Productive Data Research Articles

Competing digital capacities: between state-led digital governance and local data center tradeoffs

ABSTRACT Governments worldwide are embracing digital transformation, envisioning limitless data capacities for public policy. However, this paper uncovers a crucial oversight: the neglect of material resources sustaining digital ecosystems. This paper introduces contested ‘digital capacities’ in public policy, to show how the aspiration for unlimited data processing strains local resources. Through a comparative analysis of national digitization strategies in the United States and the United Kingdom, this paper reveals a disconnect between the sociotechnical imaginaries and infrastructural demands underpinning these strategies. The infrastructural demands are leading to social and environmental tradeoffs in regional areas strained by digital infrastructure development. I argue that these competing digital capacities – the capacity to govern through digitization and related resource capacities – provide productive data friction for governments to pause and assess the broader social and ecological costs of their national digitization policies. As such, this article emphasizes the need for policymakers to align national digital governance strategies with regional planning for equitable outcomes. By reevaluating competing digital capacities encompassed in national sociotechnical imaginaries, this paper highlights the urgent need for a balanced approach to digitization.

Acceleration of data collection and analysis using asynchronous programming tools in Web Scraping

This article discusses the importance and relevance of using web scraping technologies to effectively collect a significant amount of information in various fields. The potential of using asynchronous tools for fast and productive data retrieval from large-scale web resources has been studied. The article analyzes in detail the possibilities of using asynchronous tools in the context of web scraping, considering their advantages compared to synchronous approaches. Special attention is paid to the use of the Requests libraries, which provide tools for the standard linear approach, and Asyncio, Aiohttp, which help implement the asynchronous approach. After that, the article conducts a comparative analysis of their performance in the scenario of data collection from the website. The development process using the Python programming language is described in detail, and code is presented that illustrates each stage of the execution of the synchronous and asynchronous algorithms in combination with the libraries presented. The authors of the article consider asynchronous web scraping as a powerful tool for creating fast and efficient data collection mechanisms that can be used to train models and analyze large volumes of information. The article discusses the importance of further development of this method in order to ensure high speed of data collection and improve their applicability in various areas. It demonstrates the practical advantages of asynchronous web scraping, and also indicates the prospects for improving this method to improve the collection and processing of information on a scale that goes beyond standard methods. Further research may consider aspects of automating and expanding the capabilities of asynchronous web scraping, as well as the impact of this approach on the development of other areas of information technology. Taking these aspects into account will contribute to the further evolution and optimization of web scraping technologies for a wide range of applications.

Characterization of the Cattle Production Systems in the Department of Cundinamarca (Colombia), Proposals for Sustainability

The aim of this document is to analyze the bovine production systems in the Department of Cundinamarca (Colombia) as an initial part of the strategic plan to implement sustainable and efficient technological proposals in this region of the Andes. Descriptive and productive agricultural data related to bovine production of the 116 municipalities were analyzed using a multivariate analysis method (principal component, cluster and variance analysis). The information contained quantitative variables regarding three topics: general information (geographical, cadastral and productive), a municipal bovine inventory, and productive information about the cattle farms. The analysis enabled us to generate three clusters of bovine production systems as follows: Cluster 1, representative of the dual-purpose activity, made up of 48 medium-climate municipalities, of which 64.6% of the municipal farms correspond to dual-purpose production with an average farm size of 5.2 ha, a carrying capacity of 1.2 UGM/ha and milk production per cow of 7.75 L/d; Cluster 2, made up of 36 cold-climate municipalities dedicated mainly to dairy production on farms (65.9%) of approximately 3.8 ha, with a carrying capacity of 2.3 UGM/ha and milk production per cow of 14.34 L/d.; and Cluster 3, made up of 32 warm-climate municipalities dedicated mainly to beef production on farms. In this cluster, 66.6% of the farms correspond to beef production of approximately 8.5 ha, with a carrying capacity of 1.2 UGM/ha and milk production of 6.35 L/d. Colombia’s climatic diversity and the wide range of bovine livestock activities are among the main strengths that make up the system’s advantages. However, the low technological development in the majority of the bovine livestock farms, which is related to variables such as low carrying capacity, as in Clusters 1 and 3 and with the low employment generated by bovine production in Colombia, are opportunities to improve the livestock industry. It is advisable to continue working to obtain data on important aspects of the system such as technology, nutrition, reproductive management (including artificial insemination), the economy and environmental and social aspects, in order to delve deeper into the sustainability of productive systems.

From improvement to relationship management: a case study of data artifact creation and use in a school improvement network

ABSTRACT Improvement networks are a relatively recent phenomenon in US education that create interorganizational networks of educators working together to improve specific educational problems. A shared emphasis of these networks is the use of data to support the improvement process, but little is known about their data use in practice. This study takes an in-depth look at the 800+ data artifacts created and used in a single focal network’s 3rd year, applying a mixed methods case study design. The findings show that while data artifacts are predominantly created to support the improvement process, they are frequently used for relationship management as network hub members navigate the sociopolitical dynamics typical of a complex change effort – if they are used at all. The study has implications for practitioners as they decide what data artifacts to create and use, as well as for researchers’ theory building about productive data use in improvement efforts.

Genetic diversity and phylogenetic relationship of Angus herds in Hungary and analyses of their production traits.

This study aims to investigate the genetic structure and characteristics of the Angus cattle population in Hungary. The survey was performed with the assistance of the Hungarian Hereford, Angus, Galloway Association (HHAGA). Genetic parameters of 1,369 animals from 16 Angus herds were analyzed using the genotyping results of 12 microsatellite markers with the aid of PowerMarker, Genalex, GDA-NT2021, and STRUCTURE software. Genotyping of DNA was performed using an automated genetic analyzer. Based on pairwise identity by state values of animals, the Python networkx 2.3 library was used for network analysis of the breed and to identify the central animals. The observed numbers of alleles on the 12 loci under investigation ranged from 11 to 18. The average effective number of alleles was 3.201. The overall expected heterozygosity was 0.659 and the observed heterozygosity was 0.710. Four groups were detected among the 16 Angus herds. The breeders' information validated the grouping results and facilitated the comparison of birth weight, age at first calving, number of calves born and productive lifespan data between the four groups, revealing significant differences. We identified the central animals/herd of the Angus population in Hungary. The match of our group descriptions with the phenotypic data provided by the breeders further underscores the value of cooperation between breeders and researchers. The observation that significant differences in the measured traits occurred among the identified groups paves the way to further enhancement of breeding efficiency. Our findings have the potential to aid the development of new breeding strategies and help breeders keep the Angus populations in Hungary under genetic supervision. Based on our results the efficient use of an upcoming genomic selection can, in some cases, significantly improve birth weight, age at first calving, number of calves born and the productive lifespan of animals.

The autumn low milk yield syndrome in Brown Swiss cows in continental climates: hypotheses and facts.

Extensive research has been conducted globally on the impact of heat stress (HS) on animal health and milk production in dairy cows. In this article, we examine the possible reasons for the decrease in milk production in Brown Swiss (BS) cows during the autumn season, known as the autumn low milk yield syndrome (ALMYS). This condition has been extensively studied in high-yielding Holstein Friesian (HF) cattle and has also been observed in BS cows with a daily milk yield of around 30kg. Our hypothesis is that the drop in milk yield and the increased prevalence of mastitis in autumn, as found in our recent studies, may be a long-term consequence of summer HS. We re-evaluate our previous findings in light of the possible manifestation of an HS-related form of ALMYS in BS cows. As milk yield, mastitis spread, and reproductive function of cows are interrelated and have seasonal dependence, we examine the consistency of our hypothesis with existing data. The significant drop in milk yield in BS cows in autumn (by 2.0-3.2kg), as well as the threshold of milk yield decrease (temperature-humidity index of 70.7), may point in favour of the manifestation of ALMYS in BS cows, similar to HF cows. Only the percentage effect of seasonal factor (59.4%; p < 0.05) on milk yield of BS cows was significant. HS-related ALMYS provides a robust conceptual framework for diverse sets of productive and animal health data in BS cows, similar to observations in high-yielding HF cattle. However, the limitations associated with the lack of additional data (e.g. immunological indicators) suggest the need for further research to confirm ALMYS in BS breed.

Multiprotocol Backscatter With Commodity Radios for Personal IoT Sensors

We present multiscatter, a novel battery-free backscatter design that can simultaneously work with multiple excitation signals for personal IoT sensors. Specifically, we show for the first time that the backscatter tag can identify various excitation signals in an ultra-low-power way, including WiFi, Bluetooth, and ZigBee. Further, we employ a new modulation approach, overlay modulation, that can leverage those excitation signals to convey tag data on top of productive data, which makes decoding both data possible with only a single personal radio. Moreover, we introduce a low-power listening scheme to improve energy efficiency. Since 2.4 GHz signals and personal radios are everywhere, multiscatter is readily deployable in our everyday IoT applications. We prototype multiscatter using an FPGA and various commodity radios. Extensive experiments show that for mixed 802.11b&n, Bluetooth and ZigBee signals, the average identification accuracy of four protocols is more than 93%. The maximal aggregate throughput of both productive and tag data is 278.4 kbps with a single Bluetooth radio. When the transmitter-to-tag distance is increased from 0.2 to 1.8 m, the maximal communication for BLE drops from 71 m to 29 m. And it can leverage excitation diversity to provide uninterrupted communication and greater throughput gains, whereas the single-protocol tag being idle when carrier signals are unavailable. With indoor office light as harvesting sources, the low-power listening scheme can support backscatter rate at 12 pkts/s.

The variation of IGF2 maker is associated with growth traits in Thai native (Kradon) pigs.

To investigate polymorphisms of the MC4R and IGF2 genes and to evaluate the growth traits affected by such polymorphisms in Thai native (Kradon) pigs. Blood samples and productive data from 91 Kradon pigs were collected. DNA was extracted and quantified, the IGF2 and MC4R genes were amplified, and the polymerase chain reaction (PCR) produces were digested using the polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP) technique. Genotyping was performed, and the association between genotypes and growth traits on the birth and weaning weights were evaluated. The IGF2 intron7 g.162G>C variations in Kradon pigs were found in three genotypes: (1) GG, (2) GC, and (3) CC. The GG genotype frequency was the highest followed by the GC and CC genotypes. The frequencies of the G and C alleles were 0.703 and 0.297, respectively. The MC4R genotype was found in only one genotype (GG). The IGF2 gene pattern was not associated with birth weight traits, whereas the IGF2 gene pattern was related to the weaning weight trait in Kradon pigs. Pigs with the CC and GC genotypes had higher weaning weights than ones with the GG genotype (p < 0.001). Thai native Kradon pigs with the CC and GC genotypes of the IGF2 gene have higher weaning weights than pigs with the GG genotype.

Carvings in stone: design research for public health investigations in the age of COVID-19

The COVID-19 pandemic presented complex challenges to public health research involving human subjects, necessitating creative thinking to maintain safe and productive qualitative data collection. In this paper, we describe how an interdisciplinary team overcame these challenges by translating in-person workshops into at-home design probes to explore connections between university students’ climate change attitudes and their health. Probes were purposively designed to facilitate and focus participation during lockdown with two key interactional components: (1) participant ⇔ probe, and (2) participant ⇔ physical environment. Thematic analysis of 18 completed probes (replete with elicitive drawing, etched stone, mapping, and writing activities) revealed a rich spectrum of climate change perceptions, eco-emotions, and key areas of impact. This case study provides insight into how pandemic-imposed restrictions presented a novel opportunity to reconceptualize how we collect qualitative data in public health research and summarizes the unique benefits of integrating such design-based approaches into the field moving forward.

Metabolic profile of transition period in ewes and its influence on passive immunity transference in lambs.

The intensification of ewe production, focusing on increasing prolificity, results in a higher incidence of metabolic disorders, especially in the transition period, which can lead to production losses with consequences for the health of the animals. The objective of this study was to evaluate the metabolic profile in transition period Lacaune ewes according to the parity and litter size born and its influence on the transference of passive immunity. Nineteen Lacaune ewes, with initial mean body weight of 76.36 ± 10.37kg (SD), kept in a semi-extensive system and receiving the same diet, were used. Ewes were classified according to parity (primiparous and multiparous) and the litter size born at parturition (1, 2, and 3). On days - 14, - 7, and - 4 of the expected date of parturition and on days 1, 4, 7, and 14days postpartum, blood sampling, the evaluation of the body condition score (BCS), and the weighing of animals were performed. Blood samples were taken from the lambs 48h after birth. Colostrum was collected up to 6h postpartum to determine the brix percentage. Serum concentrations of total plasma proteins (TPP), albumin, aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), beta-hydroxybutyrate (BHB), non-esterified fatty acids (NEFA), paraoxonase (PON1), calcium (Ca), and magnesium (Mg) were evaluated. In lambs, the blood parameters evaluated were TPP, albumin, GGT, and PON1. Lamb plasma was used to estimate colostrum efficiency, brix percentage immunity, and total plasma protein (PPT). Productive data, pre- and postpartum metabolic variables for ewes, lambs, and those related to colostrum brix were analyzed. The BCS had a reduction (p < 0.0001) in the postpartum period, which is evidenced on days 7 and 14 (2.88 and 2.73, respectively). Serum concentration of BHB presented the highest concentrations in the prepartum period for ewes that gave birth to triplets, from day - 14, and for ewes that gave birth to twins on day - 4 (p = 0.0245). Serum concentration of NEFA demonstrated the highest value on day 14 in primiparous (1.07; p = 0.0402). Calcium had the lowest concentrations on day 1 (8.85mg/dl, p < 0.0001) and magnesium on day 3 (1.79mg/dl, p < 0.0001) postpartum. The multiparous with two lambs had higher brix percentages (30.5%), and the lowest value was observed in primiparous ewes that gave birth to a single lamb (27.06%; p = 0.0395). Triplet lambs had the lowest weight (3.73kg, p = 0.0007), and the best brix percentage in plasma was observed in twins (10.29%, p = 0.0174). Regardless of the parity, the ewes that presented the greatest metabolic challenge were those that gave birth to triplets, and these lambs presented the worst immunity and the lowest live weight. The quality of colostrum was influenced by the parity, and multiparous ewes that gave birth to triplets had the lowest brix percentage in the colostrum.

Typology of Brazilian dairy farms based on vulnerability characteristics

Vulnerability has been a recurring theme in animal production research around the world, as it can lead to a series of outcomes, such as abandonment of the activity. Nevertheless, in Brazil, the fifth-largest milk producer in the world, studies assessing dairy farmers' vulnerabilities are scarce. Better understanding of dairy farm vulnerability may contribute to reducing the consequences of vulnerability. In view of these limitations, we sought to analyze the typology of dairy farms based on vulnerability characteristics. We applied on-site questionnaires to 128 dairy farmers located in Paraná State, Brazil. Structural, productive, and socioeconomic data were collected and subjected to factor analysis. Two vulnerability indicators were identified: F1, productive and economic indicator; and F2, feed self-sufficiency indicator. Hierarchical cluster analysis of factor scores revealed three groups of dairy farms: Group 1, highly vulnerable; Group 2, less vulnerable; and Group 3, non-vulnerable. Dairy farms with higher vulnerability represented most of the sample, followed by less vulnerable and non-vulnerable dairy farms. Our findings indicated that the productive and economic characteristics of farms contributed the most to explaining differences in vulnerability, followed by feed self-sufficiency characteristics. Social characteristics of farmers were not important in differentiating the analyzed sample. There was an interdependent relationship between vulnerability indicators, namely productive and economic characteristics and feed self-sufficiency.

A combined approach of base and meta learners for hybrid system

The ensemble learning method is considered a meaningful yet challenging task. To enhance the performance of binary classification and predictive analysis, this paper proposes an effective ensemble learning approach by applying multiple models to produce efficient and effective outcomes. In these experimental studies, three base learners, J48, Multilayer Perceptron (MP), and Support Vector Machine (SVM) are being utilized. Moreover, two meta-learners, Bagging and Rotation Forest are being used in this analysis. Firstly, to produce effective results and capture productive data, the base learner, the J48 decision tree is aggregated with the rotation forest. Secondly, machine learning and ensemble learning classification algorithms along with the five UCI Datasets are being applied to progress the robustness of the system. Whereas, the recommended mechanism is evaluated by implementing five performance standards concerning the accuracy, AUC (Area Under Curve), precision, recall and F-measure values. In this regard, extensive strategies and various approaches were being studied and applied to obtain improved results from the current literature; however, they were insufficient to provide successful results. We present experimental results which demonstrate the efficiency of our approach to well-known competitive approaches. This method can be applied to image identification and machine learning problems, such as binary classification.

Parâmetros clínicos e peso de bezerros Nelore cujas matrizes foram suplementadas ou não com fósforo, durante a gestação e a lactação - estudo de caso

ABSTRACT The aim of this case study was to evaluate the clinical and productive data of Nellore cows during pregnancy and lactation, supplemented or not with P. Ninety-five pregnant heifers were divided into two groups (G40 and G0). Until the date of calving animals from G40 received a mineral mixture composed of 224g of Na and 40g of P/kg, and G0 received only NaCl. After calving heifers were divided into four treatments: from G40, 28 first-calf cows continued to receive the mineral mixture containing 224g of Na and 40g of P/kg (group 40/40) and 12 started to receive only NaCl (group 40/0); from G0, 26 continued to receive only NaCl (group 0/0) and 15 started to receive a mixture containing 224g of Na and 40g of P/kg (group 0/40). In the second experimental phase, 205 multiparous cows were divided into two groups: G1 (40/40) consisting of 40 cows, receiving a mineral mixture containing 244g of Na and 40g/P per kg, during pregnancy and lactation, and group 2 (0/0) formed by 165 cows, which were supplemented only with NaCl. In both experiments, whether in pregnant or lactating breeders, there was not any sign of P deficiency or differences in calves LW at 120 and 210 days.

Does Immunocastration Affect Behaviour and Body Lesions in Heavy Pigs?

Simple SummaryCastration of piglets is routinely practiced to prevent the presence of boar taint. In Europe, around 61% of pigs are surgically castrated, 54% of which do not receive any pain-relief therapy. The increasing public awareness towards animal welfare and the ethical characteristics of animal-derived products has led to the necessity of finding more animal-friendly alternatives. Among them, immunocastration, a process of active immunisation leading to the suppression of testicular function, has shown promising results in light pig production, both in terms of animal welfare and productivity. Besides, no study has yet investigated animal welfare in immunocastrated males intended for Italian heavy pig production. This study aimed at evaluating the effect of immunocastration on the welfare of heavy pigs by monitoring behaviour and body lesions in immunocastrated and surgically castrated pigs. Salivary testosterone levels and productive traits were also evaluated. Our results confirmed that immunocastration is a suitable alternative to surgical castration with profitable productive performance, whereas the higher activity of animals and the higher presence of body lesions before the effective immunisation caused an impairment of animal welfare, which should be further investigated as a critical aspect in heavy pig production.Immunocastration has been pointed out as an alternative to surgical castration; though, most of the scientific studies were performed in light pig production. This study aimed to evaluate the effect of immunocastration on animal welfare in heavy pig production through the evaluation of behaviour and body lesions. A total of 188 commercial-hybrid pigs were randomly allocated into two treatment groups: surgical castration (SC) and immunocastration with Improvac® (IC). Data on behaviour, body lesions, and salivary testosterone levels were collected the day before each vaccination at 15, 22, 32, and 36 weeks of age. IC and SC pigs were slaughtered at 40 and 41 weeks of age, respectively; productive and carcass traits data were also collected. Considering productive performance, our results confirmed that IC pigs grew faster and presented a higher weight at slaughter. A critical period for pig welfare was observed before 32 weeks: testosterone concentration and body lesion score were significantly higher in IC pigs compared to SC pigs; active behaviours were significantly more frequent in IC at 15 weeks. Immunocastration may represent a suitable alternative to surgical castration with profitable productive performances, whereas the impairment of welfare during the period before the effective vaccination should be further investigated as a potential critical aspect in heavy pig production.

P161. Impact of supplementing lactating sows’ diets with an Aspergillus oryzae-derived prebiotic

High prolific sows' productivity is limited by the sufficient nutrient intake required to meet lactation requirements, partly compromising their maternal ability and piglet survivability. Dietary-supplementation with an Aspergillus oryzae-derived prebiotic (AO) could stabilize the gut microbiota, improve feed digestibility, and nutrient absorption, enhancing production and swine health. This study intended to assess the influence of supplementing lactation diets with AO on sow and litter performance and piglet survivability. Eighty-four Large White sows (mean 2.3parities) equally-divided into a control-group (CON) or a treated-group (AOF), balanced by farrowing order and backfat depth (42 sows × group) were used in a commercial farm. The Control group was fed a commercial diet meeting lactation requirements (NRC, 2012), whereas the AOF group was fed the same diet top-dressed with 4g of AO-derived prebiotic/head/day. Groups received their respective diets from allocation into the farrowing house until weaning (24 days), trice daily as part of a broth mix with water in a ratio of 3.5 : 1 (water : feed) increasing from 2.5 up to 6.5 kg dry-diet/sow/day after 15 days. Sow bodyweight, backfat depth, Body-condition-score at farrowing, and weaning and feed intake were recorded. Recorded piglet variables included bodyweight at birth, after cross-fostering (conducted within treatment within 48 h from birth), and at weaning, number of piglets born, born alive, stillborn, mummified, mortality during lactation, and weaned. Productive data were analysed with one-way ANOVA. Mortality was analysed using a Chi-Square test. Sows and piglets’ production traits measured, were not significantly affected (P>0,05). However, feeding AO to sows reduced piglet mortality by 29% (P<0.05) during lactation compared to CON group (11.4% vs. 16.1%, respectively). This might be explained by the transmission of balanced intestinal microflora from sows to piglets and the feed intake increment observed in sows from the AOF-group. Results suggest that AO-supplementation in lactating sows can increase piglet survivability.

Effect of Artificial Insemination on production performance of dairy cattle in Case of Sayo District West Wollega Zone, Ethiopia

The study was conducted in Sayo district with the objective of assessing challenges and opportunities of artificial insemination on dairy cattle, to document potential opportunities of artificial insemination, to identify the challenges of artificial insemination and to assess the perception of farmers on the AI technology in the study area. Three kebeles, three were taken purposively and 20 households from each kebele were randomly selected. The data were collected using semi structured questionnaires, interview and direct observation. The reproductive and productive data were analysed statistical software (SAS version 9.3.1 where as other data were analysed by simple descriptive statistics. The result of this finding shows that the overall mean of daily milk and lactation length of dairy cows in the Sayo distinct were5.83+0.82L and 240+18.43 days respectively whereas the overall mean of age at first calving, calving interval and number of services in the study area were 968.42+269.83, 449+22.09 days and 1.93+0.71times respectively.

Direct cost method applied to calculate milk production costs

The objective was to apply direct costing (DC) methodology to calculate dairy farms costs located in the semiarid region of Sergipe, Brazil. Productive and financial data were used from 30 dairy farms, in which milk market is the farms primary activity. Costs were segregated as fixed and variable, as profitability index, contribution margin (CM) and net income (NI). Profit before interest, taxes, depreciation and amortization (EBITDA) was adopted as financial index. In rural farms analyzed, CM was negative equivalent to R$ - 0.06 per liter of milk, due to the high production cost setting the sold goods cost (COGS), exceeding the net income by 4.49 percentage points, equivalent to R$ 1.31 per liter of milk, contributing to the result of R$ -0.20 (twenty cents) per liter of milk, equivalent to -16.8% of the raw milk price. The direct costing methodology has applicability to calculate costs in dairy farming, as it provides full data and information from the economic-financial point of view, well segregated.

Semi convergent matrix-based neural predictive classifier for big data analytics and prediction in cloud services

Big data analytics is a technique concerning gathering, organising and analysing huge units of records to find out patterns or useful information. Recently, many research works has been designed for large records analytics of climate data. In system in accordance with overcome certain challenge, semi convergent matrix-based neural predictive classifier (SCM-NPC) procedure is recommended that gives productive big data calculation and data partaking in cloud services. Initially, the SCM-NPC technique constructs semi convergent matrix on distributed big data for improving the search accuracy of user requested information, next, the SCM-NPC technique is used neural predictive classifier for improving the prediction rate of climate data on cloud big data. Finally, the SCM-NPC technique applies MapReduce function on neural class that provides efficient predictive analytics about climate data conditions on cloud big data. The proposed SCM-NPC system conducts test takes a shot at parameter, for example, forecast rate, computation time and classification accuracy by using Amazon EC2 cloud big datasets. The results show that SCM-NPC technique is to build the expectation level of atmosphere realities conditions on big data and furthermore decreases the calculation time when contrasted with best in class works.

Data infrastructures and the governance of their accompanying narratives

Data infrastructures are suffused with stories of their past, present, and future that govern their use. This article draws on research into ‘OneSchool’, the state schooling data infrastructure used to manage student data in Queensland, Australia. The political, social, and technical histories of OneSchool’s development are shown to govern its current use. In interviews, users from across Queensland’s education system shared stories of the ways OneSchool developed to both impede and create problematic truths about students, teachers and systems. The ways in which OneSchool became known as the single point of truth are used to illustrate how the narratives that accompany data infrastructures govern data, those who record the data, and those who are represented within the data. Identifying the governance of stories that travel with data infrastructures facilitates the development of protective and productive data behaviours for educational practitioners globally who are negotiating the discourse of digital educational governance.

Considerations on Acoustic Mapping Velocimetry (AMV) Application for in-situ Measurement of Bedform Dynamics

Non-intrusive technologies for the in-situ measurement of river morphological features are increasingly popular in the scientific and practice communities due to their efficient and productive data acquisition. While the measurement of suspended load with optical and acoustic technologies is currently an active area of research, the measurement of bedform dynamics has not experienced similar progress. We have successfully demonstrated through laboratory experiments that, by combining acoustic mapping with image velocimetry concepts, we can characterize the planar dynamics of the bedform migration. The technique, labeled Acoustic Mapping Velocimetry (AMV), is currently transferred to field conditions using multiple-beam echo-sounders (MBES) for producing acoustic maps. During this transfer, new questions emerged because, in field conditions, many of the morphologic features targeted by AMV measurements are not a priori known. Moreover, the image velocimetry processing can be approached with several alternatives, each of them characterized by strength and limitations. This paper assembles guidelines for establishing optimal parameters for the acquisition of the acoustic maps based on analytical considerations, and for selecting essential features of the processing for image velocimetry. We test these guidelines using MBES data acquired in the Mississippi River.