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  • Enriched Tourism Dataset Paris (POIs)
    This dataset contains the Paris subset of the Tourpedia dataset, specifically focusing on points of interest (POIs) categorized as attractions (dataset available at http://tour-pedia.org/download/paris-attraction.csv). The original dataset comprises 4,351 entries that encompass a variety of attractions across Paris, providing details on several attributes for each POI. These attributes include a unique identifier, POI name, category, location information (address), latitude, longitude, specific details, and user-generated reviews. The review fields contain textual feedback from users, aggregated from platforms such as Google Places, Foursquare, and Facebook, offering a qualitative insight into each location. However, due to the initial dataset's high proportion of incomplete or inconsistently structured entries, a rigorous cleaning process was implemented. This process entailed the removal of erroneous and incomplete data points, ultimately refining the dataset to 477 entries that meet criteria for quality and structural coherence. These selected entries were subjected to further validation to ensure data integrity, enabling a more accurate representation of Paris' attractions. - Paris.csv It contains columns including a unique identifier, POI name, category, location information (address), latitude, longitude, specific details, and user-generated reviews. Those reviews have been previously retrieved and pre-processed from Google Places, Foursquare, and Facebook, and have different formats: all words, only nouns, nouns + verbs, noun + adjectives and nouns + verbs + adjectives. - Paris_annotated.csv It contains the ground truth relating to the previous dataset, with manual annotations made by humans on the categorisation of each of the POIs into 12 different pre-defined categories. It has the following columns: * POI name * POI's address * One column for each of the above categories. 1 means that the POI belongs to the category while blank indicates that it does not.
    • Dataset
  • Brand Recognition, Customer Loyalty, Expansion Model
    1. Brand Recognition Model The Brand Recognition Model evaluates the awareness levels of a brand across different geographical scopes and its marketing reach. The data comprises: Awareness Level Local: Simulated using a Beta distribution with parameters (2, 5), this variable represents local brand awareness as a percentage, scaled to a range of 0 to 100. The skewed distribution suggests that while some individuals have high awareness, many have lower levels. Awareness Level Regional: This variable is generated using a Beta distribution (5, 2), reflecting a higher average awareness level compared to local awareness. It indicates that regional marketing efforts may be more effective. Awareness Level National: Similar to the previous variables, this is derived from a Beta distribution (3, 7), indicating a tendency towards lower national awareness levels. Marketing Reach Online: This variable follows a normal distribution with a mean of 5000 and a standard deviation of 1000, representing the online reach of marketing efforts. This reflects typical variations in online audience engagement. Marketing Reach Offline: Generated from a log-normal distribution (mean=8, std=0.5), this variable captures the offline marketing reach, emphasizing that most values will cluster around the mean with a long tail towards higher values. Brand Recall: This percentage is drawn from a uniform distribution between 30 and 80, indicating variability in how well customers remember the brand. The summary statistics for this model provide insights into central tendencies and dispersions for each variable, facilitating an understanding of brand recognition dynamics. 2. Customer Loyalty Model The Customer Loyalty Model focuses on key metrics that indicate customer retention and satisfaction: Customer Retention Rate (CRR): This percentage is generated using a Beta distribution (2, 5), reflecting the likelihood of customers continuing their relationship with the brand. The resulting values suggest that while some customers remain loyal, there are challenges in retaining others. Net Promoter Score (NPS): Simulated using a Poisson distribution with an average score of 7, this variable measures customer willingness to recommend the brand to others. The NPS is crucial for assessing overall customer satisfaction and loyalty. Customer Lifetime Value (CLV): This metric is derived from a log-normal distribution (mean=9, std=0.3), indicating the potential revenue generated from customers over their lifetime. The skew towards higher values reflects the presence of high-value customers. Summary statistics for this model will reveal insights into customer loyalty metrics and highlight areas for improvement in retention strategies. 3. Expansion Potential Model The Expansion Potential Model assesses factors influencing a company's ability to grow and adapt.
    • Dataset
  • Vico etal_2024_musical stimuli
    Musical stimuli used in Exps 1, 2a and 2b
    • Dataset
  • teaLeafBD
    1. Content: Tea leaf image 2. Format of image: JPG 3. Number of Class: 7 4. Number of image: 5278
    • Dataset
  • Data from an experiment investigating size-space associations in both directions with verbal stimuli and vocal responses
    This data set contains the raw data files from one experiment reported in: Richter, M. & Wühr, P. (2024). Verbal stimuli allow for symmetrical S-R priming effects between size and space. Scientific Reports. https://doi.org/10.1038/s41598-024-77806-8. Here is the abstract of the paper: The spatial-size association of response codes (SSARC) effect refers to the observation that left responses are faster and more accurate to small stimuli whereas right responses are faster and more accurate to large stimuli, as compared to the reverse assignment. The underlying spatial-size associations are strongly asymmetrical with physical size/location stimuli and vocal location/size responses and allow for regular but not reciprocal SSARC effects. Recent evidence, however, points towards an important role of stimulus mode in the emergence of reciprocal compatibility effects. We investigated the reciprocity of the SSARC effect with a different stimulus mode, namely with verbal size/location stimuli and vocal responses. In a size-location task, participants vocally responded to the words “small” or “large” by saying “left” or “right” according to a compatible (“small”-“left”/“large”-“right”) or an incompatible mapping (“small”-“right”/“large”-“left”). In a location-size task, participants vocally responded to the words “left” or “right” by saying “small” or “large” according to a compatible (“left”-“small”/“right”-“large”) or an incompatible (“left”-“large”/“right”-“small”) mapping. We observed a regular and a reciprocal SSARC effect of similar size indicating symmetrical spatial-size associations. While regular SSARC effects thus emerge with verbal and physical size stimuli, reciprocal SSARC effects only emerge with verbal but not with physical location stimuli and vocal responses. Theoretical accounts of the SSARC effect differ in whether they predict reciprocal effects and whether they can account for the effect of stimulus mode on the reciprocal SSARC effect.
    • Dataset
  • REVIVA Tool - Linear Block. Energy optimization of retrofit strategies in housing blocks
    -- DESCRIPTION -- The interactive tool REVIVA is the result of the research work developed in the R&D&I project Energy Retrofitting of the Andalusian social housing. Optimization of passive solutions in residential stocks with a high vulnerability index (US.22-06) (https://institucional.us.es/reviva/), funded by the Regional Government of Junta de Andalucía (Consejería de Fomento, Articulación del Territorio y Vivienda). It is an open-access tool, which allows the interactive visualization of the project results related to the energy optimization of the retrofit strategies of the existing public social housing stock in southern Spain. This tool aims to serve as a starting point for decision-making in the renovation process of this building stock, providing decisive information on the thermal performance of different packages of combined retrofit strategies focused on the improvement of thermal comfort, contemplating different levels of intervention and associated economic investment. Specific information on the Linear block buildings typology is included in the tool. The tool is shown as an interactive parallel axis plot, developed with the HiPlot - High Dimensional Interactive Plotting visualization - application (available online for download at https://pypi.org/project/hiplot/ and described in more detail at https://facebookresearch.github.io/hiplot/index.html) and Python v3.6 programming language. -- INSTRUCTIONS -- To open the REVIVA interative tool for the Linear Block typology you must click on the *.html file attached, which may be launched in any internet browser. A more detail description on the operation and management of the interactive tool is provided in the Tool User Manual.pdf document, as well as a video tutorial.
    • Dataset
  • DATABASE MULTICRITERIA AND MULTIMETHOD EVALUATION
    The files that are stored in this database correspond to the set of collected data, the processing and the results that were found in the research carried out.
    • Dataset
  • Data for "Faithful interpretation of protein structures through weighted persistent homology improves evolutionary distance estimation"
    The dataset contains: (1) The 18 Malate dehydrogenase sequences of Methanococcales, (2) The maximum-likelihood tree inferred with the 18 Malate dehydrogenase sequences of Methanococcales, and (3) the inferred ancestral sequences.
    • Dataset
  • Changes in the intestinal microbiota induced by the postnatal environment and their association with hypertension
    Database on Intestinal Microbiota and Hypertension
    • Dataset
  • Awareness Levels and Marketing Reach Correlation Heatmap
    A Pandas DataFrame named data_awareness is created with five variables: awareness_local: Simulated using a normal distribution with a mean of 70 and a standard deviation of 10. This variable represents local brand awareness levels. awareness_regional: Generated from a normal distribution with a mean of 50 and a standard deviation of 12, indicating regional awareness levels. awareness_national: Created with a mean of 30 and a standard deviation of 15, reflecting national brand awareness. marketing_reach_online: Simulated from a normal distribution with a mean of 5000 and a standard deviation of 1000, representing online marketing reach. marketing_reach_offline: Generated with a mean of 3000 and a standard deviation of 800, indicating offline marketing reach.
    • Dataset
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