cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48366/r753854
title: ORKG Comparison Small Language Models (SLMs)

description: This comparison includes a state-of-the-art (SOTA), analysis and statistics of Small Language Models (SLMs), as of October 2024, based on the survey "SMALL LANGUAGE MODELS:
SURVEY, MEASUREMENTS, AND INSIGHTS" by Zhenyan Lu, Xiang Li, Dongqi Cai, Rongjie Yi, Fangming Liu, Xiwen Zhang, Nicholas D. Lane, and Mengwei Xu, focusing, and including a detailed table, presented in the article table, Table 1. The SLMs considered are transformer-based, decoder-only models with 100M–5B parameters designed for resource-efficient deployment on devices with constrained/low hardware requirements such as commodity laptops, high-end mobile devices such as smartphones and tablets, etc. This comparison analyzes 53 SOTA SLMs, focusing on their architectures, training datasets, and algorithms. The key insights provided in the article include the following:

1) SLM Architecture: Group-query attention, gated FFN with SiLU activation, RMS normalization, and larger vocabulary sizes are becoming standard. Innovations like parameter sharing and layer-wise parameter scaling impact runtime performance significantly. ​
2) Training Datasets: Data quality outweighs quantity; model-based filtering improves dataset quality. Recent SLMs are "over-trained" on large amounts of tokens, exceeding the Chinchilla law's recommendations. ​
3) Training Algorithms: Techniques like Maximal Update Parameterization, Knowledge Distillation, and Two-Stage Pre-training improve model capability. ​
4) SLM Capabilities: SLMs have shown significant performance improvements from 2022 to 2024, closing the gap with LLMs. ​Larger models generally perform better, but smaller models can excel in specific tasks. ​Open-source datasets are closing the gap with closed-source datasets in commonsense tasks. ​
5) In-context Learning: Most SLMs benefit from in-context learning, especially in complex tasks. ​Larger models exhibit stronger in-context learning capabilities. ​
6) Runtime Cost: Model architecture impacts latency, especially during the prefill stage. ​Quantization benefits are more significant during the decode stage. ​GPU outperforms CPU significantly in the prefill phase. ​Context length is crucial for runtime memory usage, with KV cache taking up a large portion of memory. ​
DOI: 10.15468/dl.r32dtf
title: Occurrence Download

description: A dataset containing 816 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Nypa fruticans Wurmb)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Specimen, Material citation, Material sample)"
 ]
 }
 }
 ]
}

 The dataset includes 816 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000373-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ga7mup
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bulbistroma curtotthallum Vologdin, 1962"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000375-241024112534372/datasets/export for details.
DOI: 10.15468/dl.qjuzuj
title: Occurrence Download

description: A dataset containing 717 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Oncoba brachyanthera Oliv., Oncoba kivuensis (Bamps) Hul & Breteler, Oncoba ovalis Oliv., Oncoba routledgei Sprague, Oncoba spinosa Forssk., Oncoba subtomentosa (Gilg) Hul & Breteler)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Specimen, Material citation, Material sample)"
 ]
 }
 }
 ]
}

 The dataset includes 717 records from 59 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000376-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q2fbeb
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bursiphycas bullatus Vologdin, 1962"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000378-241024112534372/datasets/export for details.
DOI: 10.15468/dl.6jd7b5
title: Occurrence Download

description: A dataset containing 3898 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Netherlands",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Mustela erminea Linnaeus, 1758",
 "Year 2020-2024"
 ]
}

 The dataset includes 3898 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000379-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q5ke69
title: Occurrence Download

description: A dataset containing 6006 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Netherlands",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Mustela putorius Linnaeus, 1758",
 "Year 2020-2024"
 ]
}

 The dataset includes 6006 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000382-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5apw9a
title: Occurrence Download

description: A dataset containing 170 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Dipsas elegans (Boulenger, 1896)"
 ]
}

 The dataset includes 170 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000387-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ncrdx4
title: Occurrence Download

description: A dataset containing 37 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Denmark",
 "IucnRedListCategory is one of (EX, EW, CR, EN, VU, NT, LC, NE, DD)",
 "TaxonKey is Silpha carinata Herbst, 1783",
 "Year 2000-2024"
 ]
}

 The dataset includes 37 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000383-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.89ey8n
title: Occurrence Download

description: A dataset containing 816 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Nypa fruticans Wurmb)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Specimen, Material citation, Material sample)"
 ]
 }
 }
 ]
}

 The dataset includes 816 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000384-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13987852
title: Data for: ACADEMIC RESILIENCE OF STUDENTS TO SCHOOL ENGAGEMENT THROUGH PERCEIVED EMOTIONAL SUPPORT FROM THE INSTRUCTOR, AND GENDER VARIATION

description: Students with high academic resilience tend to cope and overcome impediments throughout the educational process. Published scholarly works have accentuated that academically resilient students are more engaged in school than their counterparts. Studies have also stressed the importance of instructor emotional support to students’ engagement in schooling. This current study is engrossed in examining academic resiliency vis-à-vis school engagement via perceived instructor emotional support from 910 samples of undergraduate students (Male: N=536, 58.9%, and Female: N=374, 41.1%) from various institutes at City College of Angeles, Philippines. Additionally, it aimed to assess the significant difference between gender concerning the three variables. Mann-Whitney U and Independent Samples T-Test were used to determine the significant difference concerning gender in academic resilience, perceived instructor emotional support, and school engagement. The findings revealed no significant difference between gender concerning school engagement, and a significant difference was found for academic resilience and perceived instructor emotional support. Partial Least Square-Structural Equation Modelling (PLS-SEM) using SmartPLS4 was performed for the mediation analysis. The findings displayed that (1) ARS is significantly related to and predicts school engagement; (2) academic resilience is positively associated with and leverages instructor emotional support; (3) perceived instructor emotional support is significantly linked to and triggers school engagement; (4) most importantly, the relationship between academic resilience and school engagement is partially mediated by perceived instructor emotional support. The results emphasized the significance of the emotional support provided by instructors in fostering resilience and engagement in undergraduate students. Findings were discoursed by highlighting the importance of promoting personal and contextual resources in the college to promote students’ overall well-being.
DOI: 10.5281/zenodo.13987853
title: Data for: ACADEMIC RESILIENCE OF STUDENTS TO SCHOOL ENGAGEMENT THROUGH PERCEIVED EMOTIONAL SUPPORT FROM THE INSTRUCTOR, AND GENDER VARIATION

description: Students with high academic resilience tend to cope and overcome impediments throughout the educational process. Published scholarly works have accentuated that academically resilient students are more engaged in school than their counterparts. Studies have also stressed the importance of instructor emotional support to students’ engagement in schooling. This current study is engrossed in examining academic resiliency vis-à-vis school engagement via perceived instructor emotional support from 910 samples of undergraduate students (Male: N=536, 58.9%, and Female: N=374, 41.1%) from various institutes at City College of Angeles, Philippines. Additionally, it aimed to assess the significant difference between gender concerning the three variables. Mann-Whitney U and Independent Samples T-Test were used to determine the significant difference concerning gender in academic resilience, perceived instructor emotional support, and school engagement. The findings revealed no significant difference between gender concerning school engagement, and a significant difference was found for academic resilience and perceived instructor emotional support. Partial Least Square-Structural Equation Modelling (PLS-SEM) using SmartPLS4 was performed for the mediation analysis. The findings displayed that (1) ARS is significantly related to and predicts school engagement; (2) academic resilience is positively associated with and leverages instructor emotional support; (3) perceived instructor emotional support is significantly linked to and triggers school engagement; (4) most importantly, the relationship between academic resilience and school engagement is partially mediated by perceived instructor emotional support. The results emphasized the significance of the emotional support provided by instructors in fostering resilience and engagement in undergraduate students. Findings were discoursed by highlighting the importance of promoting personal and contextual resources in the college to promote students’ overall well-being.
DOI: 10.15468/dl.h9evvt
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Caenarcanum bioreactoricola"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000388-241024112534372/datasets/export for details.
DOI: 10.15468/dl.pv8w48
title: Occurrence Download

description: A dataset listing the 5317 species recorded in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "GadmGid is RUS.30_1",
 "OccurrenceStatus is Present",
 "Year 2013-2023"
 ]
}

 The dataset's 5317 records were derived from 70 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000348-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13987856
title: Conversación Autoetnográfica "Quiero ir más lento" - Autoethnographic conversation "I want to slow down"

description: The current dataset in this record refers to the paper titled "Quiero ir más lento”: understanding the GenAI hype as female LatinX academics explores the impact of generative AI (GenAI) on academic life, particularly for female LatinX scholars. It highlights how the rapid rise of AI technologies has created pressure for faster productivity, leading to feelings of disconnection and overwhelm. In response, the paper advocates for embracing "slow scholarship," which resists the fast-paced, neoliberal demands of academia and allows space for reflection and care. Using a collaborative autoethnography approach, the paper captures the personal experiences of three academics who engage in collective reflection on the pressures and ethical dilemmas associated with AI. Through this method, they discuss the importance of slowing down and adopting a critical stance toward the use of AI in academic practices. The results emphasize the need for alternative academic approaches that prioritize human understanding, emotional well-being, and collaboration. The conclusion calls for a shift away from the dominant AI-driven narrative, advocating for an inclusive and context-sensitive approach to academic work. By embracing slowness and care, the paper proposes a more sustainable and thoughtful integration of AI into education and research, resisting the push for speed and efficiency.

The dataset contains a corpus based on the autoethnographic records (messages and fieldnotes), adopting the following classification: 



Date/Time in which the information exchange or fieldnote took place

Person that post the message (audio/text)

Full text and audio transcription of messages

Fieldnotes on the contextual information about the messages exchanged.
DOI: 10.5281/zenodo.13987857
title: Conversación Autoetnográfica "Quiero ir más lento" - Autoethnographic conversation "I want to slow down"

description: The current dataset in this record refers to the paper titled "Quiero ir más lento”: understanding the GenAI hype as female LatinX academics explores the impact of generative AI (GenAI) on academic life, particularly for female LatinX scholars. It highlights how the rapid rise of AI technologies has created pressure for faster productivity, leading to feelings of disconnection and overwhelm. In response, the paper advocates for embracing "slow scholarship," which resists the fast-paced, neoliberal demands of academia and allows space for reflection and care. Using a collaborative autoethnography approach, the paper captures the personal experiences of three academics who engage in collective reflection on the pressures and ethical dilemmas associated with AI. Through this method, they discuss the importance of slowing down and adopting a critical stance toward the use of AI in academic practices. The results emphasize the need for alternative academic approaches that prioritize human understanding, emotional well-being, and collaboration. The conclusion calls for a shift away from the dominant AI-driven narrative, advocating for an inclusive and context-sensitive approach to academic work. By embracing slowness and care, the paper proposes a more sustainable and thoughtful integration of AI into education and research, resisting the push for speed and efficiency.

The dataset contains a corpus based on the autoethnographic records (messages and fieldnotes), adopting the following classification: 



Date/Time in which the information exchange or fieldnote took place

Person that post the message (audio/text)

Full text and audio transcription of messages

Fieldnotes on the contextual information about the messages exchanged.
DOI: 10.15468/dl.dudq2a
title: Occurrence Download

description: A dataset containing 3065432 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Oleaceae)"
 ]
}

 The dataset includes 3065432 records from 5439 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000352-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15154428
title: Table 1 in Molecular and morphological characterization of Tylenchus zeae n. sp. (Nematoda: Tylenchida) from Corn (Zea mays) in South Carolina

description: Table 1. Morphometrics of TylenCHUS Zeae n. sp. All measurements are in μm and in the form: mean ± standard deviation (range).
CharacterHolotypeFemale (n = 9)Male (n = 4)L830830.0 ± 39.3 (765.0–895.0)813.0 ± 45 (775.0–885.0)Stylet2020.0 ± 0.7 (20.0–22.0)20.0 (20.0–20.0)MBW2725.0±2.58 (20.0–30.0)22.0 (20.0–25.0)Ant. end to exc. pore distance102103.0 ± 6.0 (92.0–115.0)—Ant. end to esophago-intestinal valve122123.0 ± 5.5 (115.0–130.0)123.0 ± 2.0 (120.0–125.0)Tail112121.0 ± 5.9 (112.0–127.0)127.0 ± 5.0 (122.0–135.0)a3133.0 ± 3.7 (28.0–42.0)37.0 ± 2.0 (35.0–39.0)b77.0 ± 0.5 (6.0–8.0)7.0 ± 0.4 (6.0–7.0)c77.06.0 ± 0.3 (6.0–7.0)V %6363.0 ± 1.2 (61–65%)—Anal body width1515 ± 1.28 (12.0–17.0)13.0 (12.0–14.0)Spicules——21.0 ± 1.0 (20.0–23.0)Gubernaculum——6.0 ± 0.5 (5.0–6.0)
L, body length; MBW, maximum body width.
description: Published as part of Kantor, Mihail R., Handoo, Zafar A., Subbotin, Sergei A., Mowery, Joseph D., Hult, Maria N., Rogers, Stephen & Skantar, Andrea M., 2023, Molecular and morphological characterization of Tylenchus zeae n. sp. (Nematoda: Tylenchida) from Corn (Zea mays) in South Carolina, pp. 9 in Journal of Nematology 55 (1) on page 9, DOI: 10.2478/jofnem-2023-0003, http://zenodo.org/record/11644693
DOI: 10.5281/zenodo.15154429
title: Table 2. Measurements for TylenCHUS Zeae n in Molecular and morphological characterization of Tylenchus zeae n. sp. (Nematoda: Tylenchida) from Corn (Zea mays) in South Carolina

description: Table 2. Measurements for TylenCHUS Zeae n. sp. comparison with T. reX and T. SHerianUS female populations. Measurements are in μm and in the form: mean ± standard deviation (range).
CharacterT. Zeae n. sp. (n = 9)T. SHerianUS Andrassy (1979)T. reX Brzeski (1996) (n = 12)T. reX Andrassy (1979)T. reX Geraert (2008)L830.0 ± 39.3 (765.0–895.0)750–8401007.0 ± 52.4 (963.0–1087.0)960.0–980.0960–1009Stylet20.3 ± 0.7 (20.0–22.0)19–2020.4 ± 0.4 (19.5–205)20.0–21.019–21MBW26.0 (20.0–30.0)—30.7 (based on author calculations)——Ant. to exc. pore distance103.0 ± 6.0 (92.0–115.0)—130.0 ± 4.9 (121.0–157.0)——Ant. to esophago-intestinal valve123.0 ± 5.5 (115.0–130.0)—151.0 ± 4.4 (142.0–157.0)—133–157Tail120.0 ± 5.9 (112.0–127.0)100–116134.0 ± 4.7 (127.0–141.0)134.0–160.0127–160a33.0 ± 3.7 (28.0–42.0)25–2832.8 ± 1.9 (30.0–36.0)32.0–34.0—b7.0 ± 0.5 (6.0–8.0)5.7–6.06.7 ± 0.3 (6.4–7.3)6.8–7.0—c7.06.8–8.07.5 ± 0.4 (7.0–8.4)6.0–7.16.0–8.4V %63.0 ± 1.2 (61–65%)65–6864.9 ± 0.8 (63.0–66.0)61.0–63.061–66MB%—4344.2 ± 0.8 (43–45)43–4543–45
L, body length; MBW, maximum body width.
description: Published as part of Kantor, Mihail R., Handoo, Zafar A., Subbotin, Sergei A., Mowery, Joseph D., Hult, Maria N., Rogers, Stephen & Skantar, Andrea M., 2023, Molecular and morphological characterization of Tylenchus zeae n. sp. (Nematoda: Tylenchida) from Corn (Zea mays) in South Carolina, pp. 10 in Journal of Nematology 55 (1) on page 10, DOI: 10.2478/jofnem-2023-0003, http://zenodo.org/record/11644693
DOI: 10.5281/zenodo.15154430
title: Table 2. Measurements for TylenCHUS Zeae n in Molecular and morphological characterization of Tylenchus zeae n. sp. (Nematoda: Tylenchida) from Corn (Zea mays) in South Carolina

description: Table 2. Measurements for TylenCHUS Zeae n. sp. comparison with T. reX and T. SHerianUS female populations. Measurements are in μm and in the form: mean ± standard deviation (range).
CharacterT. Zeae n. sp. (n = 9)T. SHerianUS Andrassy (1979)T. reX Brzeski (1996) (n = 12)T. reX Andrassy (1979)T. reX Geraert (2008)L830.0 ± 39.3 (765.0–895.0)750–8401007.0 ± 52.4 (963.0–1087.0)960.0–980.0960–1009Stylet20.3 ± 0.7 (20.0–22.0)19–2020.4 ± 0.4 (19.5–205)20.0–21.019–21MBW26.0 (20.0–30.0)—30.7 (based on author calculations)——Ant. to exc. pore distance103.0 ± 6.0 (92.0–115.0)—130.0 ± 4.9 (121.0–157.0)——Ant. to esophago-intestinal valve123.0 ± 5.5 (115.0–130.0)—151.0 ± 4.4 (142.0–157.0)—133–157Tail120.0 ± 5.9 (112.0–127.0)100–116134.0 ± 4.7 (127.0–141.0)134.0–160.0127–160a33.0 ± 3.7 (28.0–42.0)25–2832.8 ± 1.9 (30.0–36.0)32.0–34.0—b7.0 ± 0.5 (6.0–8.0)5.7–6.06.7 ± 0.3 (6.4–7.3)6.8–7.0—c7.06.8–8.07.5 ± 0.4 (7.0–8.4)6.0–7.16.0–8.4V %63.0 ± 1.2 (61–65%)65–6864.9 ± 0.8 (63.0–66.0)61.0–63.061–66MB%—4344.2 ± 0.8 (43–45)43–4543–45
L, body length; MBW, maximum body width.
description: Published as part of Kantor, Mihail R., Handoo, Zafar A., Subbotin, Sergei A., Mowery, Joseph D., Hult, Maria N., Rogers, Stephen & Skantar, Andrea M., 2023, Molecular and morphological characterization of Tylenchus zeae n. sp. (Nematoda: Tylenchida) from Corn (Zea mays) in South Carolina, pp. 10 in Journal of Nematology 55 (1) on page 10, DOI: 10.2478/jofnem-2023-0003, http://zenodo.org/record/11644693
DOI: 10.15468/dl.adc6we
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cataenococcus olivaceus (Cockerell, 1896)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006124-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p9eq6w
title: Occurrence Download

description: A dataset containing 27 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Catolaccus grandis (Burks, 1954)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 27 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006125-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yfa242
title: Occurrence Download

description: A dataset containing 237 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Cisticola bulliens Lynes, 1930)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 237 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0295831-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.