cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.23641/asha.12462596.v1
title: Semantic feature effects on SFA treatment outcomes (Evans et al., 2020)

description: <b>Purpose: </b>Semantic feature analysis (SFA) is a naming treatment found to improve naming performance for both treated and semantically related untreated words in aphasia. A crucial treatment component is the requirement that patients generate semantic features of treated items. This article examined the role feature generation plays in treatment response to SFA in several ways: It attempted to replicate preliminary findings from Gravier et al. (2018), which found feature generation predicted treatment-related gains for both trained and untrained words. It examined whether feature diversity or the number of features generated in specific categories differentially affected SFA treatment outcomes.<b>Method: </b>SFA was administered to 44 participants with chronic aphasia daily for 4 weeks. Treatment was administered to multiple lists sequentially in a multiple-baseline design. Participant-generated features were captured during treatment and coded in terms of feature category, total average number of features generated per trial, and total number of unique features generated per item. Item-level naming accuracy was analyzed using logistic mixed-effects regression models.<b>Results: </b>Producing more participant-generated features was found to improve treatment response for trained but not untrained items in SFA, in contrast to Gravier et al. (2018). There was no effect of participant-generated feature diversity or any differential effect of feature category on SFA treatment outcomes.<b>Conclusions: </b>Patient-generated features remain a key predictor of direct training effects and overall treatment response in SFA. Aphasia severity was also a significant predictor of treatment outcomes. Future work should focus on identifying potential nonresponders to therapy and explore treatment modifications to improve treatment outcomes for these individuals.<br><b>Supplemental Material S1. </b>Participant demographics.<br><b>Supplemental Material S2. </b>Language testing results.<br><b>Supplemental Material S3.</b> Performance variables (summarized across all treated items).<br>Evans, W. S., Cavanaugh, R., Gravier, M. L., Autenreith, A. M., Doyle, P. J., Hula, W. D., & Dickey, M. W. (2020). Effects of semantic feature type, diversity, and quantity on semantic feature analysis treatment outcomes in aphasia. <i>American Journal of Speech-Language Pathology.</i> Advance online publication. https://doi.org/10.1044/2020_AJSLP-19-00112<br><b>Publisher Note:</b> This article is part of the Special Issue: Select Papers From the 49th Clinical Aphasiology Conference.
DOI: 10.6084/m9.figshare.21203129.v1
title: Untitled Item

description: data of IFWA
DOI: 10.21233/w9pt-zn55
title: Lake Biwa, North Basin ostracode surface sample dataset

description: Raw data for the Lake Biwa, North Basin ostracode surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.17632/dz343xw8jf
title: PONE-D-24-53651_Supporting files

description: Effect of mineral filler on Low velocity impact behaviour of Bi-directional Glass fibre/epoxy composites Exposed to varied temperature conditions
DOI: 10.17632/v6x3pbppb4
title: A-AP DATA (p-HCRP-CO2)

description: The generated Adapted AP (or Australian Post) dataset with the Vehicle Capacities & CO2 emissions budgets considered to solve the p-Hub Center Routing Problem under CO2 emissions budget (p-HCRP-CO2).
DOI: 10.6084/m9.figshare.25097213.v1
title: Lyssikatos and Wenzel 2024

description: Refer to ReadMe.xlsx file for details on all data and script files.
DOI: 10.17632/2m77rcd8nr
title: Supplementary videos for PhD: 'Developing Imaging and Spectroscopy Capabilities in Liquid-Phase Transmission Electron Microscopy'
DOI: 10.17632/zjv3mbzgxv
title: Growth parameters of crayfish Cambarellus montezumae with the use of salix yeast as additives in the diet

description: The study of C. montezumae is of great significance due to its socio-economic and economic implications. Furthermore, laboratory production of this species is a relatively new issue, and definitive solutions to improve its commercial exploitation have not been established yet. Therefore, the objective of this study was to assess the growth of C. montezumae by incorporating additives in its diet to enhance its laboratory production.

Juvenile crayfish were collected, introduced in a recirculating aquaculture system, and randomly distributed into three treatments: shrimp-balanced food with willow extract, shrimp-balanced food with yeast, and shrimp-balanced food (control). The crayfish were measured and weighed once a week for 77 days to obtain growth parameters. Results revealed that the addition of yeast to the balanced food improved weight, length, biomass, periodic absolute increase for length (PAIL) periodic absolute increase for weight (PAIW), relative growth rate (RGRL) at day 70, and condition factor (K) at day 77. On the other hand, the addition of willow extract to the feed improved K at day 70. Moreover, both additives are suitable for obtaining lower FCF values than conventional foods, which implies the use of low amounts of food and a decrease in costs.

The weight (gr) and the length (mm) of fifteen individuals randomly chosen from each tub were measured weekly to obtain the following growth parameters:

Periodic absolute increase for length (PAIL) and for weight (PAIW):
PAIL= FL-IL and PAIW= FW-IW
where FL= final length, IL= initial length, FW= final weight and IW= initial weight.

Relative growth rate for length (RGRL) and for weight (RGRW):
〖RGR〗_L= (FL-IL)/IL and RGR_W= (FW-IW)/IW

Instantaneous rate of growth (IRG):
IRG= (LnFW-LnIW)/t
Where Ln= natural logarithm and t= time

Specific growth rate (SGR):
SGR=((LnFW-LnIW)/t)*100

Food conversion factor (FCF):
FCF= (food consumed)/((FB-IB) )
where FB= final biomass and IB= initial biomass

Condition factor, (Fulton index):
K=(weight/〖(length)〗^3 )*100
DOI: 10.6084/m9.figshare.17159300.v1
title: Data on indication of social buffering in dairy calves.xlsx

description: We investigated the effect of pair versus individual housing on calves’ behavior and concentrate intake after disbudding.
DOI: 10.17632/cn4pnwmw5n
title: Biogeochemical metal cycles and paleoenvironmental evolution during the Late Quaternary glacial-interglacial cycles in the Western South China Sea off the coast of Vietnam

description: Table 1: redox-sensitive trace elements and enrichment factors in carbonate leachates of MD05-2901 sediments
Table 2: Rare Earth elements and PAAS normalization in carbonate elachates of MD05-2901 sediments
Table 3: radiogenic Sr-Nd and stable Cd isotopic compositions in carbonate elachates of MD05-2901 sediments
DOI: 10.25405/data.ncl.26526385.v1
title: XPS data for publication 'Carbon dioxide reduction with post-transition metal-substituted Keggin-type polyoxometalates'

description: Surface analysis of electrodes used for the controlled electrolysis of CO2 to CO by the compounds (TBA)4[BiPW11O39] to identify changes to the electrode surface such as Bi deposition before and after electrolysis.The XPS data was obtained by Dr Elisabetta Arca at Harwell 2023.
DOI: 10.6084/m9.figshare.14305673.v2
title: S1 RNA-seq Raw Counts.xlsx

description: Raw count file showing the transcriptional response of <i>M.bovis</i> BCG to the intracellular environment of a macrophage profiled by RNA-seq during an experiment undertaken by Cantillon, Goff and Waddell in 2019.
DOI: 10.17632/c9r2fwjhr4
title: T cell-derived IFN-γ Suppresses T Follicular Helper Cell Differentiation and Antibody Responses

description: CD4+ T cells play a critical role in antiviral humoral and cellular immune responses. We have previously reported that subcutaneous lymphocytic choriomeningitis virus (s.c. LCMV) infection is characterized by a stark compartmentalization of CD4+ T cells, leading to strong TH1 polarization but virtually absent T follicular helper (TFH) cells, a key driver of humoral immunity. Here, we investigated the mechanisms responsible for this impaired TFH differentiation. We found that T-bet+ cells induced by s.c. LCMV infection encompass a TH1 subset expressing Granzyme-B (GzmB) and a Tcf-1+ subset that retains the potential for TFH differentiation without expressing mature TFH markers. Interestingly, IFN-γ blockade enables full differentiation of Tcf-1+ cells into TFH, formation of germinal centers and increased antibody production. Of note, the suppression of TFH cells by IFN-γ is not directly mediated through CD4+ T cells but rather involves another cell type, likely dendritic cells (DCs). Our study provides novel insights into the mechanisms directing early CD4+ T cell polarization and affecting humoral responses to viruses, laying a foundation for the development of effective vaccine strategies.
DOI: 10.17632/pcds6gksy7
title: Dataset Mode Choice Mode Choice Sheikh Zayed Grand Mosque , Solo, Indonesia

description: The data, collected in 2024, provides a comprehensive snapshot of travel patterns and preferences within the mosque site area in Solo. This data, gathered across five distinct locations within the mosque complex, delves into the motivations and choices of individuals visiting the site.

The dataset encompasses a range of factors influencing travel decisions. It meticulously records travel characteristics, such as the primary purpose of the trip, the distance traveled to reach the mosque, and the duration of the journey. Additionally, it captures the parking fee incurred by visitors, offering insights into the economic considerations associated with travel to the mosque.

Beyond travel details, the dataset also profiles the respondents themselves. It captures demographic information, including gender, age, and occupation, providing a nuanced understanding of the diverse population visiting the mosque. Furthermore, it delves into economic indicators, such as monthly income and vehicle ownership, revealing the socioeconomic factors that influence travel choices.

This rich dataset serves as a valuable resource for understanding travel behavior within the mosque site area. By analyzing the collected data, researchers can gain valuable insights into the factors influencing travel choices, identify potential areas for improvement in accessibility and convenience, and develop strategies to enhance the overall experience for visitors.
DOI: 10.17632/mjvpd3jmt9
title: The factor affecting

description: factors
DOI: 10.17632/zgtkd256bk
title: Pandemic Effects on Rural Bangladeshi Students' Lifestyle and Learning

description: This dataset, consisting of 700 samples, captures the experiences, well-being, and educational challenges faced by students in rural Bangladesh during the COVID-19 pandemic. It surveys various aspects of student life, including personal interests, academic focus, access to learning resources, and emotional responses during the pandemic. This data provides insights into how the pandemic affected students' academic performance, mental health, family dynamics, and lifestyle in rural areas.

Column Descriptions:
Timestamp: Date and time of response; Data Type: Datetime
Name (নাম): Respondent's name; example: Meherajul Islam, Data Type: Categorical
Age (বয়স): Respondent's age in years; example: 16, Data Type: Numeric
Gender (লিঙ্গ): Respondent's gender; example: Male, Data Type: Categorical
Which class are you in? (তুমি কোন শ্রেণীতে পড়?): Current class grade; example: 10, Data Type: Numeric
District (জেলা): Home district of respondent; example: Chandpur, Data Type: Categorical
Personal Interest (তুমি কি করতে পছন্দ করো?): Preferred activity; example: Playing outside, Data Type: Categorical
Can you acquire it now? (তুমি কি এখন করতে পারতেছ?): Whether the interest can currently be pursued; example: No, Data Type: Categorical
Want to go out despite health risk? (করোনা থাকা সত্ত্বেও বাইরে যেতে চাও?): Willingness to go outside despite health risks; example: No, Data Type: Categorical
Pandemic hampers study? (পড়াশোনার ক্ষতি করছে?): If COVID-19 affected studies; example: Yes, Data Type: Categorical
Concentrate like before? (মনোযোগ দিতে পারতেছ?): Ability to focus on studies as before; example: No, Data Type: Categorical
Aware of Sangsad TV classes? (অনলাইন ক্লাসের ব্যাপারে জানো?): Awareness of Sangsad TV online classes; example: Yes, Data Type: Categorical
Participating in Sangsad TV classes? (অনলাইন ক্লাসে অংশগ্রহণ করো?): Participation in Sangsad TV classes; example: No, Data Type: Categorical
Can have favorite foods now? (প্রিয় খাবার পাচ্ছ?): Availability of favorite foods; example: No, Data Type: Categorical
Anger due to restricted activities? (প্রিয় কাজ করতে না দিলে রাগ?): Feeling of frustration due to restrictions; example: Yes, Data Type: Categorical
More scolded by parents? (বাবা-মা বেশি বকাঝকা করে?): If scolded more often by parents; example: Sometimes, Data Type: Categorical
More family time due to pandemic? (পরিবারের সাথে সময় বেশি?): Whether the pandemic increased family time; example: Yes, Data Type: Categorical
Visited relatives' homes (relative_house): Visits to relatives' homes; example: No, Data Type: Categorical
Miss school friend hangouts? (স্কুলের বন্ধুদের মিস করছো?): Missing hangouts with friends; example: Yes, Data Type: Categorical
Target: This column indicates the overall impact of the pandemic on the student’s well-being and activities. Categories include Full, Partial, and None.

*The dataset was utilized in this study: https://ijece.iaescore.com/index.php/IJECE/article/view/27098/15978 (DOI: 10.11591/ijece.v12i5.pp5501-5510)
DOI: 10.17632/47vtp22vs7
title: Data for: CubeSatNet: Ultralight Convolutional Neural Network Designed for On-orbit Binary Image Classification on a 1U CubeSat

description: CubeSat Training and Test Dataset used for the training, validation and testing of ultralight CubeSatNet CNN model designed for 1U CubeSat
DOI: 10.17632/tmxgwfsz4w
title: Road Construction in Poland

description: The dataset comprises the selected information on road construction contracts ordered in Poland in 2014-2017. All of them were executed under public procurement law. However, not all road construction orders from that time are included in the database due to the unavailability of data. There are the following types of data: label of contracts, type of works (construction of a new road or modernization of an existing one), type of road (highway, express road, national road, voivodeship road, county road), location of the road by voivodeship, type of order (order of design, then a construction ordered separately - marked as "design then build"; design and the construction jointly ordered - marked as "design and build"), the price for design (in PLN), price for construction works (in PLN), total price for design and construction works, lengths of the section of the road, total price per 1 km of the road. In the case of "design and build" orders separate prices for the design, and for the construction works are not given ("n/a" are placed there). Data were collected to verify if the unit price for 1 km of road is higher when the order type is "design and build".
DOI: 10.6084/m9.figshare.5128462.v1
title: Supplementary Material for: Electrochemical Skin Conductance in Diabetic Kidney Disease

description: <b><i>Background:</i></b> There is a need to identify patients with diabetic kidney disease (DKD) using noninvasive, cost-effective screening tests. Sudoscan®, a device using electrochemical skin conductance (ESC) to measure sweat gland dysfunction, is valuable for detecting peripheral neuropathy. ESC was tested for association with DKD (estimated glomerular filtration rate (eGFR) &lt;60 ml/min/1.73 m<sup>2</sup>) in 383 type 2 diabetes mellitus (T2D)-affected patients; diagnostic thresholds were determined in 540 patients. <b><i>Methods:</i></b> Relationships between ESC with eGFR and urine albumin:creatinine ratio (UACR) were assessed in 202 European Americans and 181 African Americans with T2D. <b><i>Results:</i></b> In 92 European American DKD cases and 110 T2D non-nephropathy controls, respectively, mean (SD) ages were 69 (9.7) and 61 (10.8) years, hemoglobin A1c (HbA1c) 7.4 (1.2) and 7.4 (1.3)%, eGFR 29.6 (12.2) and 87.8 (14.2) ml/min/1.73 m<sup>2</sup>, and UACR 1,214 (1,705) and 7.5 (5.8) mg/g. In 57 African American cases and 124 controls, respectively, mean (SD) ages were 64.0 (11.9) and 59.5 (9.7) years, HbA1c 7.4 (1.3) and 7.5 (1.7)%, eGFR 29.6 (13.3) and 90.2 (16.2) ml/min/1.73 m<sup>2</sup>, and UACR 1,172 (1,564) and 7.8 (7.1) mg/g. Mean (SD) ESC (μS) was lower in cases than controls (European Americans: case/control hands 49.5 (18.5)/62.3 (16.2); feet 62.1 (17.9)/73.6 (13.8), both p &lt; 1.3 × 10<sup>-6</sup>; African Americans: case/control hands 39.8 (19.0)/48.5 (17.1); feet 53.2 (21.3)/63.5 (19.4), both p ≤ 0.01). Adjusting for age, sex, body mass index and HbA1c, hands and feet ESC associated with eGFR &lt;60 ml/min/1.73 m<sup>2</sup> (p ≤ 7.2 × 10<sup>-3</sup>), UACR &gt;30 mg/g (p ≤ 7.0 × 10<sup>-3</sup>), UACR &gt;300 mg/g (p ≤ 8.1 × 10<sup>-3</sup>), and continuous traits eGFR and UACR (both p ≤ 5.0 × 10<sup>-9</sup>). HbA1c values were not useful for risk stratification. <b><i>Conclusions:</i></b> ESC measured using Sudoscan® is strongly associated with DKD in African Americans and European Americans. ESC is a useful screening test to identify DKD in patients with T2D.
DOI: 10.17632/n78zj449tv
title: Brassica juncea

description: The dataset comprises over 500 images of pigweed leaves (Brassica juncea album), categorized into two classes: "good" and "bad." These images were captured using a Redmi Note 8 Pro mobile camera against a black background under daylight conditions.

**Data Description:**

1. **Classes:**
 - Good: Represents healthy pigweed leaves that exhibit desired characteristics such as uniform color, absence of discoloration or lesions, and overall vitality.
 - Bad: Encompasses pigweed leaves showing signs of disease, damage, or other undesirable traits such as discoloration, spots, wilting, or pest infestation.

2. **Image Collection:**
 - Over 500 images collected in total, with a significant number representing both good and bad instances of pigweed leaves.
 - Images captured under consistent conditions to maintain uniformity and reduce variability.
 - Black background utilized to enhance leaf visibility and isolate the subject.

3. **Data Source:**
 - The images were captured using a Redmi Note 8 Pro mobile camera, ensuring consistent image quality across the dataset.
 - Daylight conditions were chosen to provide natural lighting, minimizing artificial effects on leaf appearance.

4. **Annotation:**
 - Each image is labeled according to its class (good or bad), enabling supervised learning tasks.
 - Annotation may include bounding boxes or masks outlining the leaf area to aid in localization tasks.

5. **Data Preprocessing:**
 - Images may have undergone preprocessing steps such as resizing, normalization, and background removal to enhance model performance and reduce computational complexity.
 - Metadata such as image resolution, format, and capture settings may accompany the dataset for reference.

6. **Data Distribution:**
 - The dataset maintains a balanced distribution between good and bad pigweed leaves, ensuring equal representation of both classes.
 - Randomization techniques may have been employed during data collection and organization to prevent biases in model training.

7. **Potential Applications:**
 - The dataset can be utilized for various machine learning tasks, including classification, object detection, and image segmentation.
 - Applications may include automated agricultural systems for pest detection, disease diagnosis, and crop health monitoring.

8. **Limitations:**
 - While efforts were made to ensure data consistency and quality, variations in lighting conditions, camera angles, and leaf orientation may introduce some degree of variability.
 - The dataset primarily focuses on pigweed leaves of Brassica juncea and may not generalize well to other plant species or environmental conditions.

In summary, the dataset provides a comprehensive collection of annotated pigweed leaf images suitable for training and evaluating machine learning algorithms in agricultural applications, particularly in the context of plant health assessment and crop management.
DOI: 10.17632/jkw85sm4v4
title: Versatile AHU fault detection - design, field validation and practical application

description: This dataset, related to the eponymous paper in journal Energy and Buildings, contains data of 6 air handling units with manually indiced faults. The paper is needed, it containts deiails about the fault induction and more detailed specifications of the AHU. Dataset contains classified "non-fault data" as well as "faulty data", in order to allow validation of fault detection and diagnostic tools.
DOI: 10.17632/yzz73dt884
title: 3D LiDAR and Photogrammetry Scans of a Stilt House in Tai O Village - 2022 - Case 169032

description: This data includes a point cloud file from a combined photogrammetric and LiDAR scan of a stilt house in Tai O Village. The house is designated "Case 169032" in the research project record. Researchers collected the scan using a single, approximately 10 minute walk-through of the house with the Leica-brand BLK2Go model 3D environment scanner. The scan describes the interior and partial exterior of a stilt house in high detail and color. The scan has been decimated to a voxel size of 5 mm to reduce file size. The data provides clear enough rendering of the physical conditions of the house to allow interpretation of the scan into description of the house's construction, contents, and spatial use. The data is available under embargo of one year to protect the personal information of residents of the house, provided for purposes of digital conservation of the building, and for interpretation of the house's physical conditions and interior contents through further research. More detailed information on the dataset, including the date the scan was conducted, the location of the house, and related interview and survey datasets are available upon reviewed request.
DOI: 10.6084/m9.figshare.25447453.v1
title: Polination_dataset_LMSA.xlsx

description: These are the raw data used for the elaboration of the article "Untangling biodiversity interactions: A meta network on pollination in Earth's most diverse tropical savanna", published in Ecology and Evolution.
DOI: 10.17632/r62479vvnp
title: DATASET ON MEASUREMENT OF STARTUPS' SUCCESS

description: The data set was gathered by soliciting expert opinions in order to develop a scale for assessing the success of startups using the Delphi and AHP methodologies. The Delphi approach engaged in anonymous consultation with 16 experts from April to June 2022, whereas the AHP process sought assessment opinions from 23 experts from October to December 2023. This dataset can be utilized to ascertain the structure, composition, and priority of these objects on the scale of success for startups.
DOI: 10.17632/26rhbkv6gr
title: Battery equivalent circuit model error analysis – in comparison with single particle model

description: A comparison between equivalent circuit model and single particle model for lithium in battery modelling
DOI: 10.17632/4tvjdtfyw5
title: Data for: Quantitative Impact of Catastrophe Risk Insurance on Community Resilience

description: The spreadsheets include raw data used in conducting two case studies.