For detail：http://vis.pku.edu.cn/mddv/ For detail of Assemble Factory: http://vis.pku.edu.cn/mddv/?page_id=134， For detail of Scatterplots in Parallel Cooridnates User Mannul：http://vis.pku.edu.cn/mddv/?page_id=195

A list of MEME Suite tools that are currently included in memes.

This table shows the relationship between the 41 arbitrary "bands" in the multidimensional inundation extent dataset and increasing Atchafalaya River stages at Butte La Rose. The analysis used 28 Landsat images (1984-2008) classified into categories of wet (vs dry). Each pixel value was evaluated using a probit analysis (in R) to determine the probability that each pixel would be classified as wet or dry at a series of 1/2 foot increments from 1.6 to 21.6 feet based on the Butte La Rose gaging station for a total of 41 distinct river stages. Methodologies for this classification are available in Allen et al. (2008) and Allen (2016).The dataset presented here is a multidimensional dataset with 41 bands corresponding to 41 river levels based on the Butte La Rose gaging station. Currently the bands reflect increasing river stages in 1/2 foot increments from 1.6 to 21.6 feet. Enable the multidimensional tool on the right side of the webmap to interact with the data. The Atchafalaya Basin is a resource that must be managed on a system-wide basis to ensure this invaluable national resource is protected and restored. It is recognized that better tools must be developed for managing the Basin and that data evaluation is necessary to ensure sound decision-making. The natural resource inventory and assessment system (NRIAS) that was approved and funded in the FY 2010 Louisiana Department of Natural Resources Atchafalaya Basin Program Annual Plan and served as the primary tool for decision making in the Basin. The system provided a means for scientists to access relevant project data for the Basin and to request and fund data acquisition, monitoring, and data analysis to be used in project planning. This will be critical in providing information necessary for the development and approval of specific projects to be included for construction in future Annual Plans, projects that meet the needs of Louisiana citizens and protect our our natural resources. This and related datasets were created to demonstrate the patterns of inundation, turbid water and floating aquatic vegetation in the Atchafalaya Basin Floodway System at various river levels of the Atchafalaya River.Allen, Y.C., Constant, G.C., and Couvillion, B.R., 2008, Preliminary classification of water areas within the Atchafalaya Basin Floodway System by using Landsat imagery: U.S. Geological Survey Open-File Report 2008 1320, 14 p. https://pubs.er.usgs.gov/usgspubs/ofr/ofr20081320Allen, Y.C. (2016). Landscape Scale Assessment of Floodplain Inundation Frequency Using Landsat Imagery. River Research and Applications. 32. 10.1002/rra.2987.

Chronic pain (CP) presents multidimensional components, leading individuals to experience complex biopsychosocial needs. However, efficient tools to assess these needs remain scarce. PORTRAIT-10 is a tool designed to measure the complexity of patients’ needs. The present study was aimed at documenting the psychometric properties of this tool in a sample of individuals with CP who completed the INTERMED-Self Assessment (IMSA), PORTRAIT-10, Pain Catastrophizing Scale (PCS), and Pain Self-Efficacy Questionnaire (PSEQ). PORTRAIT-10 was re-administered 3 weeks later. The sample comprised 295 participants. Mean age of the respondents was 53.3 ± 9.3 years; 88.3% were females. The median pain duration was 15 years. Results of an exploratory factor analysis showed that a 4-factor solution best fit the PORTRAIT-10 data, with at least 2 of these factors (psychological and social) being consistent with the conceptual framework of the tool. PORTRAIT-10 also showed acceptable internal consistency (Cronbach α = 0.67, 0.73) and very good reliability over time (ρ = 0.85). Correlation with IMSA was high (ρ = 0.74) and as expected, was low with PCS (ρ = 0.34) suggesting a very good construct validity. A ROC analysis demonstrated that a PORTRAIT-10 cut-off score of 10 displayed good sensitivity (0.86) and specificity (0.71) in detecting complex care needs in this population. This study provides initial validity and reliability of PORTRAIT-10 and suggests that this tool may be helpful in identifying individuals with CP who have complex needs. Further research is needed to explore the psychometric properties of PORTRAIT-10 in large and more diverse chronic pain populations and to evaluate its impact on clinical outcomes.

The main purpose of this macro-study is to shed light on the broad impact of books. For this purpose, the impact a very large collection of books (more than 200,000) has been analysed by using PlumX, an analytical tool providing a great number of different metrics provided by various tools. Furthermore, the study focuses on the evolution of the most significant measures and indicators over time. The results show usage counts in comparison to the other metrics are quantitatively predominant. Catalogue holdings and reviews represent a book's most characteristic measures deriving from its increased level of impact in relation to prior results. Our results also corroborate the long half-life of books within the scope of all metrics, excluding views and social media. Despite of some disadvantages, PlumX has proved to be a very helpful and promising tool for assessing the broad impact of books, especially because of how easy it is to enter the ISBN directly as well as its algorithm to aggregate all the data generated by the different ISBN variations.

HIV infection provokes a myriad of pathological effects on the immune system where many markers of CD4+ T cell dysfunction have been identified. However, most studies to date have focused on single/double measurements of immune dysfunction, while the identification of pathological CD4+ T cell clusters that is highly associated to a specific biomarker for HIV disease remain less studied. Here, multi-parametric flow cytometry was used to investigate immune activation, exhaustion, and senescence of diverse maturation phenotypes of CD4+ T cells. The traditional method of manual data analysis was compared to a multidimensional clustering tool, FLOw Clustering with K (FLOCK) in two cohorts of 47 untreated HIV-infected individuals and 21 age and sex matched healthy controls. In order to reduce the subjectivity of FLOCK, we developed an “artificial reference”, using 2% of all CD4+ gated T cells from each of the HIV-infected individuals. Principle component analyses demonstrated that using an artificial reference lead to a better separation of the HIV-infected individuals from the healthy controls as compared to using a single HIV-infected subject as a reference or analyzing data manually. Multiple correlation analyses between laboratory parameters and pathological CD4+ clusters revealed that the CD4/CD8 ratio was the preeminent surrogate marker of CD4+ T cells dysfunction using all three methods. Increased frequencies of an early-differentiated CD4+ T cell cluster with high CD38, HLA-DR and PD-1 expression were best correlated (Rho = -0.80, P value = 1.96×10−11) with HIV disease progression as measured by the CD4/CD8 ratio. The novel approach described here can be used to identify cell clusters that distinguish healthy from HIV infected subjects and is biologically relevant for HIV disease progression. These results further emphasize that a simple measurement of the CD4/CD8 ratio is a useful biomarker for assessment of combined CD4+ T cell dysfunction in chronic HIV disease.

This multidimensional dataset depicts the probability of inundation in the Atchafalaya Basin Floodway System (ABFS) at increasing Atchafalaya River stages. The analysis used 28 Landsat images (1984-2008) classified into categories of wet (vs dry). Each pixel value was evaluated using a probit analysis (in R) to determine the probability that each pixel would be classified as wet or dry at a series of 1/2 foot increments from 1.6 to 21.6 feet based on the Butte La Rose gaging station for a total of 41 distinct river stages. Methodologies for this classification are available in Allen et al. (2008) and Allen (2016).The dataset presented here is a multidimensional dataset with 41 bands corresponding to 41 river levels based on the Butte La Rose gaging station. Currently the bands reflect increasing river stages in 1/2 foot increments from 1.6 to 21.6 feet. Enable the multidimensional tool on the right side of the webmap to interact with the data. The Atchafalaya Basin is a resource that must be managed on a system-wide basis to ensure this invaluable national resource is protected and restored. It is recognized that better tools must be developed for managing the Basin and that data evaluation is necessary to ensure sound decision-making. The natural resource inventory and assessment system (NRIAS) that was approved and funded in the FY 2010 Louisiana Department of Natural Resources Atchafalaya Basin Program Annual Plan and served as the primary tool for decision making in the Basin. The system provided a means for scientists to access relevant project data for the Basin and to request and fund data acquisition, monitoring, and data analysis to be used in project planning. This will be critical in providing information necessary for the development and approval of specific projects to be included for construction in future Annual Plans, projects that meet the needs of Louisiana citizens and protect our our natural resources. This and related datasets were created to demonstrate the patterns of inundation, turbid water and floating aquatic vegetation in the Atchafalaya Basin Floodway System at various river levels of the Atchafalaya River.Allen, Y.C., Constant, G.C., and Couvillion, B.R., 2008, Preliminary classification of water areas within the Atchafalaya Basin Floodway System by using Landsat imagery: U.S. Geological Survey Open-File Report 2008 1320, 14 p. https://pubs.er.usgs.gov/usgspubs/ofr/ofr20081320Allen, Y.C. (2016). Landscape Scale Assessment of Floodplain Inundation Frequency Using Landsat Imagery. River Research and Applications. 32. 10.1002/rra.2987.

Syntax for Study 1 and 3 In the present multi-sample mixed method study, we developed a novel assessment tool of youth wellbeing, the Multidimensional Wellbeing in Youth Scale (MWYS). In Study 1, an online survey study among Dutch-speaking adolescents and young adults (N = 339, Mage = 18.44 years, SD = 3.53, 79 % females) was conducted to inspect which initial MWYS-items were viewed as being important for their wellbeing. In Study 2, we co-evaluated the original MWYS-items and co-created new items with adolescents and young adults (N = 25) via focus groups. In Study 3, we examined the validity and reliability of the updated MWYS in a new sample of Dutch-speaking adolescents and young adults (N = 239, Mage = 19.68 years, SD = 4.40, 68 % females). Principal Components Analyses revealed five preliminary components of adolescent wellbeing: 1) having impact, purpose, and meaning; 2) dealing with stress and worry; 3) family relationships; 4) self-confidence; and 5) feeling respected, appreciated, and loved. These MWYS-components were related to other measures of mental health and wellbeing, and the MWYS showed good internal consistency and test-retest reliability. In conclusion, the MWYS was found to be a valid and reliable measure of youth wellbeing.

This dataset consists of three high quality multidimensional and multilingual social opinion datasets in the socio-economic domain, specifically Malta's Annual Government Budget. These contain over 6,000 online posts of user-generated content in Maltese, English, Maltese-English or other languages, gathered from newswires and social networking services, for the 2018, 2019 and 2020 budgets. Each online post has been annotated for multiple opinion dimensions in subjectivity, sentiment polarity, emotion, sarcasm and irony, and in terms of negation, topic and language. These datasets are a valuable resource for developing Opinion Mining tools and Language Technologies, and can be used as a baseline for assessing the state-of-the-art and for developing new advanced analytical methods for Opinion Mining.

We provide four CSV files, with three files containing the annotated dataset of each respective annual Government Budget for 2018 (Malta-Budget-2018-dataset-v1.csv), 2019 (Malta-Budget-2019-dataset-v1.csv) and 2020 (Malta-Budget-2018-dataset-v1.csv), whereas the other file (Malta-Budget-2018-2020-data-sources-v1.csv) contains information about each data source referenced within each annual budget dataset file.

Each online post is annotated with the following metadata and information (annotation types):

Online Post Identifier (Online Post ID): unique numerical identifier for the online post;

Twitter Identifier (Twitter ID): unique numerical identifier provided by Twitter for the online post (relevant for tweets only);

Related Online Post Identifier (Related Online Post ID): numerical identifier for the parent online post (if any);

Source Identifier (Source ID): numerical identifier referring to the actual data source (e.g., website) of the online post;

Online Post Text: textual string of the online post (relevant only for newswires' comments);

Subjectivity: binary value, with 1 referring to subjective posts and 0 referring to objective posts;

Sentiment Polarity: categorical value (3-levels) for the sentiment polarity of the online post (negative, neutral, positive);

Emotion: categorical value (8-levels) for the emotion of the online post based on Plutchik's eight primary emotions (joy, sadness, fear, anger, anticipation, surprise, disgust, trust);

Sarcasm: binary value, with 1 referring to sarcasm in online posts;

Irony: binary value, with 1 referring to irony in online posts;

Negation: binary value, with 1 referring to negated online posts;

Off-topic: binary value, with 1 referring to off-topic online posts that are political but not related to the budget;

Language: numerical value, with 0 referring to online posts in English, 1 referring to posts in Maltese, 2 referring to Maltese-English (Maltenglish) code-switched posts, and 3 referring to posts in other languages.

The dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license for non-commercial use.

Chronic pain (CP) presents multidimensional components, leading individuals to experience complex biopsychosocial needs. However, efficient tools to assess these needs remain scarce. PORTRAIT-10 is a tool designed to measure the complexity of patients’ needs. The present study was aimed at documenting the psychometric properties of this tool in a sample of individuals with CP who completed the INTERMED-Self Assessment (IMSA), PORTRAIT-10, Pain Catastrophizing Scale (PCS), and Pain Self-Efficacy Questionnaire (PSEQ). PORTRAIT-10 was re-administered 3 weeks later. The sample comprised 295 participants. Mean age of the respondents was 53.3 ± 9.3 years; 88.3% were females. The median pain duration was 15 years. Results of an exploratory factor analysis showed that a 4-factor solution best fit the PORTRAIT-10 data, with at least 2 of these factors (psychological and social) being consistent with the conceptual framework of the tool. PORTRAIT-10 also showed acceptable internal consistency (Cronbach α = 0.67, 0.73) and very good reliability over time (ρ = 0.85). Correlation with IMSA was high (ρ = 0.74) and as expected, was low with PCS (ρ = 0.34) suggesting a very good construct validity. A ROC analysis demonstrated that a PORTRAIT-10 cut-off score of 10 displayed good sensitivity (0.86) and specificity (0.71) in detecting complex care needs in this population. This study provides initial validity and reliability of PORTRAIT-10 and suggests that this tool may be helpful in identifying individuals with CP who have complex needs. Further research is needed to explore the psychometric properties of PORTRAIT-10 in large and more diverse chronic pain populations and to evaluate its impact on clinical outcomes.

This file provides the primary data (database file, in *csv) and achieved results (results file, in *csv) published in the paper "Monitoring and Targeting the Sanitation Poor: A Multidimensional Approach", Natural Resources Forum, 2018 (under review).

The paper discusses the adequacy and applicability of one approach that is increasingly adopted for multidimensional poverty measurement at the household level, the Alkire-Foster methodology. Drawing on this method, we identify and combine a set of direct household-related water and sanitation deprivations that a person experiences at the same time. This new multidimensional measure is useful for gaining a better understanding of the context in which WaSH services are delivered. It captures both the incidence and intensity of WaSH poverty, and provides a new tool to support monitoring and reporting, as well as targeting and planning. For illustrative purposes, one small town in Mozambique is selected as the initial case study.

This thesis aims to enhance the in-silico identification and experimental characterization of rare PTMs by employing innovative bioinformatics methodologies and robust multidimensional liquid chromatography separation techniques. The first section presents an adaptive and fully automated bioinformatics tool for the validation and localization of rarely occurring PTMs. Utilizing a semi-supervised approach with a linear discriminant analysis (LDA) algorithm, it enhances verification through similarity scoring of tandem mass spectrometry (MS/MS) comparisons between modified peptides and their unmodified analogs. This tool addresses the limitations of traditional false discovery rate (FDR) control methods by incorporating orthogonal criteria, thereby improving the accuracy of PTM identifications. The methodology is validated through its application to a Macaca fascicularis model of stroke, resulting in the identification and confident validation of the largest number of endogenously nitrated peptides reported to date (Chapter 3). Furthermore, i its extensibility is demonstrated by its successful application to retrieve unidentified spectra, particularly for non-tryptic peptides commonly overlooked in traditional protein database searches, which are reported to account for 75% of yet-to-be-identified spectra (Chapter 4). The second section focuses on the development and evaluation of a streamlined multidimensional liquid chromatography (MDLC) system. The existing four-dimensional RP-SA(C)X-RP system, originally requiring four switching valves, has been reengineered. The new four-dimensional platform uses just two ten-port switching valves (2V); it maintains the original chromatographic resolving power and performance while reducing operational complexity and preserving solvent compatibility across separation dimensions [Anal. Chem. 2015, 87, 10015], leading to efficient peptide separation and increased detection of acidic, hydrophilic, hydrophobic and ion-suppressed peptides (Chapter 5). Key improvements include streamlined operational steps, minimized idle time, and simplified synchronization among four individual columns with complementary separation chemistries without the need to collect large numbers of fractions offline. The efficacy of the 2V-4D system was benchmarked through the analysis of the total lysate of the S. cerevisiae model, demonstrating its analytical performance in proteomic applications.

Background: Guidelines recommend including the patient’s values and preferences when choosing treatment for severe aortic stenosis (sAS). However, little is known about what matters most to patients as they develop treatment preferences. Our objective was to identify, prioritize, and organize patient-reported goals and features of treatment for sAS. Results: 51 adults with sAS and 3 caregivers with experience choosing treatment (age 36-92 years) were included. Participants were referred from multiple health centers across the U.S. and online. Eight nominal group meetings generated 32 unique treatment goals and 46 treatment features, which were grouped into 10 clusters of goals and 11 clusters of features. The most important clusters were: 1) trust in the healthcare team, 2) having good information about options, and 3) long-term outlook. Other clusters addressed the need for and urgency of treatment, being independent and active, overall health, quality of life, family and friends, recovery, homecare, and the process of decision-making. Conclusions: These patient-reported items addressed the impact of the treatment decision on the lives of patients and their families from the time of decision-making through recovery, homecare, and beyond. Many attributes had not been previously reported for sAS. The goals and features that patients’ value, and the relative importance that they attach to them, differ from those reported in clinical trials and vary substantially from one individual to another. These findings are being used to design a shared decision-making tool to help patients and their clinicians choose a treatment that aligns with the patients’ priorities. Methods This multi-center mixed-methods study conducted structured focus groups using the nominal group technique to identify patients’ most important treatment goals and features. Patients separately rated and grouped those items using card sorting techniques. Multidimensional scaling and hierarchical cluster analyses generated a cognitive map and clusters. Data were collected via customized Qualtrics survey. Participants were presented with a list of either treatment features or treatment goals and ask to sort them into groups.

The Climate Resilience Information System (CRIS) provides data and tools for developers of climate services. This image service provides access to gridded historical observations for 27 threshold values of temperature for the contiguous United States for 1950-2013. These services are intended to support analysis of climate exposure for custom geographies and time horizons. More details on the how the data were processed can be found in Understanding CRIS Data.Time RangesPixel values for each variable were calculated for each year from 1950 to 2013. Variable DefinitionsSee the variable list and definitions here. Additional ServicesTwo versions of the gridded hisorical observations are available from CRIS:nClimGrid: a 4-km resolution dataset generated by NOAA. This data was used to downscale the STAR-ESDM climate projections in CRIS.Livneh: a 6-km resolution dataset generated by Livneh et al. This data was used to downscale the LOCA2 climate projections in CRIS.Using the Imagery LayerThe ArcGIS Tiled Imagery Service has a multidimensional structure -- a data cube with variable and time dimensions. Methods for accessing the different dimensions will depend on the software/client being used. For more details, please see the CRIS Developer’s Hub along with this instructional StoryMap. To run analysis, first use the multidimensional tools Aggregate or Subset in ArcGIS Pro to copy the necessary data locally.Data ExportData export is enabled on the services if using an ArcGIS client. NetCDF or Zarr files are also available from the NOAA Open Data Distribution system on Amazon Web Services.

The Climate Resilience Information System (CRIS) provides data and tools for developers of climate services. This image service provides access to downscaled climate projections for 27 threshold values of temperature for the contiguous United States for 2 SSP climate scenarios from 1950-2100. These services are intended to support analysis of climate exposure for custom geographies and time horizons. Sixteen downscaled global circulation models (GCMs) were chosen to be included in a weighted ensemble, optimized for the contiguous United States. More details on the models included in the ensemble and the weighting methodologies can be found in Understanding CRIS Data.Time RangesPixel values for each variable were calculated for each year from 2005 to 2100. Additionally, a modeled history runs from 1950 - 2005. The modeled history and future projections have been merged into a single time series. These annual increments support deriving a temporal average, such as a decadal or thirty-year period centered on a specific year. These time steps should not be used to make predictions about conditions for a specific year, especially at a pixel-level. Climate ScenariosClimate models use estimates of future greenhouse gas concentrations and human activities to predict overall change. These different scenarios are called the Shared Socioeconomic Pathways (SSPs). Two different SSPs are presented here: 2-4.5 and 5-8.5. The 2- or 5- represents the socioeconomic growth model. The 4.5 or 8.5 number indicates the amount of radiative forcing (watts per meter square) associated with the greenhouse gas concentration scenario in the year 2100 (higher forcing = greater warming). It is unclear which scenario will be the most likely, but SSP2-4.5 aligns closest with the international targets of the COP-26 agreement for no greater than 2oC average global warming. SSP3-7.0 may be the most likely scenario based on current emission trends. SSP5-8.5 acts as a cautionary tale, depicting a worst-case scenario if reductions in greenhouse gasses are not undertaken. Variable DefinitionsSee the variable list and definitions here. Additional ServicesThree versions of the gridded climate projections are available from CRIS:LOCA2 Ensemble: a statistically downscaled 6-km resolution model. LOCA2 has SSP2-4.5, 3-7.0 and 5-8.5STAR-ESDM Ensemble: a statistically downscaled 4-km resolution model. STAR-ESDM has SSP2-4.5 and 5-8.5NCA5 Blended Ensemble: a merging of LOCA2 and STAR-ESDM ensembles at a 6-km resolution, as was done for the 5th National Climate Assessment (2023). NCA Blended Ensemble has SSP2-4.5 and 5-8.5Using the Imagery LayerThe ArcGIS Tiled Imagery Service has a multidimensional structure -- a data cube with variable, SSP, and time dimensions. Methods for accessing the different dimensions will depend on the software/client being used. For more details, please see the CRIS Developer’s Hub along with this instructional StoryMap. To run analysis, first use the multidimensional tools Aggregate or Subset in ArcGIS Pro to copy the necessary data locally.Data ExportData export is enabled on the services if using an ArcGIS client. NetCDF or Zarr files are also available from the NOAA Open Data Distribution system on Amazon Web Services.

The Climate Resilience Information System (CRIS) provides data and tools for developers of climate services. This image service provides access to downscaled climate projections for 16 threshold values of precipitation for the contiguous United States for 2 SSP climate scenarios from 1950-2100. These services are intended to support analysis of climate exposure for custom geographies and time horizons. Sixteen downscaled global circulation models (GCMs) were chosen to be included in a weighted ensemble, optimized for the contiguous United States. More details on the models included in the ensemble and the weighting methodologies can be found in Understanding CRIS Data.Time RangesPixel values for each variable were calculated for each year from 2005 to 2100. Additionally, a modeled history runs from 1950 - 2005. The modeled history and future projections have been merged into a single time series. These annual increments support deriving a temporal average, such as a decadal or thirty-year period centered on a specific year. These time steps should not be used to make predictions about conditions for a specific year, especially at a pixel-level. Climate ScenariosClimate models use estimates of future greenhouse gas concentrations and human activities to predict overall change. These different scenarios are called the Shared Socioeconomic Pathways (SSPs). Two different SSPs are presented here: 2-4.5 and 5-8.5. The 2- or 5- represents the socioeconomic growth model. The 4.5 or 8.5 number indicates the amount of radiative forcing (watts per meter square) associated with the greenhouse gas concentration scenario in the year 2100 (higher forcing = greater warming). It is unclear which scenario will be the most likely, but SSP2-4.5 aligns closest with the international targets of the COP-26 agreement for no greater than 2oC average global warming. SSP3-7.0 may be the most likely scenario based on current emission trends. SSP5-8.5 acts as a cautionary tale, depicting a worst-case scenario if reductions in greenhouse gasses are not undertaken. Variable DefinitionsSee the variable list and definitions here. Additional ServicesThree versions of the gridded climate projections are available from CRIS:LOCA2 Ensemble: a statistically downscaled 6-km resolution model. LOCA2 has SSP2-4.5, 3-7.0 and 5-8.5STAR-ESDM Ensemble: a statistically downscaled 4-km resolution model. STAR-ESDM has SSP2-4.5 and 5-8.5NCA5 Blended Ensemble: a merging of LOCA2 and STAR-ESDM ensembles at a 6-km resolution, as was done for the 5th National Climate Assessment (2023). NCA Blended Ensemble has SSP2-4.5 and 5-8.5Using the Imagery LayerThe ArcGIS Tiled Imagery Service has a multidimensional structure -- a data cube with variable, SSP, and time dimensions. Methods for accessing the different dimensions will depend on the software/client being used. For more details, please see the CRIS Developer’s Hub along with this instructional StoryMap. To run analysis, first use the multidimensional tools Aggregate or Subset in ArcGIS Pro to copy the necessary data locally.Data ExportData export is enabled on the services if using an ArcGIS client. NetCDF or Zarr files are also available from the NOAA Open Data Distribution system on Amazon Web Services.

The university group at RWTH Aachen specializes in neutron TOF powder diffraction method development, primarily driven by the new concepts of the neutron time-of-flight diffractometer POWTEX, developed in collaboration with Forschungszentrum Jülich at FRM-II/MLZ in Garching. Unfortunately, no free neutrons were available in 2024. Within the DAPHNE project, we are therefore spreading our methods to other neutron TOF diffractometers while generally aiming to allow a broader, more sustainable applicability of the new developments. This overall goal splits into the following tasks, addressing the different steps in the workflow from raw data to scientific result: 1. multidimensional data reduction using Mantid, 2. derive and include fundamental instrument description in NeXuS data files, 3. multidimensional Rietveld test-cases, 4. AIXtal, a web and cross-platform Rietveld platform for (not only) first-time users, 5. AI tools for structure solution and profile refinement of powder diffraction data. Utilizing our Mantid routine PowderReduceP2D for high-pressure data collected from SNAP@SNS, ORNL, USA combined with the derived and iterated fundamental instrumental parameters of SNAP allowed us to test-case the multidimensional Rietveld refinement on high-pressure neutron TOF powder diffraction data of a PbNCN sample. While the details were recently published in [1], it is important to note three things: At first, it is very tedious to collect instrumental parameters from various sources while they should actually be part of the data file. At second, a detector coverage of only 1.3 sr (SNAP) vs. ≈9 sr (POWTEX) already allows to do multidimensional Rietveld, which is remarkable and underlines the general applicability. While the data reduction steps, with the exception of the one- or multi-dimensional binning, were as similar as possible, the scientific result of the multi-dimensional refinement does differ significantly from the conventional, one-dimensional Rietveld refinement. Based on these results, we created a sample nexus file containing data fields for the fundamental instrumental parameters. This was presented and discussed at a workshop with the SNS diffraction department which also allowed us to collect the view from the facility perspective. Shaping the future of the Rietveld diffraction software was also recognized as a common interest, especially since the existing software has been mostly around for a long time and the future of the method needs to be clarified. AIXtal v1 was developed as prototypic, modern web-platform, which allows first-time users to utilize the Rietveld method while partly hiding the complexity of the existing tools GSAS-II and FullProf for the case of conventional X-ray diffraction. The web interface now runs on WebAssembly, which promises performance gains, and moreover now runs not only in the WebBrowser but also natively on Windows, Linux to allow a local installation as well. The higher-performance GUI shall allow to process multidimensional neutron data in the future. While multidimensional Rietveld (GSAS-II 2D) is available as worker already, we only recently started working on GUI and plotting features for this case. The integration of AI methods into AIXtal, e.g. to predict the space group symbol or to support the refinement process, shall ease the structure solution and refinement from powder dat, not only for the unexperienced user. [1] Y. Meinerzhagen, K. Eickmeier, P.C. Müller, J. Hempelmann, A. Houben, R. Dronskowski, J. Appl. Crystallogr. 2024, 57, 1436–1445, doi:10.1107/s1600576724007635.

The Climate Resilience Information System (CRIS) provides data and tools for developers of climate services. This image service provides access to gridded historical observations for 16 threshold values of precipitation for the contiguous United States for 1950-2013. These services are intended to support analysis of climate exposure for custom geographies and time horizons. More details on the how the data were processed can be found in Understanding CRIS Data.Time RangesPixel values for each variable were calculated for each year from 1950 to 2013. Variable DefinitionsSee the variable list and definitions here. Additional ServicesTwo versions of the gridded hisorical observations are available from CRIS:nClimGrid: a 4-km resolution dataset generated by NOAA. This data was used to downscale the STAR-ESDM climate projections in CRIS.Livneh: a 6-km resolution dataset generated by Livneh et al. This data was used to downscale the LOCA2 climate projections in CRIS.Using the Imagery LayerThe ArcGIS Tiled Imagery Service has a multidimensional structure -- a data cube with variable and time dimensions. Methods for accessing the different dimensions will depend on the software/client being used. For more details, please see the CRIS Developer’s Hub along with this instructional StoryMap. To run analysis, first use the multidimensional tools Aggregate or Subset in ArcGIS Pro to copy the necessary data locally.Data ExportData export is enabled on the services if using an ArcGIS client. NetCDF or Zarr files are also available from the NOAA Open Data Distribution system on Amazon Web Services.

Scientific Hypothesis Generation AI Market Outlook

According to our latest research, the global scientific hypothesis generation AI market size reached USD 1.42 billion in 2024, with a robust compound annual growth rate (CAGR) of 28.7% projected through the forecast period. By 2033, the market is expected to attain a value of USD 13.35 billion, reflecting surging adoption across research-driven industries. This remarkable growth is propelled by the increasing need for accelerated scientific discovery, the exponential rise in complex datasets, and the integration of artificial intelligence (AI) into advanced research methodologies, as highlighted by our comprehensive analysis.

One of the primary growth drivers for the scientific hypothesis generation AI market is the sheer volume and complexity of data generated across scientific fields. The proliferation of next-generation sequencing, high-throughput screening, and multi-omics technologies has created datasets that are not only vast but also multidimensional. Traditional analytical tools and human expertise alone are no longer sufficient to extract meaningful insights or generate innovative hypotheses from these data troves. AI-powered hypothesis generation tools leverage advanced algorithms, natural language processing, and machine learning to identify hidden patterns, correlations, and emerging trends within these datasets. By automating the process of hypothesis formulation, these AI solutions significantly reduce the time and effort required for scientific discovery, enabling researchers to focus on experimental validation and innovation. This paradigm shift is fueling widespread adoption in life sciences, healthcare, pharmaceuticals, and academia, driving the market’s exponential growth.

Another critical factor contributing to market expansion is the growing emphasis on interdisciplinary and collaborative research. Scientific problems today often span multiple domains, requiring expertise and data integration from diverse fields such as biology, chemistry, computer science, and environmental science. Scientific hypothesis generation AI platforms are designed to bridge these gaps by ingesting heterogeneous data sources and synthesizing information across disciplines. This capability is particularly valuable for complex challenges like drug discovery, disease modeling, and climate science, where traditional siloed approaches fall short. Furthermore, AI-driven platforms foster collaboration by providing a common analytical framework and facilitating transparent, reproducible research workflows. As funding agencies and research organizations prioritize interdisciplinary initiatives, the demand for AI-powered hypothesis generation tools continues to surge.

The rapid advancements in AI technologies, coupled with increasing investments from both public and private sectors, are further accelerating the market’s upward trajectory. Governments, universities, and industry leaders recognize the transformative potential of AI in scientific discovery and are allocating substantial resources to develop and deploy these solutions. The emergence of cloud-based platforms has democratized access to sophisticated AI tools, enabling even small research teams and organizations in emerging economies to leverage state-of-the-art hypothesis generation capabilities. Additionally, the integration of explainable AI (XAI) and ethical AI frameworks is addressing concerns around transparency and bias, further boosting user confidence and adoption rates. As a result, the scientific hypothesis generation AI market is poised for sustained, high-velocity growth over the next decade.

Regionally, North America maintains its dominance in the scientific hypothesis generation AI market, underpinned by robust investments in research and development, a mature digital infrastructure, and a vibrant ecosystem of AI startups and established technology providers. Europe follows closely, benefiting from strong government initiatives and collaborative research networks. The Asia Pacific region is rapidly emerging as a high-growth market, driven by expanding research activities in China, India, and Japan, as well as increasing investments in AI and life sciences. Latin America and the Middle East & Africa are also witnessing steady adoption, albeit at a slower pace, as digital transformation initiatives gain momentum. Collectively, these regional dynamics underscore the global nature of the scientific hypothesis generation AI market and its pivot

The Climate Resilience Information System (CRIS) provides data and tools for developers of climate services. This image service provides access to gridded historical observations for 27 threshold values of temperature for the contiguous United States for 1950-2023. These services are intended to support analysis of climate exposure for custom geographies and time horizons. More details on the how the data were processed can be found in Understanding CRIS Data.Time RangesPixel values for each variable were calculated for each year from 1950 to 2023. Variable DefinitionsSee the variable list and definitions here. Additional ServicesTwo versions of the gridded hisorical observations are available from CRIS:nClimGrid: a 4-km resolution dataset generated by NOAA. This data was used to downscale the STAR-ESDM climate projections in CRIS.Livneh: a 6-km resolution dataset generated by Livneh et al. This data was used to downscale the LOCA2 climate projections in CRIS.Using the Imagery LayerThe ArcGIS Tiled Imagery Service has a multidimensional structure -- a data cube with variable and time dimensions. Methods for accessing the different dimensions will depend on the software/client being used. For more details, please see the CRIS Developer’s Hub along with this instructional StoryMap. To run analysis, first use the multidimensional tools Aggregate or Subset in ArcGIS Pro to copy the necessary data locally.Data ExportData export is enabled on the services if using an ArcGIS client. NetCDF or Zarr files are also available from the NOAA Open Data Distribution system on Amazon Web Services.