Availability Of Raw Data Research Articles

Self-Management Systems for Patients and Clinicians in Parkinson's Disease Care: A Scoping Review.

Digital self-management tools including mobile apps and wearables can enhance personalized care in Parkinson's disease, and incorporating patient and clinician feedback into their evaluation can empower users and nurture patient-clinician relationships, necessitating a review to assess the state of the art and refine their use. This review aimed to summarize the state of the art of self-management systems used in Parkinson's disease management, detailing the application of self-management techniques and the integration of clinicians. It also aimed to provide a concise synthesis on the acceptance and usability of these systems from the clinicians' standpoint, reflecting both patient engagement and clinician experience. The review was organized following the PRISMA extension for Scoping Reviews and PICOS frameworks. Studies were retrieved from PubMed, CINAHL, Scopus, ACM Digital Library, and IEEE Xplore. Data was collected using a predefined form and then analyzed descriptively. Of the 15,231 studies retrieved, 33 were included. Five technology types were identified, with systems combining technologies being the most evaluated. Common self-management strategies included educational material and symptom journals. Only 11 studies gathered data from clinicians or reported evidence of clinician integration; out of those, six studies point out the importance of raw data availability, data visualization, and integrated data summaries. While self-management systems for Parkinson's disease are well-received by patients, the studies underscore the urgency for more research into their usability for clinicians and integration into daily medical workflows to enhance overall care quality.

On the application of process management and process mining to Industry 4.0

AbstractThe continuous evolution of digital technologies applied to the more traditional world of industrial automation led to Industry 4.0, which envisions production processes subject to continuous monitoring and able to dynamically respond to changes that can affect the production at any stage (resilient factory). The concept of agility, which is a core element of Industry 4.0, is defined as the ability to quickly react to breaks and quickly adapt to changes. Accurate approaches should be implemented aiming at managing, optimizing and improving production processes. In this vision paper, we show how process management (BPM) can benefit from the availability of raw data from the industrial internet of things to obtain agile processes by using a top-down approach based on automated synthesis and a bottom-up approach based on mining.

Forecasting regional water demand using multi-fidelity data and harris hawks optimization of generalized regression neural network models – A case study of Heilongjiang Province, China

Accurate forecasting of water demand plays a crucial role in decision makers to effectively allocate regional water resources and promotes the rational development and utilization of water resources. To address the challenges posed by limited raw data availability and the difficulty in selecting parameters for water demand forecasting models, this study proposes a coupled model called MF-HHO-GRNN (Multi-Fidelity Data, Hybrid Harris Hawks Optimization, and Generalized Regression Neural Network). Initially, multi-fidelity data was constructed employing water demand factor data for Heilongjiang Province, China (1999–2020) as high-fidelity data, alongside data from Liaoning and Jilin Provinces of China as low-fidelity data. Adaptive boosting algorithms, data concatenation, and data dimensionality reduction techniques were employed to construct multi-fidelity data, thereby improving the availability of the original data. Subsequently, Harris hawk optimization algorithms were implemented to optimize the smoothing factor of the GRNN to resolve the challenge of selecting suitable parameters for water demand forecasting models. Finally, the MF-HHO-GRNN model was compared with other prediction models to analyze the prediction accuracy. The optimized MF-HHO-GRNN model meeting accuracy requirements was utilized to forecast agricultural irrigation, industrial, domestic, ecological water demand, and total water demand in Heilongjiang Province for the next 5 years. Results demonstrated that the proposed MF-HHO-GRNN model attained predictive accuracy of 98 %, 98 %, and 97 % for total water demand under three distinct time horizons (1 year, 3 years, and 5 years), proving excellent predictive capabilities. Furthermore, a comprehensive comparison was made between the predicted total water demand and user-specific water demands for the next 5 years in Heilongjiang Province and the regional development plans. The results showed that the predictions of the coupled model aligned with the future development trends of Heilongjiang Province, validating the practicality of the MF-HHO-GRNN model. This study provides decision-makers with an effective method for forecasting water demand, contributing to the future realization of more intelligent and accurate water resource management and decision-making.

Assessment of Usefulness of Randomized Control Trials in Child Health Research Published in 2007 and 2017

ObjectiveTo examine how clinical usefulness in pediatric research with randomized controlled trials (RCTs) has changed over a 10-year period via a research usefulness tool composed of unique clinical usefulness criteria. Study designWe leveraged a pre-existing sample of child health RCTs published in 2007, used by our team in a previous study. Using the same methods, a research librarian executed a literature search in the Cochrane Central Register of Controlled Trials for the 2017 cohort. We included the first 300 eligible citations from the randomly ordered list for each year, creating two cohorts of 300 publications each, one in 2007 and one in 2017. Each publication was analyzed and data regarding primary and secondary outcomes, as well as 11 unique criteria of clinical usefulness, were extracted. Each publication was then graded using a tool created by our research team. After quality review, statistical analysis was then performed. Results600 pediatric RCT publications were included in this review. The mean score increased from 6.07 in 2007 to 9.20 in 2017 (P<0.001). Usefulness factors that saw the largest increase in reporting were context placement, funding statements, and conflict of interest statements, while patient centeredness, value for money, and raw data availability remained infrequently reported. ConclusionOur results demonstrate that clinical usefulness of pediatric research improved over this 10-year period, but there are still areas that need a great deal of improvement in order to maximize clinical usefulness and reduce research waste.

Research on predicting the diffusion of toxic heavy gas sulfur dioxide by applying a hybrid deep learning model to real case data

Toxic heavy gas sulfur dioxide (SO2) is a specific life and environmental hazard. Predicting the diffusion of SO2 has become a research focus in fields such as environmental and safety studies. However, traditional methods, such as kinetic models, cannot balance precision and time. Thus, they do not meet the needs of emergency decision-making. Deep learning (DL) models are emerging as a highly regarded solution, providing faster and more accurate predictions of gas concentrations. To this end, this study proposes an innovative hybrid DL model, the parallel-connected convolutional neural network-gated recurrent unit (PC CNN-GRU). This model utilizes two CNNs connected in parallel to process gas release and meteorological datasets, enabling the automatic extraction of high-dimensional data features and handling of long-term temporal dependencies through the GRU. The proposed model demonstrates good performance (RMSE, MAE, and R2 of 20.1658, 10.9158, and 0.9288, respectively) with real data from the Project Prairie Grass (PPG) case. Meanwhile, to address the issue of limited availability of raw data, in this study, time series generative adversarial network (TimeGAN) are introduced for SO2 diffusion studies for the first time, and their effectiveness is verified. To enhance the practicality of the research, the contribution of drivers to SO2 diffusion is quantified through the utilization of the permutation importance (PIMP) and Sobol’ method. Additionally, the maximum safe distance downwind under various conditions is visualized based on the SO2 toxicity endpoint concentration. The results of the analyses can provide a scientific basis for relevant decisions and measures.

Management of the effectiveness of investment planning using economic and mathematical modeling

Introduction. When solving optimal tasks at the stage of technical and economic planning, it is necessary to establish an optimality criterion, which is mathematically written as an objective function. Minimization of the duration of the project works during its implementation within the time limit set by the investor is such a criterion in the formulation of our task. By limitations of the model, we understand such conditions, which are expressed in the limitation of resources both quantitatively and qualitatively: hence the problem of their saving, the best distribution, and effective use arises.Goal consists in the theoretical and methodological substantiation of the concept of managing the effectiveness of investment planning with the use of economic and mathematical modelingResearch methods. In the research process, general scientific and special methods of cognition were used: analysis and synthesis, theoretical generalization and formal logic, system approach, classification, statistical analysis, functional-value analysis, generalization, observation, mathematical statistics, simulation and network modeling, specification, technical and economic calculationsResults. It was found that with the help of the proposed technology «Flow» it is possible to determine: the calendar schedule of works for the optimal version of the project implementation; the calendar dates of the start and end of critical works, on which the duration of the entire project depends; actual project implementation costs. The value of the objective function shows what additional costs are necessary for the implementation of the investment project in a given period. It has been proven that the «Flow» technology allows you to determine the value of the model and, in our opinion, demonstrates the improvement of the existing solution to the tasks of project implementation within the given time and ensures the compatibility and unity of information support, and therefore allows you to receive more reliable data. The main advantages of the proposed method were evaluated: availability of raw data; a calculation algorithm that is understandable to any project participant; clarity of results; connection in a single model of the economic interests of the main participants of the project.Perspectives. Further research should be aimed at carrying out a continuous process of qualitative and quantitative assessment of the effectiveness of the project management information system and the need to create a special organizational unit - a strategic committee, which will allow the creation of mechanisms for effective management of investment projects of the territorial community, to achieve its strategic goals, and will also contribute to the implementation of strategies due to the consistent actions of the community investment development management staff.

A meta-analysis of the effect of Eimeria spp. and/or Clostridium perfringens infection on the microbiota of broiler chickens

Coccidiosis in chickens is caused by Eimeria spp. The infection provides a growth advantage to Clostridium perfringens (CP), frequently leading to necrotic enteritis. One approach to alleviate the negative impacts of the diseases is to improve the bacterial composition in chickens, and many experiments investigating chicken enteric health in recent years include the characterization of the bacterial microbiota. This meta-analysis synthesized the data of studies investigating the intestinal microbiota after infection with coccidia and/or CP to provide a basis for future research. Inclusion criteria were that experiments contained a group infected with one or both pathogens and an uninfected control group, the use of 16SrRNA Illumina sequencing and the availability of raw data. A total of 17 studies could be included. Meta-analyses of 3 different data sets were performed: 1 on data of 9 experiments on chickens infected with coccidia only; the second on data of 4 studies on chickens infected with CP only; the third on raw data of 8 experiments with chickens infected with coccidia and CP. The meta-analysis of relative abundance and alpha diversity of the data sets was performed in R using the SIAMCAT and metafor packages. The number of families of interest identified by the analyses of experiments with infection with coccidia only, CP only and the combined infection were 23, 2, and 29, respectively. There was an overlap of 13 families identified by analyses of experiments with infection with coccidia only and of experiments with the combined infections. Machine learning was not able to find a model to predict changes of the microbiota in either 1 of the 3 analyses. Meta-analyses of functional profiles showed a more uniform reaction to the infections with the relative abundance of many pathways significantly altered. Alpha diversity was not affected by infection with either pathogen or the combination. In conclusion, the heterogeneity of these microbiota studies makes recognizing common trends difficult, although it seems that coccidia infection affects the microbiota more than an infection with CP. Future studies should focus on the bacterial functions that are changed due to these infections using metagenome techniques.

Accurate intensity integration in the twinned γ-form of o-nitroaniline

o-Nitroaniline, C6H6N2O3, is known to be polymorphic. The α-form is probably amorphous, while the β- and γ-forms are crystalline. Difficulties with the unit-cell determination of the γ-form were reported as a consequence of twinning. In this paper, newly recorded diffraction data of the γ-form of o-nitroaniline are described that were processed taking into account the two twin lattices. Data were partly deconvoluted and much better agreement was obtained in terms of R 1 values and C—C bond precision. The availability of raw data and proper reprocessing using twin lattices is by far superior to efforts to de-twin processed structure factors.

Development and Assessment of a Movement Disorder Simulator Based on Inertial Data.

The detection analysis of neurodegenerative diseases by means of low-cost sensors and suitable classification algorithms is a key part of the widely spreading telemedicine techniques. The choice of suitable sensors and the tuning of analysis algorithms require a large amount of data, which could be derived from a large experimental measurement campaign involving voluntary patients. This process requires a prior approval phase for the processing and the use of sensitive data in order to respect patient privacy and ethical aspects. To obtain clearance from an ethics committee, it is necessary to submit a protocol describing tests and wait for approval, which can take place after a typical period of six months. An alternative consists of structuring, implementing, validating, and adopting a software simulator at most for the initial stage of the research. To this end, the paper proposes the development, validation, and usage of a software simulator able to generate movement disorders-related data, for both healthy and pathological conditions, based on raw inertial measurement data, and give tri-axial acceleration and angular velocity as output. To present a possible operating scenario of the developed software, this work focuses on a specific case study, i.e., the Parkinson’s disease-related tremor, one of the main disorders of the homonym pathology. The full framework is reported, from raw data availability to pathological data generation, along with a common machine learning method implementation to evaluate data suitability to be distinguished and classified. Due to the development of a flexible and easy-to-use simulator, the paper also analyses and discusses the data quality, described with typical measurement features, as a metric to allow accurate classification under a low-performance sensing device. The simulator’s validation results show a correlation coefficient greater than 0.94 for angular velocity and 0.93 regarding acceleration data. Classification performance on Parkinson’s disease tremor was greater than 98% in the best test conditions.

A review of reproducible and transparent research practices in urology publications from 2014 to2018

BackgroundReproducibility is essential for the integrity of scientific research. Reproducibility is measured by the ability of different investigators to replicate the outcomes of an original publication using the same materials and procedures. Unfortunately, reproducibility is not currently a standard being met by most scientific research.MethodsFor this review, we sampled 300 publications in the field of urology to assess for 14 indicators of reproducibility including material availability, raw data availability, analysis script availability, pre-registration information, links to protocols, and if the publication was available free to the public. Publications were also assessed for statements about conflicts of interest and funding sources.ResultsOf the 300 sample publications, 171 contained empirical data available for analysis of reproducibility. Of the 171 articles with empirical data to analyze, 0.58% provided links to protocols, 4.09% provided access to raw data, 3.09% provided access to materials, and 4.68% were pre-registered. None of the studies provided analysis scripts. Our review is cross-sectional in nature, including only PubMed indexed journals-published in English-and within a finite time period. Thus, our results should be interpreted in light of these considerations.ConclusionCurrent urology research does not consistently provide the components needed to reproduce original studies. Collaborative efforts from investigators and journal editors are needed to improve research quality while minimizing waste and patient risk.

P-203 Searching for the optimal timing of intracytoplasmic sperm injection post oocyte retrieval: a systematic review and meta-analysis

Abstract Study question What is the optimal time post oocyte retrieval (POR) to undertake intracytoplasmic sperm injection (ICSI) in order to maximise subsequent pregnancy and live birth outcomes? Summary answer Delaying ICSI for 5 hours or more POR may compromise subsequent clinical pregnancy and live birth rates. What is known already It is often challenging to perform ICSI at a set timing POR, especially in ART clinics with a heavy workload. However, the degree of flexibility in ICSI timing remains unclear in the literature. Some articles have reported detrimental impacts by delaying ICSI on the subsequent treatment outcomes (Pujol et al 2018), while others reported no evidence of adverse effects caused by different ICSI timings (Vandenberghe et al 2021). Therefore, we aimed to explore a “safety time window” for ICSI procedures to enable flexibility in workflow arrangement without compromising pregnancy outcomes. Study design, size, duration A systematic review and meta-analysis were performed according to PRISMA guidelines, with protocol registered with PROSPERO (ID: CRD42022296838). The studies were searched for in PubMed, MEDLINE, EMBASE and the Cochrane Library on 16 December 2021, using (“subfertile” OR “sub-fertile” OR “infert*” OR “fertility treatment” OR “women” OR “female” OR “couple”) AND (“ICSI” OR “intracytoplasmic sperm injection” OR “intra-cytoplasmic sperm injection”) AND (“timing” OR “time interval”) AND (“pregnancy” OR “fertili*” OR “birth”) NOT (Review [Publication Type]). Participants/materials, setting, methods Two independent reviewers systematically evaluated each study on patient, intervention, comparison, and outcome (PICO). The Newcastle-Ottawa scale was used for risk of bias assessment in non-randomised trials and observational studies. Further screening was performed separately by the two independent reviewers, with differences mediated by the third reviewer. Data extraction was performed according to Cochrane methods. Clinical pregnancy or live birth data were synthesised from eligible studies depending on availability of raw data in corresponding subgroups. Main results and the role of chance A total of 605 records were hit in the search. After initial exclusion according to PICO, 30 articles were included for further screening for eligibility. For meta-analysis, 4 retrospective cohort studies were eventually included for clinical pregnancy rate assessment while 2 for live birth rate analysis. Due to different groupings in ICSI timing amongst these studies, a series of comparisons on ICSI timings with a cut-off at an hourly increment were set up to pool data from the 4 included studies where applicable. Clinical pregnancy rates were comparable when ICSI was performed (a) 2+ hours (n = 6448, Relative Risk or RR = 1.031, 95% CI 0.964-1.102) in reference to &amp;lt; 2 hours POR (n = 2336), (b) 3+ hours (n = 5101, RR = 1.005, 95% CI 0.945-1.069) in reference to &amp;lt; 3 hours POR (n = 3465), and (c) 4+ hours (n = 3192, RR = 1.017, 95% CI 0.956-1.082) in reference to &amp;lt; 4 hours POR (n = 5374). However, clinical pregnancy rates were significantly compromised when ICSI was performed at 5+ hours (n = 2335, RR = 1.103, 95% CI 1.036-1.175) in reference to &amp;lt; 5 hours POR (n = 7169). Similarly, live birth rate was also significantly reduced when ICSI was performed 5+ hours (n = 2335, RR = 1.155, 95% CI 1.070-1.246) in reference to &amp;lt; 5 hours POR (n = 7169). Limitations, reasons for caution All studies included for meta-analysis were retrospective with heterogeneity. There were various groupings amongst studies due to the wide range of ICSI timings POR, which limits data pooling and subsequent comparisons. A future well designed randomised controlled trial is warranted for further analysis based on better quality evidence. Wider implications of the findings Our results indicate 5 hours POR should be considered the maximum time limit to perform ICSI. This finding, supported by statistics with improved power through meta-analysis, offers valuable guidance to busy clinics. This information could both improve pregnancy outcomes and allow better workflow arrangements without compromising them. Trial registration number not applicable

Data Availability of Open T-Cell Receptor Repertoire Data, a Systematic Assessment

Modern data-driven research has the power to promote novel biomedical discoveries through secondary analyses of raw data. Therefore, it is important to ensure data-driven research with great reproducibility and robustness for promoting a precise and accurate secondary analysis of the immunogenomics data. In scientific research, rigorous conduct in designing and conducting experiments is needed, specifically in scientific writing and reporting results. It is also crucial to make raw data available, discoverable, and well described or annotated in order to promote future re-analysis of the data. In order to assess the data availability of published T cell receptor (TCR) repertoire data, we examined 11,918 TCR-Seq samples corresponding to 134 TCR-Seq studies ranging from 2006 to 2022. Among the 134 studies, only 38.1% had publicly available raw TCR-Seq data shared in public repositories. We also found a statistically significant association between the presence of data availability statements and the increase in raw data availability (p = 0.014). Yet, 46.8% of studies with data availability statements failed to share the raw TCR-Seq data. There is a pressing need for the biomedical community to increase awareness of the importance of promoting raw data availability in scientific research and take immediate action to improve its raw data availability enabling cost-effective secondary analysis of existing immunogenomics data by the larger scientific community.

ARGEOS: A New Bioinformatic Tool for Detailed Systematics Search in GEO and ArrayExpress.

Simple SummaryA systematic search for datasets of transcriptome data is a hefty task. Therefore, we developed the ARGEOS web tool, which simplifies the search and selection of datasets from various public databases. In addition, the service carries out an advanced analysis of a dataset, including collecting detailed protocols, information on the number of datasets, and providing additional reference information. An example of a cell polarization study exemplifies the effectiveness of the tool.Conduct a reanalysis of transcriptome data for studying intracellular signaling or solving other experimental problems is becoming increasingly popular. Gene expression data are archived as microarray or RNA-seq datasets mainly in two public databases: Gene Expression Omnibus (GEO) and ArrayExpress (AE). These databases were not initially intended to systematically search datasets, making it challenging to conduct a secondary study. Therefore, we have created the ARGEOS service, which has the following advantages that facilitate the search: (1) Users can simultaneously send several requests that are supposed to be used for systematic searches, and it is possible to correct the requests; (2) advanced analysis of information about the dataset is available. The service collects detailed protocols, information on the number of datasets, analyzes the availability of raw data, and provides other reference information. All this contributes to both rapid data analysis with the search for the most relevant datasets and to the systematic search with detailed analysis of the information of the datasets. The efficiency of the service is shown in the example of analyzing transcriptome data of activated (polarized) cells. We have performed a systematic search of studies of cell polarization (when cells are exposed to different immune stimuli). The web interface for ARGEOS is user-friendly and straightforward. It can be used by a person who is not familiar with database searching.

Validation of a novel handheld 3D ultrasound system for imaging scoliosis - phantom study.

Use of 3D ultrasound (US) scanners to detect and monitor scoliosis have been validated. The Cobb angle, axial vertebral rotation, spinal flexibility, curvatures in the sagittal profile and the Cobb angle on the plane of maximum curvature (PMC) can be measured from coronal, transverse and sagittal planes of ultrasound images. However, traditional 3D ultrasound scanners are relatively bulky and expensive. 2D US handheld and low-cost scanners are widely available. To adapt the 2D scanners for scoliosis applications, a position and orientation system is integrated with the scanner. The objective of this study was to validate a newly developed 3D handheld US system to image the spine. The wireless handheld US scanner (C3-HD, Clarius, Canada) was selected because of its high resolution and availability of raw data. A wireless tracking system based on electromagnetic (G4 system, Polhemus, USA) was integrated with the Clarius ultrasound. During scanning, the ultrasound information was synchronized with the scanner's position and orientation by using custom developed software. Both information were streamed wirelessly to a laptop. Custom software reconstructed and displayed the 3D spinal image in real-time. A single 3D printed vertebra, two full plastic spine phantoms from T1-T12 vertebrae and a non-scoliotic volunteer were scanned. The 3D reconstruction process of a spine image was less than 3 seconds. The dimensional and the angle errors were 1 mm and 3°, respectively. This study demonstrated that a low-cost ($11,000 USD) handheld 3D ultrasound system was developed and validated. Clinical trials on subjects attending will be the next step.

Revisiting seasonal dynamics of total nitrogen in reservoirs with a systematic framework for mining data from existing publications

Investigation of seasonal variations of water quality parameters is essential for understanding the mechanisms of structural changes in aquatic ecosystems and their pollution control. Despite the ongoing rise in scientific production on spatiotemporal distribution characteristics of water quality parameters, such as total nitrogen (TN) in reservoirs, attempts to use published data and incorporate them into a large-scale comparison and trends analyses are lacking. Here, we propose a framework of Data extraction, Data grouping and Statistical analysis (DDS) and illustrate application of this DDS framework with the example of TN in reservoirs. Among 1722 publications related to TN in reservoirs, 58 TN time-series data from 19 reservoirs met the analysis requirements and were extracted using the DDS framework. We performed statistical analysis on these time-series data using Dynamic Time Warping (DTW) combined with agglomerative hierarchical clustering as well as Generalized Additive Models for Location, Scale, and Shape (GAMLSS). Three patterns of seasonal TN dynamics were identified. In Pattern V-Sum, TN concentrations change in a "V" shape, dropping to its lowest value in summer; in Pattern P-Sum, TN increases in late summer/early fall before decreasing again; and in Pattern P-Spr, TN peaks in spring. Identified patterns were driven by phytoplankton growth and precipitation (Pattern V-Sum), nitrate wet deposition and agricultural runoff (Pattern P-Sum), and anthropogenic discharges (Pattern P-Spr). Application of the DDS framework has identified a key bottleneck in assessing the dynamics of TN — low data accessibility and availability. Providing an easily accessible data sharing platform and increasing the accessibility and availability of raw data for research will facilitate improvements and expand the applicability of the DDS framework. Identification of additional spatiotemporal patterns of water quality parameters can provide new insights for more comprehensive pollution control and management of aquatic ecosystems.

Water and hydropower reservoirs: High temporal resolution time series derived from MODIS data to characterize seasonality and variability

Remote sensing time series offer the possibility to monitor surface water at dense temporal intervals. Open data archives as well as developments in cloud computing are the main drivers towards and increased availability of raw data allowing for the extraction of detailed information on water bodies such as natural lakes and artificial reservoirs. At the same time, changes in precipitation patterns, increasing frequency and intensity of droughts, intensification of human water use, and regulatory upstream measurements affect water resources around the world today. With regard to water availability and supply-demand balance, artificial water reservoirs have become most important elements e.g. for hydropower, irrigated agriculture, flood control, as well as for domestic and industrial water use. Nevertheless, publicly accessible information on reservoirs is still not harmonized and available at global scale. Therefore, it is more essential than ever to acquire detailed knowledge about spatio-temporal variability of water resources - especially reservoirs - and the drivers of their dynamics. In this study, we analyze daily water extent time series of the 1267 largest reservoirs worldwide based on the existing DLR-DFD Global WaterPack product derived from MODIS data (Klein et al., 2017). The study aims to present an experimental way of spatio-temporal variability analysis by implementing the TIMESAT software which is usually used for vegetation analyses. In our experimental approach we derive information on the timing when the open surface water areas of reservoirs increase and decrease by identifying start date, end date and duration of such reservoir cycles as well as timing of maximum surface water extent (hydro-metrics). For four selected reservoirs, these hydro-metrics derived from surface water extent are compared with hydro-metrics derived from in-situ water level measurements or altimetry datasets and are discussed in more detail. Based on the presented examples we demonstrate the potential of high temporal resolution surface water extent data and spatio-temporal variability analyses with TIMESAT for future applications supporting the understanding of reservoir variability as a result of water management and hydroclimatic variability.

Reproducible research practices and transparency in reproductive endocrinology and infertility articles

To analyse the published literature in reproductive endocrinology and infertility (REI) to examine the transparency and the use of reproducible research practices of the scientific literature and to identify possible avenues for improvement. Meta-epidemiologic study. We examined the first 20 consecutive full-text original articles presenting primary data from five REI-specific journals for 2013 and for 2018, and eligible REI articles published in 2013-2018 in five high-impact general journals. Eligible articles were required to be full-text original articles, presenting primary data. Not applicable. Not applicable. Not applicable. Each article was assessed for study type, trial registration, protocol and raw data availability, funding and conflict of interest declarations, inclusion in subsequent systematic reviews and/or meta-analyses, sample size, and whether the work claimed to be novel or replication. Sample sizes and citation counts also were obtained. A total of 222 articles were deemed eligible; 98 from REI journals published in 2013, 90 from REI journals published in 2018, and 34 from high-impact journals. There were 37 studies registered, 15 contained a protocol, and two stated actively that they were willing to share data. Most studies provided a statement about funding and conflicts of interest. Two articles explicitly described themselves as replications. All randomized controlled trial published in REI journals were registered prospectively; many meta-analyses were not registered. High-impact journal articles had a greater median sample size and more citations and were more likely to be registered, to have a protocol, and to claim novelty explicitly when compared with REI 2013 and 2018 articles. Research in REI can be improved in prospective registration, routine availability of protocols, wider sharing of raw data whenever feasible, and more emphasis on replication.

Survey of the fruits and endocarps of Icacinaceae (Lamiids, Icacinales)

Icacinaceae Miers are a well-described family. However, the family lacks a comprehensive guide to endocarp morphology, which would be an important tool for interpreting the family’s extensive fossil record of endocarp remains. In this survey, we describe fruits and endocarps of 88 species of Icacinaceae s. str., four of Icacinaceae s. lat. (now Metteniusaceae) and one of Oncothecaceae. We investigated the value of the endocarp in species recognition. In addition, we generated an Xper3 database with an associated e-key to increase the availability of raw data and the understanding of the characters used. This study documents great diversity in epicarp hairs, mesocarp thickness, endocarp ornamentation, tubercle shape (if present), endocarp structure and thickness, and the locule lining. Some morphological features appear diagnostic for individual genera. In particular, the genus Iodes Blume, which is very common in the fossil record, is the only clade with the three following characters: papillae on the inner locule lining, ridged ornamentation on the endocarp surface, and a vascular bundle embedded within the endocarp wall. Finally, we discuss issues related to the preservation of fruit material in herbarium collections.

DEVELOPMENT OF SCIENTIFIC BASIS FOR WATER MANAGEMENT IN AGROLANDSCAPES

Quantitative and qualitative depletion of water resources in Ukraine because of the increase in climate aridity make the problem of integrated surface and ground water management more urgent. The integrated management is based on the water balance of the territories, which automatically takes into account natural and artificial features of water use.&#x0D; The paper presents the results of a long term research aimed at resolving the problems of scientific support of integrated water resources management in Ukraine in the context of climate change.&#x0D; The plans for integrated water management are developed according to the administrative principle allowing optimization of the structure of water consumption and the assessment of available water resources capacity to meet water demand of the required quality without environmental risk for aquatic ecosystems and population.&#x0D; The surface and ground water in Ukraine have spatially homogeneous hydrochemical spectrum, which characterizes the concentration anions and cations in water and the nature of their change at different water exchange rates.&#x0D; Studies of hydrochemical spectrum have shown that its basic parameters are much higher for groundwater than for surface water.&#x0D; The results of the study on the spatial changes in the chemical composition of surface waters in the Dnipro river basin testify to the distinct homogeneity of their hydrochemical spectrum, which gives the reason to limit in practice observations of changes in the chemical composition of water to local points.&#x0D; As the experimental data are accumulated and the geospatial analysis of hydrochemical spectrum is made, the number of such points can be reduced ten times without significant negative impact on the reliability of the final results.&#x0D; It is possible to draw up a water balance with the use of correlation functions, the observance of which will guarantee the rational water resources use, based on their actual available volume and restrictions on the total water inflow, filtration losses, surface runoff (discharges).&#x0D; The water balance in the graphical representation is described by a linear function, which cuts the average of the sum of expendable elements on the ordinate axis over the entire observation period. The tangent of the slope of the graph represents the coefficient of water supply.&#x0D; The scope of this methodological approach's application is limited only by the availability of raw data. There are sufficient data from precipitation observations, water abstraction from all sources in volumes comparable to precipitation and more. It is also desirable to have observations of groundwater level fluctuations and air moisture deficit.&#x0D; The experimental research confirmed a high positive impact on the water resources formation made by forests, shelterbelts and meadows. In the humid zone it is possible to separate natural landscapes of wetlands and floodplains (60% of the territory), in the southern part - transitional landscapes that replace artificial agrolandscapes (40% of the territory). In arid climate conditions, the types of landscapes differ in the area of irrigated lands and the density of shelterbelts.&#x0D; Agrolandscapes are complex natural-technical systems. In most cases, when investigating their impact on the processes of formation and use of water, it is possible to operate a finite number of interconnected parts (subsystems). The purpose of such separation on subsystems is to obtain typical areas for which spatial structure of agricultural landscapes can be experimentally evaluated and the nature of its relationship with the factors of surface water resources formation can be determined.&#x0D; According to the assessment of water balance of rural territories, their water resources potential is equal to the sum of atmospheric precipitation and the volumes of water entering the balance space due to its withdraw for economic needs from surface and ground water bodies. When drawing up water balance for a hydrological year, water resources do not include precipitation, which is almost completely converted to river flow. The available volume of water for use should include the volume of water, the removal of which from natural or artificial circulation will not lead to negative environmental impacts or increase the cost of economic activity and restoration of fresh water bodies. The volume of water available for use is also limited by its qualitative indicators, which in turn are determined by its chemical composition (spectrum).

Popler: An r package for extraction and synthesis of population time series from the long‐term ecological research (LTER) network

Abstract Population dynamics play a central role in the historical and current development of fundamental and applied ecological science. The nascent culture of open data promises to increase the value of population dynamics studies to the field of ecology. However, synthesis of population data is constrained by the difficulty in identifying relevant datasets, by the heterogeneity of available data and by access to raw (as opposed to aggregated or derived) observations. To obviate these issues, we built a relational database, popler, and its R client, the library "popler". popler accommodates the vast majority of population data under a common structure, and without the need for aggregating raw observations. The "popler" R library is designed for users unfamiliar with the structure of the database and with the SQL language. This R library allows users to identify, download, explore and cite datasets salient to their needs. We implemented popler as a PostgreSQL instance, where we stored population data originated by the United States Long Term Ecological Research (LTER) Network. Our focus on the US LTER data aims to leverage the potential of this vast open data resource. The database currently contains 305 datasets from 25 LTER sites. popler is designed to accommodate automatic updates of existing datasets, and to accommodate additional datasets from LTER as well as non‐LTER studies. The combination of the online database and the R library "popler" is a resource for data synthesis efforts in population ecology. The common structure of popler simplifies comparative analyses, and the availability of raw data confers flexibility in data analysis. The "popler" R library maximizes these opportunities by providing a user‐friendly interface to the online database.