Views provided by UsageCounts
This is the dataset associated with the paper Dui, L. G., Lomurno, E., Lunardini, F., Termine, C., Campi, A., Matteucci, M., & Ferrante, S. (2022). Identification and characterization of learning weakness from drawing analysis at the pre-literacy stage. Scientific Reports, 12(1), 21624. File content: characterizationSciRep22.xlsx: an Excel file rows: subjects columns: year of birth month of birth dominant hand (0=right, 1=left) sex (0=male, 1=female) mother tongue (0=non-Italian, 1=Italian) featureSciRep22: a Matlab file. For each sub-matrix, rows are subjects "dataset" sub-matrix: each column is a feature computed from drawings "names_type" sub-matrix: metadata for the "dataset" matrix column 1: feature name column 2: game the feature refers to column 3: feature type "risk_questions": each column corresponds to the answer to one of the 15 items for assessing graphical abilities. 0=not selected, 1=selected "risk": the risk label computed from "risk_questions", 0=not at risk, 1= at risk
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 3 |

Views provided by UsageCounts