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[Purpose/Significance] Evidence-informed decision-making is a means to bridge the gap between research and policy and evidence synthesis has become an important tool for evidence-based decision-making in many fields. However, evidence synthesis is resource-intensive, especially when it comes to scientific knowledge on complex issues. The efficiency of evidence synthesis currently cannot meet the needs of decision makers. Crowdsourcing is seen as a potential way to improve the productivity of evidence synthesis. At present, the research and practice on the applications of crowdsourcing in evidence synthesis is still in its infancy. This study takes the application of crowdsourcing in the Cochrane Crowd citizen science project as an example to summarize the practical applications of crowdsourcing in evidence synthesis. The comprehensive analysis of the application mechanism of crowdsourcing in Cochrane Crowd project will provide certain reference and inspiration for the use of crowdsourcing in evidence synthesis, so as to improve the production efficiency of evidence synthesis and provide timely and powerful scientific information for evidence-based decision-making. [Method/Process] The application mechanism of crowdsourcing in the Cochrane Crowd citizen science project was analyzed from five dimensions: crowdsourcer, volunteers, crowdsourcing task, Cochrane Crowd platform and effectiveness evaluation, using literature research, network investigation, case analysis and other methods. Cochrane Crowd provides an easy-to-use interface for contributors to engage volunteers to participate and design , in addition to task-focused learning activities, diverse ways of accessing tasks, interactive online training modules and feedback mechanisms to improve the likelihood of volunteers' performing tasks correctly. At the same time, an agreement algorithm is provided at the platform level to aggregate the crowd classification results, which further improves the possibility of correct classification of records. In addition, the platform has used the records identified by the crowd to build a machine-learning model called as RCT classifier which can predict how likely a new citation is to be described an RCT to reduce the manual burden. [Results/Conclusions] Crowdsourcing is an effective method to improve the efficiency of evidence synthesis and shorten the production cycle. With comprehensive participant training and appropriate quality control mechanisms, it is possible to produce high quality crowdsourcing results that meet the "gold standard" of evidence synthesis. In order to motivate volunteers to participate and promote continued engagement, participants are suggested to be provided with clear goals, clear tasks, and timely feedback or rewards. Interest and activity in introducing crowdsourcing into evidence synthesis is growing rapidly, and new tools and platforms to facilitate crowdsourcing also need to be further developed as researchers from different disciplines use crowdsourcing in the evidence synthesis projects. In the future, the application of crowdsourcing in evidence synthesis in different fields and in different stages of evidence synthesis should be further studied.

The variation trends and characteristics of polar mesospheric clouds (PMCs) are important for studying the evolution of atmospheric systems and understanding various atmospheric dynamic processes. Through observation and analysis of PMCs, we can gain a comprehensive understanding of the mechanisms driving atmospheric processes, providing a scientific basis and support for addressing climate change. Ultraviolet (UV) imaging technology, adopted by the Cloud Imaging and Particle Size (CIPS) instrument on board the Aeronomy of Ice in the Mesosphere (AIM) satellite, has significantly advanced the research on PMCs. Due to the retirement of the AIM satellite, there is currently no concrete plan for next-generation instruments based on the CIPS model, resulting in a discontinuity in the observation data sequence. In this study, we propose a compact and cost-effective wide-field-of-view ultraviolet imager (WFUI) that can be integrated into various satellite platforms for future PMC observation missions. A forward model was built to evaluate the detection capability and efficiency of the WFUI. CIPS and Solar Occultation for Ice Experiment (SOFIE) data were fused to reconstruct a three-dimensional PMC scene as the input background. Based on the scattering and extinction characteristics of ice particles and atmospheric molecules, the radiative transfer was calculated using the solar radiation path through the atmosphere and PMCs. The optical system and satellite platform parameters of the WFUI were selected according to CIPS, enabling the calculation of the number of photons received by the WFUI. The actual detection signal is then simulated by photoelectric conversion, and the PMC information can be obtained by removing detector noise. Subsequently, a comparison with the input background field was conducted to compute and analyze the detection efficiency. Additionally, a sensitivity analysis of the instrument and platform parameters was conducted. Simulations were performed for both individual orbits and for the entire PMC seasons. The research results demonstrate that the WFUI performs well in PMC detection and has high detection efficiency. Statistical analysis of the detection efficiency using data from 2008 to 2012 revealed an exponential relationship between the ice water content (IWC) of PMCs and detection efficiency. During the initial and final durations of the PMC season, when the IWC was relatively low, the detection efficiency remained limited. However, as the season progressed and the IWC increased, the detection efficiency significantly improved. We note that regions at lower latitudes exhibited a lower IWC and, consequently, lower detection efficiency. In contrast, regions at higher latitudes, with a greater IWC, demonstrated better detection efficiency. Additionally, the sensitivity analysis results suggest that increasing the satellite orbit altitude and expanding the field of view (FOV) of the WFUI both contribute to improving the detection efficiency. [ABSTRACT FROM AUTHOR]

The variation trends and characteristics of polar mesospheric clouds (PMCs) are important for studying the evolution of atmospheric systems and understanding various atmospheric dynamic processes. Through observation and analysis of PMCs, we can gain a comprehensive understanding of the mechanisms driving atmospheric processes, providing a scientific basis and support for addressing climate change. Ultraviolet (UV) imaging technology, adopted by the Cloud Imaging and Particle Size (CIPS) instrument onboard the Aeronomy of Ice in the Mesosphere (AIM) satellite, has significantly advanced the research on PMCs. Due to the retirement of the AIM satellite, there is currently no concrete plan for next-generation instruments based on the CIPS model, resulting in a discontinuity in the observation data sequence. In this study, we propose a compact and cost-effective wide field-of-view ultraviolet imager (WFUI) that can be integrated into various satellite platforms for future PMCs observation missions. A forward model was built to evaluate the detection capability and efficiency of the WFUI. The fusion of CIPS and solar occultation for ice experiment (SOFIE) data was employed to reconstruct a three-dimensional PMCs scene as the input background. Based on the scattering and extinction characteristics of ice particles and atmospheric molecules, the radiative transfer was calculated using the solar radiation path through the atmosphere and PMCs. The optical system and satellite platform parameters of the WFUI were selected according to the CIPS, enabling the calculation of the number of photons received by the WFUI. The actual detection signal is then simulated by photoelectric conversion, and the PMCs information can be obtained by removing detector noise. Subsequently, a comparison with the input background field was conducted to compute and analyze the detection efficiency. Additionally, a sensitivity analysis of the instrument and platform parameters was conducted. Simulations were performed for both individual orbits and for the entire PMCs seasons. The research results demonstrate that using data from 2008 to 2012 revealed an exponential relationship between the ice water content (IWC) of PMCs and detection efficiency. During the initial and final durations of the PMCs season, when the IWC was relatively low, the detection efficiency the WFUI performs well in PMCs detection and has high detection efficiency. Statistical analysis of the detection efficiency remained limited. However, as the season progressed and the IWC increased, the detection efficiency significantly improved. We note that regions at lower latitudes exhibited a lower IWC and, consequently, lower detection efficiency. In contrast, regions at higher latitudes, with a greater IWC, demonstrated better detection efficiency. Additionally, the sensitivity analysis results suggest that increasing the satellite orbit altitude and expanding the field of view (FOV) of the WFUI both contribute to improving the detection efficiency. [ABSTRACT FROM AUTHOR]

[Purpose / significance] At the important node of IR development entering a stable period and in the transition to the next generation of IR, this paper reviews the construction process, status quo and challenges of institutional repository in China, so as to achieve the purpose of accelerating and deepening the development of IR in China., [Method/process] First, it adopts the empirical research method combining the network survey method and telephone and email survey methods, and takes domestic universities, Chinese Academy of Sciences, Chinese Academy of Agricultural Sciences and other scientific research institutes as the survey object to investigate the development status of the national IR; second, the survey results According to the comparative analysis of the university system, scientific research system and the overall situation, summarize the IR development achievements and problems. [Result/conclusion]Some achievements have been made in IR construction. The number of IR under construction and to be built is increasing, and the resource scale is increasing greatly. However, there are some problems in the development of IR, such as imperfect management policies, excessive access restrictions, unclear intellectual property rights, insufficient openness and low self storage rate.

This study presents a simulation framework for cloud and precipitation measurements via spaceborne millimeter wave radar composed of nine sub modules. To demonstrate the influence of the assumed physical parameters and optimizing the microphysical modeling of the hydrometeors, we first conducted a sensitivity analysis. The results indicated that the radar reflectivity was highly sensitive to the particle size distribution (PSD) parameter of the median volume diameter and particle density parameter, which can cause reflectivity variations of several to more than 10 dB. The variation in the prefactor of the mass-power relations that related to riming degree may result in an uncertainty of approximately 30–45 %. The particle shape and orientation also had a significant impact on the radar reflectivity. The spherical assumption may result in an average overestimation of the reflectivity by approximately 4–8 %, dependent on the particle shape and orientation modeling. Typical weather cases were simulated using optimal physical modeling accounting for the particle shapes, typical PSD parameters corresponding to the cloud precipitation types, mass-power relations for snow and graupel, and melting modeling. We present and validate the simulation results for a cold front stratiform cloud and a deep convective process with observations from W-band cloud profiling radar (CPR) on the CloudSat satellite. The simulated brightness band features, echo structure, and intensity showed good agreement with the CloudSat observations; the average relative error in the vertical profile was within 20 %. Our results quantify the uncertainty in the millimeter wave radar echo simulation that may be caused by the physical model parameters and provide a scientific basis for optimal forward modeling. They also provide suggestions for prior physical parameter constraints for the retrieval of the microphysical properties of clouds and precipitation. [ABSTRACT FROM AUTHOR]

ABSTRACT Calibration error, due to ground-based radar ( GR) hardware parameters, introduces biases to GR reflectivity factor data. As a result of this, GR observations from the China new-generation weather radar network have sometimes been found to be inconsistent, even when observing precipitation in the same area. In this paper a method, called the Available Best Comparable Data set ( ABCD) method, is proposed. The method evaluates the radar reflectivity bias in GR datasets by using TRMM Standard Product ( TSP) 2A25 data from Tropical Rainfall Measuring Mission ( TRMM) satellite-borne precipitation radar ( PR). The analysis is supported by simultaneous measurement data from two precipitation events that occurred in the middle and lower reaches of the Yangtze River between June and July 2010; data were collected during these events by three radars in Nanjing, Changzhou and Nantong (Jiangsu province, China). The ABCD method could correct deviations in the GR reflectivity factors; the GR-corrected values were consistent with those observed by automatic weather stations. The method can be applied to improve the quality of precipitation data products from the GR network and their quantitative application for the southern region in China. [ABSTRACT FROM AUTHOR]

The measurement error of differential reflectivity (ZDR), especially systematic ZDR bias, is a fundamental issue for the application of polarimetric radar data. Several calibration methods have been proposed and applied to correct ZDR bias. However, recent studies have shown that ZDR bias is time-dependent and can be significantly different on two adjacent days. This means that the frequent monitoring of ZDR bias is necessary, which is difficult to achieve with existing methods. As radar sensitivity has gradually been enhanced, large amounts of online solar echoes have begun to be observed in volume-scan data. Online solar echoes have a high frequency, and a known theoretical value of ZDR (0 dB) could thus allow the continuous monitoring of ZDR bias. However, online solar echoes are also affected by low signal-to-noise ratio and precipitation attenuation for short-wavelength radar. In order to understand the variation of ZDR bias in a C-band polarimetric radar at the Nanjing University of Information Science and Technology (NUIST-CDP), we analyzed the characteristics of online solar echoes from this radar, including the daily frequency of occurrence, the distribution along the radial direction, precipitation attenuation, and fluctuation caused by noise. Then, an automatic method based on online solar echoes was proposed to monitor the daily ZDR bias of the NUIST-CDP. In the proposed method, a one-way differential attenuation correction for solar echoes and a maximum likelihood estimation using a Gaussian model were designed to estimate the optimal daily ZDR bias. The analysis of three months of data from the NUIST-CDP showed the following: (1) Online solar echoes occurred very frequently regardless of precipitation. Under the volume-scan mode, the average number of occurrences was 15 per day and the minimum number was seven. This high frequency could meet the requirements of continuous monitoring of the daily ZDR bias under precipitation and no-rain conditions. (2) The result from the proposed online solar method was significantly linearly correlated with that from the vertical pointing method (observation at an elevation angle of 90°), with a correlation coefficient of 0.61, suggesting that the proposed method is feasible. (3) The day-to-day variation in the ZDR bias was relatively large, and 32% of such variations exceeded 0.2 dB, meaning that a one-time calibration was not representative in time. Accordingly, continuous calibration will be necessary. (4) The ZDR bias was found to be largely influenced by the ambient temperature, with a large negative correlation between the ZDR bias and the temperature. [ABSTRACT FROM AUTHOR]