection, and analysis of crowdsourced mapping data for identifying socially acceptable conservation opportunities. Public preferences for koalas were mapped closer to home, in higher suitable koala habitats than expected, were more pronounced in conservation and natural areas on public lands, and were mapped less frequently in modified agricultural landscapes. When the multiple criteria (ecological, social, and economic) were included in the conservation assessment, we found the social acceptability criterion exerted the greatest influence on spatial conservation priorities. The systematic assessment of social criteria for conservation using spatial surveys provides information that can be integrated with ecological information to prioritize conservation opportunities. Potential enhancements include expanding survey recruitment efforts and using alternative social data collection methods to achieve greater geographic and socio-demographic representation, and augmenting the economic feasibility assessment with private property values and transaction data from voluntary conservation agreements with private landowners.