cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21070054.v1
title: Voter Turnout and Digital Voting Dataset

description: Dataset to discover the relationship between voter turnout and digital voting methods from the past to present
DOI: 10.6084/m9.figshare.21308469
title: ESC_ATAC_seq_rep2.filtered.seq_depth_norm.bw

description: GSE92280 ATAC-seq SRR5099536 ESC_ATAC_seq_rep2 GSM2425099
DOI: 10.6084/m9.figshare.21308469.v1
title: ESC_ATAC_seq_rep2.filtered.seq_depth_norm.bw

description: GSE92280 ATAC-seq SRR5099536 ESC_ATAC_seq_rep2 GSM2425099
DOI: 10.6084/m9.figshare.21070288.v1
title: Datasets of the study "Cyberpolitics of Electoral Processes: A Comparative Study on Digital Voting"

description: Datasets used in the study " <strong>Cyberpolitics of Electoral Processes: A Comparative Study on Digital Voting</strong> "
DOI: 10.5439/1879994
title: 30ripbe1mcfarlane (c1)

description: 30ripbe1mcfarlane
DOI: 10.6084/m9.figshare.21308478
title: Selected Papers Fin.xlsx

description: The dataset contains list and details of papers selected for the analysis
DOI: 10.5439/1891453
title: camsrsuds (a1)

description: camsrsuds
DOI: 10.5439/1891454
title: camsrsudsmovie (a1)

description: camsrsudsmovie
DOI: 10.5439/1891455
title: cammawsground (a1)

description: cammawsground
DOI: 10.6084/m9.figshare.21070039
title: Dataset on Rate of Accession to Internet and Utilization of Digital Voting between 2015-2020

description: Dataset takes the mean value of the rates of individuals using internet (% of population) between 2015-2020 and the availability of digital voting for the countries in question between the same years.
DOI: 10.6084/m9.figshare.21070039.v1
title: Dataset on Rate of Accession to Internet and Digital Voting between 2015-2020

description: Dataset takes the mean value of the rates of individuals using internet (% of population) between 2015-2020 and the availability of digital voting for the countries in question between the same years.
DOI: 10.6084/m9.figshare.21070039.v2
title: Dataset on Rate of Accession to Internet and Utilization of Digital Voting between 2015-2020

description: Dataset takes the mean value of the rates of individuals using internet (% of population) between 2015-2020 and the availability of digital voting for the countries in question between the same years.
DOI: 10.6084/m9.figshare.21070021
title: Dataset on Free and Fair Elections Index and Utilization of Digital Voting between 2015-2021

description: Dataset consisting of Mean of Free and Fair Elections Index of countries between 2015-2021 and the availability of digital voting for the countries between these years.
DOI: 10.6084/m9.figshare.21070021.v1
title: Dataset on Free and Fair Elections Index and Utilization of Digital Voting between 2015-2021

description: Dataset consisting of Mean of Free and Fair Elections Index of countries between 2015-2021 and the availability of digital voting for the countries between these years.
DOI: 10.6084/m9.figshare.21070000
title: World-system Theory and Digital Voting Dataset

description: A dataset to scrutinize the relationship between economic zones and digital voting methods.
DOI: 10.6084/m9.figshare.21070000.v1
title: World-system Theory and Digital Voting Dataset

description: A dataset to scrutinize the relationship between economic zones and digital voting methods.
DOI: 10.6084/m9.figshare.21300249
title: regional embeddings

description: This the dataset of the regional embedding vectors through KR-EAR.
DOI: 10.6084/m9.figshare.21300249.v1
title: regional embeddings

description: This the dataset of the regional embedding vectors through KR-EAR.
DOI: 10.6084/m9.figshare.21300252
title: ground truth

description: this is the dataset of the land use type proportions in each region.
DOI: 10.6084/m9.figshare.21300252.v1
title: ground truth

description: this is the dataset of the land use type proportions in each region.
DOI: 10.6084/m9.figshare.16778287
title: data and codes for contour-based symbolization

description: data and codes for the study on the generation of contour-based symbol representations.
DOI: 10.6084/m9.figshare.16778287.v1
title: data and codes for contour-based symbol

description: data and codes for the study on the generation of contour-based symbol representations
DOI: 10.6084/m9.figshare.21308490
title: ESC_ATAC_seq_rep1.filtered.seq_depth_norm.bw

description: SRR5099535 ESC_ATAC_seq_rep1 GSM2425098
DOI: 10.6084/m9.figshare.21308490.v1
title: ESC_ATAC_seq_rep1.filtered.seq_depth_norm.bw

description: SRR5099535 ESC_ATAC_seq_rep1 GSM2425098
DOI: 10.6084/m9.figshare.21308517.v1
title: Untitled Item

description: SRR5099532 SSEA4+_SSC_ATAC_seq_patient1_rep2 GSM2425095
DOI: 10.6084/m9.figshare.21308517
title: Untitled Item

description: SRR5099532 SSEA4+_SSC_ATAC_seq_patient1_rep2 GSM2425095
DOI: 10.6084/m9.figshare.21308535
title: SSEA4+_SSC_ATAC_seq_patient2_rep2.filtered.seq_depth_norm.bw

description: SRR5099534 SSEA4+_SSC_ATAC_seq_patient2_rep2 GSM2425097
DOI: 10.6084/m9.figshare.21308535.v1
title: SSEA4+_SSC_ATAC_seq_patient2_rep2.filtered.seq_depth_norm.bw

description: SRR5099534 SSEA4+_SSC_ATAC_seq_patient2_rep2 GSM2425097
DOI: 10.6084/m9.figshare.21308565
title: SSEA4+_SSC_ATAC_seq_patient1_rep1.filtered.seq_depth_norm.bw

description: SRR5099531 SSEA4+_SSC_ATAC_seq_patient1_rep1 GSM2425094
DOI: 10.6084/m9.figshare.21308565.v1
title: SSEA4+_SSC_ATAC_seq_patient1_rep1.filtered.seq_depth_norm.bw

description: SRR5099531 SSEA4+_SSC_ATAC_seq_patient1_rep1 GSM2425094
DOI: 10.6084/m9.figshare.21308571
title: SSEA4+_SSC_ATAC_seq_patient2_rep1.filtered.seq_depth_norm.bw

description: SRR5099533 SSEA4+_SSC_ATAC_seq_patient2_rep1 GSM2425096
DOI: 10.6084/m9.figshare.21308571.v1
title: SSEA4+_SSC_ATAC_seq_patient2_rep1.filtered.seq_depth_norm.bw

description: SRR5099533 SSEA4+_SSC_ATAC_seq_patient2_rep1 GSM2425096
DOI: 10.34740/kaggle/dsv/4310101
title: Ukraine Conflict Twitter Dataset (53.02M tweets)
DOI: 10.3886/e181561
title: Resettlement - China Data

description: The data comes from primary survey collection as well as lab-in-the-field experiments in seven villages in Qingdao, China. The article in Contemporary Economic Policy explains the data variables in the summary statistics.
DOI: 10.34740/kaggle/dsv/4310180
title: Aksara Jawa / Hanacaraka
DOI: 10.6084/m9.figshare.21308568
title: Data and code for contour-based symbolization

description: data and codes for the study on the generation of contour-based symbol representations.
DOI: 10.6084/m9.figshare.21308568.v1
title: Data and code for contour-based symbolization

description: data and codes for the study on the generation of contour-based symbol representations.
DOI: 10.6084/m9.figshare.21308637.v1
title: Additional file 1 of Comparative transcriptomics in human COPD reveals dysregulated genes uniquely expressed in ferrets

description: Additional file 1: Table S1. RNA-seq data for 12 samples (six biological replicates each for air and smoke experimental conditions) sequenced using an Illumina NextSeq 500 System. [GEO Accession Number GSE193749].
DOI: 10.6084/m9.figshare.21308637
title: Additional file 1 of Comparative transcriptomics in human COPD reveals dysregulated genes uniquely expressed in ferrets

description: Additional file 1: Table S1. RNA-seq data for 12 samples (six biological replicates each for air and smoke experimental conditions) sequenced using an Illumina NextSeq 500 System. [GEO Accession Number GSE193749].
DOI: 10.6084/m9.figshare.21308646
title: Additional file 4 of Comparative transcriptomics in human COPD reveals dysregulated genes uniquely expressed in ferrets

description: Additional file 4: Table S3. Complete annotation GO TERMS in smoke exposed ferrets compared to controls.
DOI: 10.6084/m9.figshare.21308646.v1
title: Additional file 4 of Comparative transcriptomics in human COPD reveals dysregulated genes uniquely expressed in ferrets

description: Additional file 4: Table S3. Complete annotation GO TERMS in smoke exposed ferrets compared to controls.
DOI: 10.6084/m9.figshare.21308649
title: Additional file 5 of Comparative transcriptomics in human COPD reveals dysregulated genes uniquely expressed in ferrets

description: Additional file 5: Table S4. Complete list (52) of DEGs in common between ferret, mice, and human transcriptome.
DOI: 10.6084/m9.figshare.21308649.v1
title: Additional file 5 of Comparative transcriptomics in human COPD reveals dysregulated genes uniquely expressed in ferrets

description: Additional file 5: Table S4. Complete list (52) of DEGs in common between ferret, mice, and human transcriptome.
DOI: 10.6084/m9.figshare.21308661
title: Additional file 2 of Association between CYP2D6 phenotype and recurrence of Plasmodium vivax infection in south Korean patients

description: Additional file 2: Table S2. The CYP2D6 alleles of the entire samples defined by tagging single nucleotide polymorphisms (tSNPs).
DOI: 10.6084/m9.figshare.21308661.v1
title: Additional file 2 of Association between CYP2D6 phenotype and recurrence of Plasmodium vivax infection in south Korean patients

description: Additional file 2: Table S2. The CYP2D6 alleles of the entire samples defined by tagging single nucleotide polymorphisms (tSNPs).
DOI: 10.6084/m9.figshare.21308680
title: Additional file 2 of Metabolic engineering enables Bacillus licheniformis to grow on the marine polysaccharide ulvan

description: Additional file 2: Table S6. Summary of the proteomic results.
DOI: 10.6084/m9.figshare.21308680.v1
title: Additional file 2 of Metabolic engineering enables Bacillus licheniformis to grow on the marine polysaccharide ulvan

description: Additional file 2: Table S6. Summary of the proteomic results.
DOI: 10.6084/m9.figshare.21308683
title: Additional file 3 of Metabolic engineering enables Bacillus licheniformis to grow on the marine polysaccharide ulvan

description: Additional file 3: Table S7. Results of statistical analyses.
DOI: 10.6084/m9.figshare.21308683.v1
title: Additional file 3 of Metabolic engineering enables Bacillus licheniformis to grow on the marine polysaccharide ulvan

description: Additional file 3: Table S7. Results of statistical analyses.
DOI: 10.5281/zenodo.7084136
title: Supporting data for: Type 1 diabetes risk genes mediate pancreatic beta cell survival in response to proinflammatory cytokines

description: <strong>SUMMARY OF THE STUDY</strong> We combined functional genomics and human genetics to investigate processes that affect type 1 diabetes (T1D) risk by mediating beta-cell survival in response to proinflammatory cytokines. We mapped 38,931 cytokine-responsive candidate <em>cis-</em>regulatory elements (cCREs) in beta-cells using ATAC-seq and snATAC-seq and linked them to target genes using co-accessibility and HiChIP. Using a genome-wide CRISPR screen in EndoC-βH1 cells we identified 867 genes affecting cytokine-induced survival, and genes promoting survival and up-regulated in cytokines were enriched at T1D risk loci. Using SNP-SELEX, we identified 2,229 variants in cytokine-responsive cCREs altering transcription factor (TF) binding, and variants altering binding of TFs regulating stress, inflammation and apoptosis were enriched for T1D risk. At the 16p13 locus, a fine-mapped T1D variant altering TF binding in a cytokine-induced cCRE interacted with <em>SOCS1</em>, which promoted survival in cytokine exposure. Our findings reveal processes and genes acting in beta-cells during inflammation that modulate T1D risk. <strong>DESCRIPTION OF FILES:</strong> Supplementary Data 1. List of islet cCREs annotated with cell type and cytokine response - also in GSE205853 Supplementary Data 2. Coaccessible sites in untreated beta cells and promoter annotations - also in GSE205853 Supplementary Data 3. Coaccessible sites in cytokine-treated beta cells and promoter annotations - also in GSE205853 Supplementary Data 4. Coaccessible sites in cytokine treated and untreated beta cells and promoter annotations - also in GSE205853 Supplementary Data 5. Chromatin interactions in EndoC-BH1 cells - also in GSE205853 Supplementary Data 6. Variants selected for SNP-SELEX assay Supplementary Data 7. Variants with TF binding and allelic binding results from SNP-SELEX Supplementary Data 8. snATAC-seq barcodes and metadata - also in GSE205853 Supplementary Data 9. CRISPR-KO screen results - also in GSE205853 Supplementary Data 10. Bulk ATAC-seq count matrix - also in GSE205853 Supplementary Data 11. Bulk RNA-seq count matrix - also in GSE205853 Supplementary Data 12. Alpha cells snATAC-seq count matrix - also in GSE205853 Supplementary Data 13. Acinar cells snATAC-seq count matrix - also in GSE205853 Supplementary Data 14. Beta cells snATAC-seq count matrix - also in GSE205853 Supplementary Data 15. Stellate cells snATAC-seq count matrix - also in GSE205853 Supplementary Data 16. Endothelial cells snATAC-seq count matrix - also in GSE205853 Supplementary Data 17. Delta cells snATAC-seq count matrix - also in GSE205853 Supplementary Data 18. Luciferase assay rs10483809 Supplementary Data 19. SOCS1 knockdown qPCR results Supplementary Data 20. SOCS1 knockdown Apotracker (flow-cytometry)results <strong>Raw data deposited at GEO, accessions GSE205853 and GSE118725.</strong> <em>Please refer to publication and GEO for details on methods.</em>
DOI: 10.5281/zenodo.7084137
title: Supporting data for: Type 1 diabetes risk genes mediate pancreatic beta cell survival in response to proinflammatory cytokines

description: <strong>SUMMARY</strong> We combined functional genomics and human genetics to investigate processes that affect type 1 diabetes (T1D) risk by mediating beta-cell survival in response to proinflammatory cytokines. We mapped 38,931 cytokine-responsive candidate <em>cis-</em>regulatory elements (cCREs) in beta-cells using ATAC-seq and snATAC-seq and linked them to target genes using co-accessibility and HiChIP. Using a genome-wide CRISPR screen in EndoC-βH1 cells we identified 867 genes affecting cytokine-induced survival, and genes promoting survival and up-regulated in cytokines were enriched at T1D risk loci. Using SNP-SELEX, we identified 2,229 variants in cytokine-responsive cCREs altering transcription factor (TF) binding, and variants altering binding of TFs regulating stress, inflammation and apoptosis were enriched for T1D risk. At the 16p13 locus, a fine-mapped T1D variant altering TF binding in a cytokine-induced cCRE interacted with <em>SOCS1</em>, which promoted survival in cytokine exposure. Our findings reveal processes and genes acting in beta-cells during inflammation that modulate T1D risk. Raw data deposited at GEO, accessions GSE205853 and GSE118725.
DOI: 10.5281/zenodo.7106728
title: Supporting data for: Type 1 diabetes risk genes mediate pancreatic beta cell survival in response to proinflammatory cytokines

description: <strong>SUMMARY OF THE STUDY</strong> We combined functional genomics and human genetics to investigate processes that affect type 1 diabetes (T1D) risk by mediating beta-cell survival in response to proinflammatory cytokines. We mapped 38,931 cytokine-responsive candidate <em>cis-</em>regulatory elements (cCREs) in beta-cells using ATAC-seq and snATAC-seq and linked them to target genes using co-accessibility and HiChIP. Using a genome-wide CRISPR screen in EndoC-βH1 cells we identified 867 genes affecting cytokine-induced survival, and genes promoting survival and up-regulated in cytokines were enriched at T1D risk loci. Using SNP-SELEX, we identified 2,229 variants in cytokine-responsive cCREs altering transcription factor (TF) binding, and variants altering binding of TFs regulating stress, inflammation and apoptosis were enriched for T1D risk. At the 16p13 locus, a fine-mapped T1D variant altering TF binding in a cytokine-induced cCRE interacted with <em>SOCS1</em>, which promoted survival in cytokine exposure. Our findings reveal processes and genes acting in beta-cells during inflammation that modulate T1D risk. <strong>DESCRIPTION OF FILES:</strong> Supplementary Data 1. List of islet cCREs annotated with cell type and cytokine response - also in GSE205853 Supplementary Data 2. Coaccessible sites in untreated beta cells and promoter annotations - also in GSE205853 Supplementary Data 3. Coaccessible sites in cytokine-treated beta cells and promoter annotations - also in GSE205853 Supplementary Data 4. Coaccessible sites in cytokine treated and untreated beta cells and promoter annotations - also in GSE205853 Supplementary Data 5. Chromatin interactions in EndoC-BH1 cells - also in GSE205853 Supplementary Data 6. Variants selected for SNP-SELEX assay Supplementary Data 7. Variants with TF binding and allelic binding results from SNP-SELEX Supplementary Data 8. snATAC-seq barcodes and metadata - also in GSE205853 Supplementary Data 9. CRISPR-KO screen results - also in GSE205853 Supplementary Data 10. Bulk ATAC-seq count matrix - also in GSE205853 Supplementary Data 11. Bulk RNA-seq count matrix - also in GSE205853 Supplementary Data 12. Alpha cells snATAC-seq count matrix - also in GSE205853 Supplementary Data 13. Acinar cells snATAC-seq count matrix - also in GSE205853 Supplementary Data 14. Beta cells snATAC-seq count matrix - also in GSE205853 Supplementary Data 15. Stellate cells snATAC-seq count matrix - also in GSE205853 Supplementary Data 16. Endothelial cells snATAC-seq count matrix - also in GSE205853 Supplementary Data 17. Delta cells snATAC-seq count matrix - also in GSE205853 Supplementary Data 18. Luciferase assay rs10483809 Supplementary Data 19. SOCS1 knockdown qPCR results Supplementary Data 20. SOCS1 knockdown Apotracker (flow-cytometry)results <strong>Raw data deposited at GEO, accessions GSE205853 and GSE118725.</strong> <em>Please refer to publication and GEO for details on methods.</em>
DOI: 10.34740/kaggle/dsv/4310106
title: Ukraine Conflict Twitter Dataset (53.18M tweets)
DOI: 10.6084/m9.figshare.21308721
title: Case points

description: Human cases Occurrence points in Pakistan.
DOI: 10.6084/m9.figshare.21308721.v1
title: Case points

description: Human cases Occurrence points in Pakistan.
DOI: 10.5517/ccdc.csd.cc2961f0
title: CCDC 2120971: Experimental Crystal Structure Determination

description: Related Article: Claudia M. Ramírez-Lozano, Ma. Eugenia Ochoa, Pablo Labra-Vázquez, Norberto Farfán, Rosa Santillan|2022|J.Mol.Struct.|1272|134191|doi:10.1016/j.molstruc.2022.134191
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d7qzc
title: CCDC 2211973: Experimental Crystal Structure Determination

description: Related Article: bao chao|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d7r0f
title: CCDC 2211974: Experimental Crystal Structure Determination

description: Related Article: bao chao|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.14457/tu.the.2021.714
title: Forecasting SET index returns using ARIMA, SVR, RNN and random forest models

description: Nowadays, quantitative models based on artificial intelligence and machine learning have been widely used. Compared with traditional statistical or econometric models, machine learning can quickly process and analyze massive data, and has better generalization ability. This paper attempts to apply machine learning algorithms such as LSTM, RF, and SVR to the analysis of time series to predict the return of the SET index and verify which model has a better prediction effect.
DOI: 10.14457/tu.the.2020.1377
title: บทบาท อำนาจหน้าที่ของหัวหน้างานบริหารบุคคลและสวัสดิการต่อการชับเคลื่อนยุทธศาสตร์องค์กร ตามศักยภาพลุ่มน้ำ 17 แห่ง กรมชลประทาน

description: การศึกษาครั้งนี้มีวัตถุประสงค์ 1) เพื่อศึกษาบทบาทในการบริหารทรัพยากรมนุษย์ของหัวหน้างานบริหารบุคคลและสวัสดิการที่ส่งผลต่อการขับเคลื่อนยุทธศาสตร์องค์กร 2) เพื่อศึกษาความคาดหวังต่อบทบาทของบุคลากรที่ปฏิบัติงานด้านการบริหารทรัพยากรมนุษย์ของกรมชลประทาน 3) เพื่อศึกษาอายุและประสบการณ์การทำงานด้านการบริหารทรัพยากรมนุษย์ของกรมชลประทาน ส่งผลต่อการขับเคลื่อนยุทธศาสตร์องค์กร 4) เพื่อหาข้อเสนอแนะที่เป็นประโยชน์เกี่ยวกับบทบาทในการบริหารทรัพยากรมนุษย์เพื่อขับเคลื่อนยุทธศาสตร์ของกรมชลประทานการศึกษาครั้งนี้ใช้วิธีวิจัยเชิงคุณภาพ (Quality Research Methodology) และเชิงปริมาณ (Quantitative Research Methodology) มีกลุ่มประชากรเป็นบุคลากรที่ดำรงตำแหน่งหัวหน้างานบริหารบุคคลและสวัสดิการ จากสำนักงานชลประทาน 1-17 จำนวน 17 คน สำหรับการสัมภาษณ์ และกลุ่มตัวอย่างที่ปฏิบัติงานร่วมกับหัวหน้างานบริหารบุคคลและสวัสดิการสำหรับแบบสอบถาม จำนวน 297 คน โดยใช้เทคนิคของทาโร ยามาเน ใช้เครื่องมือวิจัย 2 ประเภท คือ 1) แบบสัมภาษณ์ เป็นคำถามเกี่ยวกับวัตถุประสงค์ในการศึกษา โดยเป็นแบบสัมภาษณ์รูปแบบกึ่งมีโครงสร้าง (Semi-Structure Interview) มี คำถาม 3 ประเด็น คือ 1. ข้อมูลที่จะส่งผลต่อการขับเคลื่อนยุทธศาสตร์องค์กร 2. ความคิดเห็นที่มีต่อสมรรถนะเฉพาะตามลักษณะงานที่ปฏิบัติของหัวหน้างานบริหารบุคคลและสวัสดิการ 3. ความคิดเห็นที่มีต่อบทบาทด้านการบริหารทรัพยากรมนุษย์ในการขับเคลื่อนยุทธศาสตร์องค์กร 2) แบบสอบถาม ประยุกต์จากแนวคิดบทบาทในการบริหารทรัพยากรมนุษย์ของ Dave Ulrich ประด้วยคำถาม 3 ตอน คือ ตอนที่ 1 ข้อมูลทั่วไปของผู้ตอบแบบสอบถาม ตอนที่ 2 ความคิดเห็นที่มีบทบาทในปัจจุบันและความคาดหวังในการบริหารทรัพยากรมนุษย์ของหัวหน้างานบริหารบุคคลและสวัสดิการ ใช้คำตอบมาตรการประเมินค่าแบบลิเคิร์ท (Likert Scale) 5 ระดับ จาก 1 ถึง 5 มีเกณฑ์แปลผล 5 ระดับ คือ น้อยที่สุดถึงมากที่สุดตามลำดับ วิเคราะห์ข้อมูลโดยใช้สถิติเชิงพรรณนาได้แก่ ค่าเฉลี่ย (Mean) ร้อยละ (Percentage) และส่วนเบี่ยงเบนมาตรฐาน (Standard Deviation) การวิเคราะห์สถิติสหสัมพันธ์ (Correlation Analysis) ซึ่งดูทิศทางความสัมพันธ์ระหว่างตัวแปร 2 ตัวขึ้นไป การวิเคราะห์ดัชนีจัดเรียงลำดับความสำคัญของความต้องการจำเป็น (Priority Needs Index) ผลของค่าเฉลี่ยบทบาทในปัจจุบันและความคาดหวังที่ควรจะเป็น ตอนที่ 3 ข้อเสนอแนะในการบริหารทรัพยากรมนุษย์ต่อหัวหน้างานบริหารบุคคลและสวัสดิการผลการศึกษาผลว่า 1. หัวหน้างานบริหารบุคคลและสวัสดิการแสดงบทบาทในด้านผู้เชี่ยวชาญในการบริหาร (Administrative Expert) ได้อย่างชัดเจนที่สุด เรื่องจากเป็นเนื้องานที่ปฏิบัติอยู่เป็นประจำทำให้แสดงออกมาได้อย่างชัดเจนที่สุด เช่น ในเรื่องของการวิเคราะห์อัตรากำลังในแต่ละพื้นที่ 2. ความคาดหวังต่อการบริหารทรัพยากรมนุษย์ที่ผู้ปฏิบัติงานร่วมกับของหัวหน้างานบริหารบุคคลและสวัสดิการ พบว่า ความคาดหวังต่อการบริหารทรัพยากรมนุษย์ของผู้ที่ปฏิบัติงานร่วมกับหัวหน้างานบริหารบุคคลและสวัสดิการในด้านที่พึ่งพิงของพนักงาน (Employee Champion) มากที่สุด (อยู่ในลำดับที่ 1) รองลงมาคือ ด้านผู้สนับสนุนการเปลี่ยนแปลง (Change Agent) ด้านหุ้นส่วนเชิงกลยุทธ์ (Strategic Partner) และความคาดหวังต่อบทบาทด้านผู้เชี่ยวชาญ (Administrative Expert) ในด้านการบริหารน้อยที่สุด